2018–2026

2026 187

October 2026

1. 30 Oct7 Ways Your AI Agent Is Wasting Tokens (And What to Do Instead)
2. 28 OctFine-Tuning vs. Frozen Embeddings: When to Adapt the LLM vs. the Sklearn Head
3. 27 OctContext Managers Beyond Opening Files in Python
4. 26 OctRobust Serialization with JSON and Custom Types
5. 23 OctSpeculative Decoding Explained: How to Get 3× Faster Inference from Any LLM
6. 21 OctHow to Build a Multi-Modal RAG Pipeline for Images and Documents in Python
7. 20 Octpgvector vs. Dedicated Vector Databases: A Production Benchmark
8. 19 OctDescriptors and Controlled Attributes in Python
9. 16 OctIterators and Generators for Memory-Efficient Processing
10. 15 OctChoosing a Chunking Strategy for Your LLM Apps: A Decision-Tree Approach
11. 14 OctBuilding a Custom LLM Evaluation Harness in Python from Scratch
12. 13 OctBuilding a RAG Pipeline with LLM Embeddings and Scikit-learn
13. 12 OctAI Agent Benchmarks Explained: SWE-Bench, GAIA, WebArena, and More
14. 9 OctHow to Fine-Tune Llama 3 for Custom Tool Calling with Unsloth in Python
15. 8 OctEvaluating Graph-RAG vs. Standard RAG: A Hallucination Benchmark on Fact-Dense Queries
16. 7 OctChoosing the Right Agentic AI Framework for 2026: A Decision-Tree Approach
17. 6 OctSynchronous vs. Asynchronous Agent Execution: Architecture Patterns for Production
18. 5 OctHow (and Why) to Build an AI Agent from Scratch in Python
19. 1 OctAdding Temporal Reasoning to Graph-RAG: Tracking Fact Freshness and Staleness
20. 1 OctAI Agent Observability: Logging, Tracing, and Debugging Explained

September 2026

1. 29 SepAutomating Knowledge Graph Population: Extracting Entities and Triples from Unstructured Text with an LLM
2. 28 SepLocal Agentic AI Workflows with Hermes + Ollama
3. 25 SepTool Calling vs. Code Execution for AI Agents: Choosing the Right Action Primitive
4. 24 SepAgent or Workflow? A Practical Test for Knowing When You Actually Need an AI Agent
5. 23 SepRAG vs. Fine-Tuning for Domain Adaptation: When to Use Which
6. 22 SepMonitoring Embedding Drift in Production Scikit-LLM Pipelines
7. 21 SepThe Roadmap to Mastering LLM Inference Optimization
8. 18 SepBuild And Understand a Vector Database From Scratch in 10 Easy Steps
9. 18 SepWhat’s Actually Inside 24,723 Tokens of a Search Result? We Broke It Down, Field by Field
10. 17 SepMultilingual Text Classification with Scikit-LLM and Multilingual Embeddings
11. 16 SepThe Roadmap to Mastering Voice Agents
12. 15 SepTreating Prompt Templates as Hyperparameters in Scikit-LLM GridSearchCV
13. 14 SepA Gentle Introduction to Model Distillation
14. 11 SepFine-Tuning Agentic AI: A Practical Guide
15. 10 SepHow to Combine Traditional Machine Learning with Agentic Reasoning
16. 9 SepVersioning and Tracking Scikit-LLM Experiments
17. 8 SepChain of Thought vs. Tree of Thoughts: Which is Best for AI Agents?
18. 4 SepDataclasses for Structured Application Data
19. 3 SepSingle-Agent vs. Multi-Agent Systems: When the Complexity Is Worth It
20. 2 SepAI Agent Memory Design: What Works and What Doesn’t
21. 1 Sep3 Ways to Enhance Your AI Model’s Interpretability

August 2026

1. 31 AugCombining LLM Embeddings with Tabular Features in a Unified Scikit-learn Pipeline
2. 28 AugInterpretable Text Classification: Probing Scikit-LLM Embedding Spaces
3. 26 AugLearn Vectorized Thinking in Python Through Examples
4. 25 AugComparing Local Tool Calling: Gemma 4 vs. Llama 3 vs. Mistral
5. 24 AugIntegrating Agentic AI with Existing Machine Learning Pipelines
6. 20 AugHow to Build a Robust RAG System with Minimal Resources
7. 18 AugManaging Small Context Windows in Language Models
8. 17 Aug7 Regression Tests Every AI Agent Should Pass Before Deploy
9. 14 AugUnderstanding the Role of Latent Space in Machine Learning Models
10. 12 AugRetrieval vs. Memory in Agentic AI Systems
11. 11 Aug7 Async Patterns for Running Agents Concurrently in Python
12. 10 AugPrompt Caching vs. Fine-Tuning: A Cost and Latency Decision Framework
13. 7 AugIdentifying Token Costs Hiding in Your Agentic Loop
14. 6 AugDesigning AI Agents That Can Self-Correct
15. 5 Aug7 Chunking Strategies That Decide Whether Your RAG Works
16. 4 AugMeasuring Performance of Transformer Inference
17. 4 AugStatic vs. Dynamic vs. Continuous Batching in LLM Inference
18. 3 AugDecoding Strategies and Output Control

July 2026

1. 31 JulUsing a Transformer Model: From Training to Inference
2. 30 JulThe End-to-End Agentic AI Pipeline
3. 29 JulOllama vs. LM Studio vs. llama.cpp: Which Local AI Runtime Should You Use in 2026?
4. 27 Jul5 Architectural Patterns for Persistent Memory and State in AI Agents
5. 24 JulStateful vs. Stateless Agent Design: Tradeoffs for Scalable Agentic Systems
6. 23 JulAn Introduction to Loop Engineering
7. 21 JulThe Current State of Agentic AI
8. 20 JulBuilding Agentic Workflows in Python with LangGraph
9. 17 JulAgentic AI Security: Defending Against Prompt Injection and Tool Misuse
10. 16 JulRun a Local AI Model with Ollama in 15 Minutes
11. 15 JulScikit-Ollama for Scikit-LLM/Ollama Integration
12. 14 JulLLM Evaluation Frameworks Compared: How to Actually Measure What Your Model Does
13. 13 JulBuilding AI Agents? Here Are Some Anti-Patterns to Avoid.
14. 10 JulChoosing the Right AI Agent Memory Strategy: A Decision-Tree Approach
15. 9 JulLLM Orchestration Frameworks Compared: LangChain vs. LlamaIndex vs. Raw API Calls
16. 7 JulTools vs. Subagents: Building Effective AI Agents Without Over-Engineering
17. 6 JulThe Complete Guide to Tool Selection in AI Agents
18. 2 JulContext vs. Memory Engineering in Agentic AI Systems

June 2026

1. 30 JunContext Window Management for Long-Running Agents: Strategies and Tradeoffs
2. 29 JunModel Context Protocol Explained in 3 Levels of Difficulty
3. 26 JunThe AI Agent Tech Stack Explained
4. 25 JunAgentic Workflow vs. Autonomous Agent: What’s the Difference?
5. 24 JunContext Windows Are Not Memory: What AI Agent Developers Need to Understand
6. 23 JunClustering Unstructured Text with LLM Embeddings and HDBSCAN
7. 22 JunBuilding Browser-Using AI Agents in Python
8. 18 JunThe Roadmap to Mastering AI Agent Evaluation
9. 16 JunBuilding an End-to-End Sentiment Analysis Pipeline with Scikit-LLM
10. 15 JunAI Agent Tool Design: What Works and What Doesn’t
11. 12 JunPython Concepts Every AI Engineer Must Master
12. 11 JunMulti-Label Text Classification with Scikit-LLM
13. 10 JunMultimodal Browser AI with Transformers.js for Images and Speech
14. 8 JunThe Practitioner’s Guide to AgentOps
15. 5 JunBuilding Semantic Search with Transformers.js and Sentence Embeddings
16. 4 JunUsing Scikit-LLM with Open-Source LLMs
17. 2 JunScikit-LLM vs. Traditional Text Classifiers: When Should You Use an LLM?
18. 1 JunThe Roadmap for Mastering LLMOps in 2026

May 2026

1. 30 MayServing Multiple Users at Once: How Continuous Batching Keeps LLM Inference Efficient
2. 28 MayBuilding a Context Pruning Pipeline for Long-Running Agents
3. 27 MayThe Statistics of Token Selection: Logits, Temperature, and Top-P Walkthrough
4. 26 MayBuilding a Multi-Tool Gemma 4 Agent with Error Recovery
5. 25 MayImplementing Hybrid Semantic-Lexical Search in RAG
6. 22 MayBuilding Context-Aware Search in Python with LLM Embeddings + Metadata
7. 21 MayHow to Build a Multi-Agent Research Assistant in Python
8. 20 MayAgentic Programming: A Roadmap
9. 19 MayPrompt Engineering for Agentic AI
10. 18 MayBuilding Vector Similarity Search in PostgreSQL with pgvector
11. 13 MayChoosing the Right Agentic Design Pattern: A Decision-Tree Approach
12. 12 MayLLM Observability Tools for Reliable AI Applications
13. 11 MayImplementing Prompt Compression to Reduce Agentic Loop Costs
14. 8 MayImplementing Permission-Gated Tool Calling in Python Agents
15. 7 MayThe Roadmap to Mastering Tool Calling in AI Agents
16. 5 MayImplementing Statistical Guardrails for Non-Deterministic Agents
17. 4 MayAgentic RAG Explained in 3 Levels of Difficulty

April 2026

1. 30 AprEffective KV Compression with TurboQuant
2. 29 AprBuilding AI Agents in Python with Pydantic AI
3. 28 AprEffective Context Engineering for AI Agents: A Developer’s Guide
4. 27 AprText Summarization with Scikit-LLM
5. 23 AprBuilding AI Agents with Local Small Language Models
6. 22 AprTrain, Serve, and Deploy a Scikit-learn Model with FastAPI
7. 21 AprAI Agent Memory Explained in 3 Levels of Difficulty
8. 20 AprGetting Started with Zero-Shot Text Classification
9. 17 AprThe Complete Guide to Inference Caching in LLMs
10. 16 AprPython Decorators for Production Machine Learning Engineering
11. 15 Apr5 Techniques for Efficient Long-Context RAG
12. 13 AprHow to Implement Tool Calling with Gemma 4 and Python
13. 13 AprStructured Outputs vs. Function Calling: Which Should Your Agent Use?
14. 10 AprBeyond Vector Search: Building a Deterministic 3-Tiered Graph-RAG System
15. 9 AprThe Roadmap to Mastering Agentic AI Design Patterns
16. 8 AprA Hands-On Guide to Testing Agents with RAGAs and G-Eval
17. 7 AprHandling Race Conditions in Multi-Agent Orchestration
18. 6 AprTop 5 Reranking Models to Improve RAG Results
19. 1 Apr7 Machine Learning Trends to Watch in 2026

March 2026

1. 31 MarBuilding a ‘Human-in-the-Loop’ Approval Gate for Autonomous Agents
2. 30 MarFrom Prompt to Prediction: Understanding Prefill, Decode, and the KV Cache in LLMs
3. 30 Mar7 Essential Python Itertools for Feature Engineering
4. 27 MarLlamaAgents Builder: From Prompt to Deployed AI Agent in Minutes
5. 26 MarVector Databases Explained in 3 Levels of Difficulty
6. 25 Mar5 Practical Techniques to Detect and Mitigate LLM Hallucinations Beyond Prompt Engineering
7. 24 MarBeyond the Vector Store: Building the Full Data Layer for AI Applications
8. 23 Mar7 Steps to Mastering Memory in Agentic AI Systems
9. 20 MarWhy Agents Fail: The Role of Seed Values and Temperature in Agentic Loops
10. 19 Mar5 Production Scaling Challenges for Agentic AI in 2026
11. 18 Mar7 Readability Features for Your Next Machine Learning Model
12. 17 MarEverything You Need to Know About Recursive Language Models
13. 12 MarBuilding Smart Machine Learning in Low-Resource Settings
14. 10 MarSetting Up a Google Colab AI-Assisted Coding Environment That Actually Works
15. 10 MarFrom Text to Tables: Feature Engineering with LLMs for Tabular Data
16. 9 MarThe 6 Best AI Agent Memory Frameworks You Should Try in 2026
17. 5 MarVector Databases vs. Graph RAG for Agent Memory: When to Use Which
18. 4 Mar5 Essential Security Patterns for Robust Agentic AI
19. 3 MarDeploying AI Agents to Production: Architecture, Infrastructure, and Implementation Roadmap
20. 2 MarBuild Semantic Search with LLM Embeddings

February 2026

1. 27 FebCan LLM Embeddings Improve Time Series Forecasting? A Practical Feature Engineering Approach
2. 26 FebKV Caching in LLMs: A Guide for Developers
3. 25 FebHow to Combine LLM Embeddings + TF-IDF + Metadata in One Scikit-learn Pipeline
4. 24 FebIntroduction to Small Language Models: The Complete Guide for 2026
5. 23 FebBeyond Accuracy: 5 Metrics That Actually Matter for AI Agents
6. 19 FebBuilding a Simple MCP Server in Python
7. 18 FebAgentify Your App with GitHub Copilot’s Agentic Coding SDK
8. 17 FebLLM Embeddings vs TF-IDF vs Bag-of-Words: Which Works Better in Scikit-learn?
9. 16 FebTop 7 Small Language Models You Can Run on a Laptop
10. 12 FebChoosing Between PCA and t-SNE for Visualization
11. 11 FebThe Machine Learning Practitioner’s Guide to Speculative Decoding
12. 10 FebDocument Clustering with LLM Embeddings in Scikit-learn
13. 9 FebThe 7 Biggest Misconceptions About AI Agents (and Why They Matter)
14. 5 FebAgent Evaluation: How to Test and Measure Agentic AI Performance
15. 4 FebExport Your ML Model in ONNX Format
16. 3 Feb7 Advanced Feature Engineering Tricks Using LLM Embeddings
17. 2 FebA Beginner’s Reading List for Large Language Models for 2026

January 2026

1. 29 Jan7 Important Considerations Before Deploying Agentic AI in Production
2. 28 Jan5 Ways to Use Cross-Validation to Improve Time Series Models
3. 27 JanThe 3 Invisible Risks Every LLM App Faces (And How to Guard Against Them)
4. 26 JanLeveling Up Your Machine Learning: What To Do After Andrew Ng’s Course
5. 22 JanThe 2026 Time Series Toolkit: 5 Foundation Models for Autonomous Forecasting
6. 21 JanEverything You Need to Know About How Python Manages Memory
7. 20 JanThe Machine Learning Practitioner’s Guide to Model Deployment with FastAPI
8. 19 JanTop 5 Agentic AI Website Builders (That Actually Ship)
9. 16 JanThe Complete Guide to Data Augmentation for Machine Learning
10. 15 JanThe Beginner’s Guide to Computer Vision with Python
11. 14 JanUncertainty in Machine Learning: Probability & Noise
12. 13 JanHow to Read a Machine Learning Research Paper in 2026
13. 12 Jan10 Ways to Use Embeddings for Tabular ML Tasks
14. 8 JanQuantizing LLMs Step-by-Step: Converting FP16 Models to GGUF
15. 7 JanSupervised Learning: The Foundation of Predictive Modeling
16. 6 JanA Gentle Introduction to Language Model Fine-tuning
17. 6 JanMastering LLM Tool Calling: The Complete Framework for Connecting Models to the Real World
18. 5 Jan7 Agentic AI Trends to Watch in 2026
19. 2 JanGradient Descent:The Engine of Machine Learning Optimization

2025 280

December 2025

1. 31 DecTrain Your Large Model on Multiple GPUs with Tensor Parallelism
2. 30 DecTrain Your Large Model on Multiple GPUs with Fully Sharded Data Parallelism
3. 30 DecBeyond Short-term Memory: The 3 Types of Long-term Memory AI Agents Need
4. 29 DecTrain Your Large Model on Multiple GPUs with Pipeline Parallelism
5. 29 Dec5 Python Libraries for Advanced Time Series Forecasting
6. 26 DecTraining a Model on Multiple GPUs with Data Parallelism
7. 25 DecTrain a Model Faster with torch.compile and Gradient Accumulation
8. 24 DecTraining a Model with Limited Memory using Mixed Precision and Gradient Checkpointing
9. 24 DecPractical Agentic Coding with Google Jules
10. 23 DecEvaluating Perplexity on Language Models
11. 22 Dec3 Smart Ways to Encode Categorical Features for Machine Learning
12. 22 DecPretraining a Llama Model on Your Local GPU
13. 20 DecRotary Position Embeddings for Long Context Length
14. 19 DecHow to Fine-Tune a Local Mistral or Llama 3 Model on Your Own Dataset
15. 18 Dec5 Agentic Coding Tips & Tricks
16. 17 DecThe Real Cost of Inaction: How Silos Hurt Productivity for Data Scientists (Sponsored)
17. 17 DecTop 5 Vector Databases for High-Performance LLM Applications
18. 16 DecThe Machine Learning Engineer’s Checklist: Best Practices for Reliable Models
19. 15 DecTransformer vs LSTM for Time Series: Which Works Better?
20. 13 DecHow LLMs Choose Their Words: A Practical Walk-Through of Logits, Softmax and Sampling
21. 12 Dec3 Feature Engineering Techniques for Unstructured Text Data
22. 10 Dec3 Subtle Ways Data Leakage Can Ruin Your Models (and How to Prevent It)
23. 9 DecHow AI Cuts Costs and Adds Value for Data Science Workflows (Sponsored)
24. 8 DecCreating a Llama or GPT Model for Next-Token Prediction
25. 8 DecTop 5 Agentic AI LLM Models
26. 5 DecTraining a Tokenizer for the Llama Model
27. 4 DecPrompt Engineering for Time Series Analysis
28. 3 DecThe Complete Guide to Using Pydantic for Validating LLM Outputs
29. 2 DecThe Roadmap for Mastering Agentic AI in 2026
30. 1 DecPrompt Compression for LLM Generation Optimization and Cost Reduction

November 2025

1. 30 NovHow to Speed Up Training Convergence of Language Models
2. 28 NovFine-Tuning a BERT Model
3. 26 NovThe Journey of a Token: What Really Happens Inside a Transformer
4. 26 NovPretrain a BERT Model from Scratch
5. 25 NovK-Means Cluster Evaluation with Silhouette Analysis
6. 24 NovThe Complete Guide to Docker for Machine Learning Engineers
7. 24 NovPreparing Data for BERT Training
8. 22 NovBERT Models and Its Variants
9. 20 NovFrom Shannon to Modern AI: A Complete Information Theory Guide for Machine Learning
10. 19 NovWhy Decision Trees Fail (and How to Fix Them)
11. 18 NovTraining a Tokenizer for BERT Models
12. 18 NovForecasting the Future with Tree-Based Models for Time Series
13. 17 NovThe Complete AI Agent Decision Framework
14. 14 NovMastering JSON Prompting for LLMs
15. 13 Nov5 Essential Python Scripts for Intermediate Machine Learning Practitioners
16. 12 NovDatasets for Training a Language Model
17. 12 NovBuilding ReAct Agents with LangGraph: A Beginner’s Guide
18. 11 NovExpert-Level Feature Engineering: Advanced Techniques for High-Stakes Models
19. 10 NovEverything You Need to Know About LLM Evaluation Metrics
20. 7 NovThe 7 Statistical Concepts You Need to Succeed as a Machine Learning Engineer
21. 6 NovFree AI and Data Courses with 365 Data Science—100% Unlimited Access until Nov 21
22. 6 NovEssential Chunking Techniques for Building Better LLM Applications
23. 5 NovHow to Diagnose Why Your Language Model Fails
24. 4 Nov10 Python One-Liners for Calculating Model Feature Importance
25. 3 Nov7 Prompt Engineering Tricks to Mitigate Hallucinations in LLMs

October 2025

1. 30 Oct7 Machine Learning Projects to Land Your Dream Job in 2026
2. 29 Oct7 Advanced Feature Engineering Tricks for Text Data Using LLM Embeddings
3. 28 OctThe Complete Guide to Model Context Protocol
4. 27 Oct10 Python One-Liners for Generating Time Series Features
5. 24 OctThe Complete Guide to Pydantic for Python Developers
6. 23 OctThe Machine Learning Practitioner’s Guide to Fine-Tuning Language Models
7. 22 Oct5 Advanced Feature Engineering Techniques with LLMs for Tabular Data
8. 21 Oct7 Must-Know Agentic AI Design Patterns
9. 20 OctFuture-Proofing Your AI Engineering Career in 2026
10. 17 OctRevolutionizing MLOps: Enhanced BigQuery ML UI for Seamless Model Creation and Management
11. 17 OctThe Complete Guide to Vector Databases for Machine Learning
12. 16 Oct3 Ways to Speed Up Model Training Without More GPUs
13. 16 Oct7 Feature Engineering Tricks for Text Data
14. 14 Oct10 Python One-Liners for Calling LLMs from Your Code
15. 13 Oct7 Pandas Tricks to Handle Large Datasets
16. 12 OctBuilding Transformer Models from Scratch with PyTorch (10-day Mini-Course)
17. 10 OctThe Machine Learning Practitioner’s Guide to Agentic AI Systems
18. 9 OctBuild an Inference Cache to Save Costs in High-Traffic LLM Apps
19. 8 Oct7 NumPy Tricks to Vectorize Your Code
20. 7 OctIs ChatGPT-5 Able to Provide Proofs for Advanced Mathematics?
21. 6 OctA Decision Matrix for Time Series Forecasting Models
22. 3 OctAlgorithm Showdown: Logistic Regression vs. Random Forest vs. XGBoost on Imbalanced Data
23. 1 OctMinMax vs Standard vs Robust Scaler: Which One Wins for Skewed Data?

September 2025

1. 30 SepThe Model Selection Showdown: 6 Considerations for Choosing the Best Model
2. 29 Sep7 Python Decorator Tricks to Write Cleaner Code
3. 26 SepWhy and When to Use Sentence Embeddings Over Word Embeddings
4. 25 Sep5 AI Agent Projects for Beginners
5. 24 SepBeyond Vector Search: 5 Next-Gen RAG Retrieval Strategies
6. 23 SepBagging vs Boosting vs Stacking: Which Ensemble Method Wins in 2025?
7. 22 Sep10 Machine Learning Newsletters to Stay Informed
8. 18 SepA Hands-On Introduction to cuML for GPU-Accelerated Machine Learning Workflows
9. 18 SepA Hands-On Introduction to cuDF for GPU-Accelerated Data Workflows
10. 17 SepFeature Scaling in Practice: What Works and What Doesn’t
11. 16 Sep7 NumPy Tricks for Faster Numerical Computations
12. 15 Sep5 Lesser-Known Visualization Libraries for Impactful Machine Learning Storytelling
13. 11 SepThe Roadmap for Mastering AI-Assisted Coding in 2025
14. 10 Sep10 Common Misconceptions About Large Language Models
15. 9 SepMulti-Agent Systems: The Next Frontier in AI-Driven Cyber Defense
16. 9 SepROC AUC vs Precision-Recall for Imbalanced Data
17. 8 Sep7 Scikit-learn Tricks for Optimized Cross-Validation
18. 5 SepA Gentle Introduction to Batch Normalization
19. 4 SepSmall Language Models are the Future of Agentic AI
20. 3 Sep10 Python One-Liners Every Machine Learning Practitioner Should Know
21. 2 Sep3 Ways to Speed Up and Improve Your XGBoost Models

August 2025

1. 29 Aug5 Key Ways LLMs Can Supercharge Your Machine Learning Workflow
2. 28 Aug7 Pandas Tricks for Efficient Data Merging
3. 28 AugHow to Decide Between Random Forests and Gradient Boosting
4. 27 AugA Gentle Introduction to Bayesian Regression
5. 26 Aug10 Useful NumPy One-Liners for Time Series Analysis
6. 25 AugLogistic vs SVM vs Random Forest: Which One Wins for Small Datasets?
7. 25 Aug5 Scikit-learn Pipeline Tricks to Supercharge Your Workflow
8. 21 AugSeeing Images Through the Eyes of Decision Trees
9. 21 Aug7 Pandas Tricks to Improve Your Machine Learning Model Development
10. 20 AugA Practical Guide to Handling Out-of-Memory Data in Python
11. 19 AugThe Bias-Variance Trade-Off: A Visual Explainer
12. 18 AugHow to Diagnose Why Your Classification Model Fails
13. 18 Aug7 NumPy Tricks You Didn’t Know You Needed
14. 14 Aug7 Matplotlib Tricks to Better Visualize Your Machine Learning Models
15. 12 AugMaking Sense of Text with Decision Trees
16. 11 AugHow to Interpret Your XGBoost Model: A Practical Guide to Feature Importance
17. 8 AugGrok’s Share and Claude’s Leak: 5 Things We Can Learn From System Prompts
18. 7 Aug7 Pandas Tricks for Time-Series Feature Engineering
19. 6 AugTime-Series Transformation Toolkit: Feature Engineering for Predictive Analytics
20. 5 AugA Gentle Introduction to Q-Learning
21. 4 AugBuilding a Decoder-Only Transformer Model Like Llama-2 and Llama-3
22. 2 AugBuilding a Transformer Model for Language Translation

July 2025

1. 31 JulHow to Diagnose Why Your Regression Model Fails
2. 30 JulImplementing Advanced Feature Scaling Techniques in Python Step-by-Step
3. 29 JulYour First Containerized Machine Learning Deployment with Docker and FastAPI
4. 28 JulBuilding a Seq2Seq Model with Attention for Language Translation
5. 28 JulBeyond Pandas: 7 Advanced Data Manipulation Techniques for Large Datasets
6. 25 JulImage Augmentation Techniques to Boost Your CV Model Performance
7. 23 Jul10 Critical Mistakes that Silently Ruin Machine Learning Projects
8. 22 JulZero-Shot and Few-Shot Classification with Scikit-LLM
9. 22 JulBuilding a Plain Seq2Seq Model for Language Translation
10. 21 JulSynthetic Dataset Generation with Faker
11. 18 JulFrom Linear Regression to XGBoost: A Side-by-Side Performance Comparison
12. 17 JulFeature Engineering with LLM Embeddings: Enhancing Scikit-learn Models
13. 16 JulRevisiting k-Means: 3 Approaches to Make It Work Better
14. 15 JulDiscussing Decision Trees: What Makes a Good Split?
15. 14 Jul7 Pandas Tricks That Cut Your Data Prep Time in Half
16. 11 JulWord Embeddings for Tabular Data Feature Engineering
17. 10 JulDecision Trees Aren’t Just for Tabular Data
18. 8 Jul10 NumPy One-Liners to Simplify Feature Engineering
19. 7 JulYour First OpenAI API Project in Python Step-By-Step
20. 4 JulSecuring FastAPI Endpoints for MLOps: An Authentication Guide
21. 4 JulSkip Connections in Transformer Models
22. 2 Jul5 Advanced RAG Architectures Beyond Traditional Methods
23. 1 JulMixture of Experts Architecture in Transformer Models

June 2025

1. 30 JunYour First Local LLM API Project in Python Step-By-Step
2. 30 JunLinear Layers and Activation Functions in Transformer Models
3. 28 JunLayerNorm and RMS Norm in Transformer Models
4. 26 Jun7 AI Agent Frameworks for Machine Learning Workflows in 2025
5. 26 JunA Gentle Introduction to Attention Masking in Transformer Models
6. 25 Jun10 Essential Machine Learning Key Terms Explained
7. 24 JunCombining XGBoost and Embeddings: Hybrid Semantic Boosted Trees?
8. 23 JunA Gentle Introduction to Multi-Head Latent Attention (MLA)
9. 23 JunConverting Pandas DataFrames to PyTorch DataLoaders for Custom Deep Learning Model Training
10. 20 JunBeyond GridSearchCV: Advanced Hyperparameter Tuning Strategies for Scikit-learn Models
11. 19 JunA Gentle Introduction to Multi-Head Attention and Grouped-Query Attention
12. 19 Jun10 Must-Know Python Libraries for MLOps in 2025
13. 18 JunUnlocking Performance: Accelerating Pandas Operations with Polars
14. 17 Jun7 Concepts Behind Large Language Models Explained in 7 Minutes
15. 17 JunInterpolation in Positional Encodings and Using YaRN for Larger Context Window
16. 16 JunHow to Combine Scikit-learn, CatBoost, and SHAP for Explainable Tree Models
17. 15 JunPositional Encodings in Transformer Models
18. 13 JunAdvanced Feature Engineering Using Scikit-Learn Pipelines with Pandas’ ColumnTransformer and NumPy Arrays
19. 12 JunNavigating Imbalanced Datasets with Pandas and Scikit-learn
20. 11 JunStep-by-Step Guide to Deploying Machine Learning Models with FastAPI and Docker
21. 10 JunImplementing Vector Search from Scratch: A Step-by-Step Tutorial
22. 9 JunHow to Optimize Language Model Size for Deployment
23. 6 JunDealing with Missing Data Strategically: Advanced Imputation Techniques in Pandas and Scikit-learn
24. 5 JunLoss Functions Explained: Understand the Maths in Just 2 Minutes Each
25. 5 Jun10 MLOps Tools for Machine Learning Practitioners to Know
26. 4 JunNumPy Ninjutsu: Mastering Array Operations for High-Performance Machine Learning
27. 3 Jun10 Python One-Liners That Will Simplify Feature Engineering
28. 2 JunWord Embeddings in Language Models

May 2025

1. 30 MayA Gentle Introduction to SHAP for Tree-Based Models
2. 29 MayUsing Quantized Models with Ollama for Application Development
3. 28 MayTokenizers in Language Models
4. 28 May10 Python Libraries That Speed Up Model Development
5. 27 MaySelecting the Right Feature Engineering Strategy: A Decision Tree Approach
6. 26 MayUsing NotebookLM as Your Machine Learning Study Guide
7. 24 MayEncoders and Decoders in Transformer Models
8. 23 MayA Gentle Introduction to Word Embedding and Text Vectorization
9. 23 MayUnderstanding OpenAI Codex CLI Commands
10. 22 May10 Underrated Books for Mastering Machine Learning
11. 21 May10 Python One-Liners for Feature Selection Like a Pro
12. 20 May5 Breakthrough Machine Learning Research Papers Already in 2025
13. 16 MayA Gentle Introduction to Learning Rate Schedulers
14. 14 MayCustom Fine-Tuning for Domain-Specific LLMs
15. 13 MayRoadmap to Python in 2025
16. 12 MayHow to Combine Pandas, NumPy, and Scikit-learn Seamlessly
17. 9 MayCreating a Qwen-Powered Lightweight Personal Assistant
18. 8 MayAttention May Be All We Need… But Why?
19. 7 MayCreating a Secure Machine Learning API with FastAPI and Docker
20. 6 MayZero-Shot and Few-Shot Learning with Reasoning LLMs
21. 6 MayAutomate Dataset Labeling with Active Learning

April 2025

1. 30 Apr7 Emerging Trends in Generative AI and Their Real-World Impact
2. 30 Apr5 Problems Encountered Fine-Tuning LLMs with Solutions
3. 29 AprPyTorch Lightning Hyperparameter Optimization with Optuna
4. 26 AprAdvanced Techniques to Build Your RAG System
5. 25 Apr10 Python One-Liners for Machine Learning Modeling
6. 23 AprBuilding RAG Systems with Transformers
7. 22 AprLet’s Build a RAG-Powered Research Paper Assistant
8. 21 Apr10 Must-Know Python Libraries for Machine Learning in 2025
9. 21 AprUnderstanding Text Generation Parameters in Transformers
10. 18 AprFurther Applications with Context Vectors
11. 18 AprBuilding a RAG Pipeline with llama.cpp in Python
12. 17 AprDetecting & Handling Data Drift in Production
13. 17 AprQuantization in Machine Learning: 5 Reasons Why It Matters More Than You Think
14. 16 AprApplications with Context Vectors
15. 14 AprGenerating and Visualizing Context Vectors in Transformers
16. 14 Apr5 Lessons Learned Building RAG Systems
17. 11 AprUnderstanding RAG Part X: RAG Pipelines in Production
18. 10 AprUnderstanding RAG Part IX: Fine-Tuning LLMs for RAG
19. 9 AprHow to Perform Scikit-learn Hyperparameter Optimization with Optuna
20. 8 Apr5 Reasons Why Traditional Machine Learning is Alive and Well in the Age of LLMs
21. 7 AprExample Applications of Text Embedding
22. 5 AprUsing Auto Classes in the Transformers Library
23. 4 AprText Embedding Generation with Transformers
24. 3 AprThe Beginner’s Guide to Clustering with Python
25. 2 AprThe Roadmap for Mastering MLOps in 2025
26. 1 AprFine-Tuning DistilBERT for Question Answering
27. 1 AprA Practical Guide to Building Local RAG Applications with LangChain

March 2025

1. 31 Mar3 Ways Vibe Coding and AI-Assisted Development Are 2 Different Things
2. 29 MarAdvanced Q&A Features with DistilBERT
3. 28 MarA Gentle Introduction to Attention and Transformer Models
4. 28 MarBuilding a Recommender System From Scratch with Matrix Factorization in Python
5. 26 MarThe Beginner’s Guide to Machine Learning with Rust
6. 25 MarA Gentle Introduction to Graph Neural Networks in Python
7. 24 Mar10 Must-Know Python Libraries for LLMs in 2025
8. 23 MarImplementing Multilingual Translation with T5 and Transformers
9. 21 MarBias Detection in LLM Outputs: Statistical Approaches
10. 20 MarBuilding Q&A Systems with DistilBERT and Transformers
11. 20 MarUnderstanding RAG Part VIII: Mitigating Hallucinations in RAG
12. 19 Mar6 Lesser-Known Scikit-Learn Features That Will Save You Time
13. 18 MarDebugging PyTorch Machine Learning Models: A Step-by-Step Guide
14. 17 MarA Gentle Introduction to Transformers Library
15. 17 MarThe Roadmap for Mastering Language Models in 2025
16. 14 MarStatistical Methods for Evaluating LLM Performance
17. 12 MarUnderstanding RAG Part VII: Vector Databases & Indexing Strategies
18. 12 MarMastering Time Series Forecasting: From ARIMA to LSTM
19. 11 MarA Complete Guide to Matrices for Machine Learning with Python
20. 10 MarThe Beginner’s Guide to Language Models with Python
21. 10 MarUnderstanding the DistilBart Model and ROUGE Metric
22. 8 MarText Summarization with DistilBart Model
23. 7 MarDiagnosing and Fixing Overfitting in Machine Learning with Python
24. 4 MarBuilding LLM Applications with Hugging Face Endpoints and FastAPI
25. 3 Mar10 Python One-Liners That Will Boost Your Data Preparation Workflow
26. 1 MarText Generation with GPT-2 Model

February 2025

1. 24 FebAuto-Completion Style Text Generation with GPT-2 Model
2. 21 FebUnderstanding RAG Part VI: Effective Retrieval Optimization
3. 19 FebUnderstanding Probability Distributions for Machine Learning with Python
4. 19 FebHow to Do Named Entity Recognition (NER) with a BERT Model
5. 18 FebUnderstanding RAG Part V: Managing Context Length
6. 17 FebIntegrating TensorFlow and NumPy for Custom Operations
7. 13 FebPrompt Engineering Patterns for Successful RAG Implementations
8. 12 FebImplementing Multi-Modal RAG Systems
9. 11 FebNext-Level Data Science (7-Day Mini-Course)
10. 11 FebCreating Custom Layers and Loss Functions in PyTorch
11. 11 FebThe Role of Domain Knowledge in Machine Learning: Why Subject Matter Experts Matter
12. 10 Feb10 Useful LangChain Components for Your Next RAG System
13. 7 FebBuilding Your First Multi-Agent System: A Beginner’s Guide
14. 7 FebTime Series Forecasting with PyCaret: Building Multi-Step Prediction Model
15. 4 FebAutomated Feature Engineering in PyCaret
16. 3 FebThe 2025 Machine Learning Toolbox: Top Libraries and Tools for Practitioners
17. 3 FebA Complete Introduction to Using BERT Models

January 2025

1. 30 JanAn Introduction to Logarithms in Machine Learning with Python
2. 30 JanOptimizing Memory Usage in PyTorch Models
3. 28 JanCreating Powerful Ensemble Models with PyCaret
4. 27 JanKernel Methods in Machine Learning with Python
5. 24 JanYOU SEE AN LLM HERE: Integrating Language Models Into Your Text Adventure Games
6. 23 JanUnderstanding RAG Part IV: RAGAs & Other Evaluation Frameworks
7. 22 Jan3 Easy Ways to Fine-Tune Language Models
8. 21 Jan5 Python Libraries to Build an Optimized RAG System
9. 20 JanFuture-Proof Your Machine Learning Career in 2025
10. 17 JanThe Roadmap for Mastering Machine Learning in 2025
11. 16 JanBuilding a Custom Model Pipeline in PyCaret: From Data Prep to Production
12. 13 JanA Practical Guide to the Claude API
13. 9 JanRAG Hallucination Detection Techniques
14. 8 Jan5 Common Mistakes to Avoid When Training LLMs
15. 6 Jan7 Next-Generation Prompt Engineering Techniques
16. 2 JanLLM Evaluation Metrics Made Easy
17. 1 Jan7 Machine Learning Trends to Watch in 2025

2024 157

December 2024

1. 30 DecMachine Learning Salaries and Job Market Analysis for 2024 and Beyond
2. 24 Dec5 of the Most Influential Machine Learning Papers of 2024
3. 23 Dec6 Language Model Concepts Explained for Beginners
4. 20 Dec5 Tools for Visualizing Machine Learning Models
5. 17 Dec7 Machine Learning Projects For Beginners
6. 16 DecHow to Quickly Deploy Machine Learning Models with Streamlit
7. 13 Dec10 Podcasts That Every Machine Learning Enthusiast Should Subscribe To
8. 10 DecThe Ultimate Guide to Building a Machine Learning Portfolio That Lands Jobs
9. 9 DecOptimizing Machine Learning Models for Production: A Step-by-Step Guide
10. 5 Dec5 Beginner-Friendly Projects to Learn LLMs & RAG
11. 4 Dec5 Free APIs for Building AI Applications
12. 3 DecMachine Learning vs. Traditional Analytics: When to Use Which?
13. 2 DecBuilding a Graph RAG System: A Step-by-Step Approach

November 2024

1. 28 Nov5 Free Courses for Mastering LLMs
2. 25 NovThe Beginner’s Guide to Natural Language Processing with Python
3. 22 NovDemystifying Ensemble Methods: Boosting, Bagging, and Stacking Explained
4. 20 NovA Practical Guide to Building Recommender Systems
5. 18 NovBuilding Your First Chatbot: A Hands-On Tutorial with Open-Source Tools
6. 15 Nov5 Tips for Avoiding Common Rookie Mistakes in Machine Learning Projects
7. 14 NovMastering the Art of Hyperparameter Tuning: Tips, Tricks, and Tools
8. 11 NovA Practical Guide to Choosing the Right Algorithm for Your Problem: From Regression to Neural Networks
9. 5 NovBuilding a Robust Machine Learning Pipeline: Best Practices and Common Pitfalls
10. 4 NovNatural Language Generation Inside Out: Teaching Machines to Write Like Humans
11. 1 NovIndustries in Focus: Machine Learning in Finance

October 2024

1. 31 OctFree Data and AI Courses with 365 Data Science—Unlimited Access until Nov 21
2. 31 Oct10 Python One-Liners That Will Boost Your Data Science Workflow
3. 30 OctAnomaly Detection Techniques in Large-Scale Datasets
4. 29 OctLeveraging Transfer Learning in Computer Vision for Quick Wins
5. 25 Oct7 Open-Source Machine Learning Projects You Can Contribute To Today
6. 24 OctFree AI-Powered Interview Preparation Tool by 365 Data Science
7. 23 OctUnderstanding RAG III: Fusion Retrieval and Reranking
8. 21 Oct5 Free Books on Computer Vision
9. 18 OctA Practical Guide to Deploying Machine Learning Models
10. 16 Oct7 Scikit-Learn Secrets You Probably Didn’t Know About
11. 15 OctInterpreting and Communicating Data Science Results
12. 14 OctUnderstanding RAG Part II: How Classic RAG Works
13. 12 OctFrom Features to Performance: Crafting Robust Predictive Models
14. 11 Oct5 Free Datasets to Kickstart Your Machine Learning Projects Today
15. 10 OctUnderstanding RAG Part I: Why It’s Needed
16. 9 OctPlanning Your Data Science Project
17. 9 OctA Roadmap for Your Machine Learning Career
18. 7 Oct7 Free Machine Learning Tools Every Beginner Should Master in 2024
19. 6 OctCatBoost Essentials: Building Robust Home Price Prediction Systems
20. 4 Oct7 Machine Learning Algorithms Every Data Scientist Should Know
21. 3 Oct7 LLM Projects to Boost Your Machine Learning Portfolio
22. 1 OctExploring LightGBM: Leaf-Wise Growth with GBDT and GOSS
23. 1 OctIndustries in Focus: Machine Learning for Cybersecurity Threat Detection

September 2024

1. 29 SepNavigating Missing Data Challenges with XGBoost
2. 26 SepBuilding 3 Fun AI Applications with ControlFlow
3. 25 SepBoosting Over Bagging: Enhancing Predictive Accuracy with Gradient Boosting Regressors
4. 23 Sep5 Free Courses to Master Deep Learning in 2024
5. 22 SepFrom Single Trees to Forests: Enhancing Real Estate Predictions with Ensembles
6. 20 SepDecision Trees and Ordinal Encoding: A Practical Guide
7. 20 SepFrom Data to Insights: A Beginner’s Journey in Exploratory Data Analysis
8. 19 Sep5 Real-World Machine Learning Projects You Can Build This Weekend
9. 18 SepThe Concise Guide to Feature Engineering for Better Model Performance
10. 17 SepBranching Out: Exploring Tree-Based Models for Regression
11. 13 SepAutomating Data Cleaning Processes with Pandas
12. 13 SepFilling the Gaps: A Comparative Guide to Imputation Techniques in Machine Learning
13. 12 SepComparing Scikit-Learn and TensorFlow for Machine Learning
14. 10 SepScaling to Success: Implementing and Optimizing Penalized Models
15. 9 SepTips for Using Machine Learning in Fraud Detection
16. 7 SepDetecting and Overcoming Perfect Multicollinearity in Large Datasets
17. 6 Sep5 Emerging AI Technologies That Will Shape the Future of Machine Learning
18. 5 SepTips for Effective Feature Selection in Machine Learning
19. 3 SepThe Power of Pipelines
20. 3 Sep10 Machine Learning Algorithms Explained Using Real-World Analogies

August 2024

1. 31 AugInterior Design with Stable Diffusion (8-day mini-course)
2. 30 AugUsing R for Predictive Modeling in Finance
3. 29 Aug5 Influential Machine Learning Papers You Should Read
4. 28 Aug5 Groundbreaking Applications of Reinforcement Learning in 2024
5. 27 AugEverything You Need to Know About the Hugging Face Model Hub and Community
6. 26 Aug10 Must-Know Python Libraries for Machine Learning in 2024
7. 26 AugCapturing Curves: Advanced Modeling with Polynomial Regression
8. 23 AugA Gentle Introduction to Bayesian Statistics
9. 22 AugInterpreting Coefficients in Linear Regression Models
10. 22 AugBasic Statistical Analysis with NumPy
11. 20 Aug3 Ways of Using Gemma 2 Locally
12. 19 Aug7 Machine Learning Projects That Can Add Value to Any Resume
13. 18 AugOne Hot Encoding: Understanding the “Hot” in Data
14. 14 AugThe Search for the Sweet Spot in a Linear Regression with Numeric Features
15. 13 AugFree Tools Every ML Beginner Should Use
16. 12 Aug5 Free Podcasts That Demystify Machine Learning Concepts
17. 12 AugBuilding a Simple RAG Application Using LlamaIndex
18. 12 AugThe Strategic Use of Sequential Feature Selector for Housing Price Predictions
19. 9 Aug5 Tips for Getting Started with Time Series Analysis
20. 7 AugFrom Train-Test to Cross-Validation: Advancing Your Model’s Evaluation
21. 6 AugTips for Tuning Hyperparameters in Machine Learning Models
22. 5 AugIntegrating Scikit-Learn and Statsmodels for Regression

July 2024

1. 31 Jul7 Free Resource to Master LLMs
2. 31 JulHow to Use Stable Diffusion Effectively
3. 30 Jul7 Key Terms Every Machine Learning Beginner Should Know
4. 29 Jul5 DIY Python Functions for Data Cleaning
5. 25 JulIntroduction to AutoML: Automating Machine Learning Workflows
6. 25 JulUsing ControlNet with Stable Diffusion
7. 23 Jul5 Challenges in Machine Learning Adoption and How to Overcome Them
8. 22 JulUnderstanding LangChain LLM Output Parser
9. 19 JulUsing Machine Learning in Customer Segmentation
10. 15 JulTips for Effectively Training Your Machine Learning Models
11. 10 JulPrinciples of Reinforcement Learning: An Introduction with Python
12. 9 Jul5 Tips for Getting Started with Deep Learning
13. 5 JulTips for Effective Feature Engineering in Machine Learning
14. 2 Jul5 Common Mistakes in Machine Learning and How to Avoid Them

June 2024

1. 30 JunStable Diffusion Project: Reviving Old Photos
2. 28 JunThe Ultimate Beginner’s Guide to Docker
3. 27 JunStable Diffusion Project: Commercial Poster
4. 25 Jun5 Effective Ways to Handle Imbalanced Data in Machine Learning
5. 23 JunTips for Choosing the Right Machine Learning Model for Your Data
6. 23 JunStable Diffusion Project: Creating Illustration
7. 21 Jun5 Free Books on Machine Learning Algorithms You Must Read
8. 20 JunStable Diffusion Project: Word Art
9. 20 Jun5 Free YouTube Channels Dedicated to Machine Learning Education
10. 18 JunTips for Choosing the Right Machine Learning Course
11. 17 Jun5 Free Courses on Reinforcement Learning
12. 15 JunTraining Stable Diffusion with Dreambooth
13. 14 Jun5 Free Resources for Understanding Neural Networks
14. 14 JunFine-Tuning Stable Diffusion with LoRA
15. 12 Jun5 Free Datasets to Start Your Machine Learning Projects
16. 11 JunInpainting and Outpainting with Diffusers
17. 10 Jun5 Free Platforms to Collaborate on Machine Learning Projects
18. 8 JunFurther Stable Diffusion Pipeline with Diffusers
19. 6 Jun5 Useful Loss Functions
20. 5 JunRunning Stable Diffusion with Python
21. 3 Jun5 Free Machine Learning Courses from Top Universities

May 2024

1. 29 MayTips for Deploying Machine Learning Models Efficiently
2. 24 MayBeginning Data Science (7-day mini-course)
3. 24 MayTips for Handling Imbalanced Data in Machine Learning
4. 22 May5 Essential Classification Algorithms Explained for Beginners
5. 20 May5 Essential Free Tools for Getting Started with LLMs
6. 5 MayMore Prompting Techniques for Stable Diffusion
7. 4 MayUsing OpenPose with Stable Diffusion

April 2024

1. 24 AprInpainting and Outpainting with Stable Diffusion
2. 20 AprGenerate Realistic Faces in Stable Diffusion
3. 18 AprUsing LoRA in Stable Diffusion
4. 14 AprPrompting Techniques for Stable Diffusion
5. 11 AprHow to Create Images Using Stable Diffusion Web UI
6. 6 AprA Technical Introduction to Stable Diffusion
7. 2 AprBrief Introduction to Diffusion Models for Image Generation

March 2024

1. 19 MarUnfolding Data Stories: From First Glance to In-Depth Analysis
2. 15 MarThe Da Vinci Code of Data: Mastering The Data Science Mind Map
3. 11 MarFinding Value with Data: The Cohesive Force Behind Luxury Real Estate Decisions
4. 6 MarSkewness Be Gone: Transformative Tricks for Data Scientists
5. 5 MarBest Free Resources to Learn Data Analysis and Data Science
6. 3 MarHarmonizing Data: A Symphony of Segmenting, Concatenating, Pivoting, and Merging
7. 1 MarBeyond SQL: Transforming Real Estate Data into Actionable Insights with Pandas

February 2024

1. 27 FebSpotting the Exception: Classical Methods for Outlier Detection in Data Science
2. 22 FebLeveraging ANOVA and Kruskal-Wallis Tests to Analyze the Impact of the Great Recession on Housing Prices
3. 19 FebGarage or Not? Housing Insights Through the Chi-Squared Test for Ames, Iowa
4. 15 FebTesting Assumptions in Real Estate: A Dive into Hypothesis Testing with the Ames Housing Dataset
5. 13 FebInferential Insights: How Confidence Intervals Illuminate the Ames Real Estate Market
6. 8 FebMastering Pair Plots for Visualization and Hypothesis Creation in the Ames Housing Market
7. 5 FebFeature Relationships 101: Lessons from the Ames Housing Data

January 2024

1. 31 JanExploring Dictionaries, Classifying Variables, and Imputing Data in the Ames Dataset
2. 29 JanFrom Data to Map: Visualizing Ames House Prices with Python
3. 25 JanDecoding Data: An Introduction to Descriptive Statistics with the Ames Housing Dataset
4. 22 JanRevealing the Invisible: Visualizing Missing Values in Ames Housing
5. 20 JanMachine Learning in OpenCV (7-Day Mini-Course)

2023 92

December 2023

1. 31 DecLogistic Regression for Image Classification Using OpenCV
2. 29 DecLogistic Regression in OpenCV
3. 25 DecRunning a Neural Network Model in OpenCV
4. 20 DecImage Vector Representation for Machine Learning Using OpenCV
5. 17 DecK-Means Clustering in OpenCV and Application for Color Quantization
6. 9 DecTraining a Haar Cascade Object Detector in OpenCV
7. 7 DecUsing Haar Cascade for Object Detection
8. 5 DecRandom Forest for Image Classification Using OpenCV
9. 1 DecNormal Bayes Classifier for Image Segmentation Using OpenCV

November 2023

1. 28 NovSupport Vector Machines for Image Classification and Detection Using OpenCV
2. 22 NovHardware-Accelerated AI for Windows Apps Using ONNX RT
3. 21 NovSupport Vector Machines in OpenCV
4. 17 NovHow to Train a Object Detection Engine with HOG in OpenCV
5. 15 NovImage Datasets for Practicing Machine Learning in OpenCV
6. 12 NovExtracting Histogram of Gradients with OpenCV
7. 10 NovImage Feature Extraction in OpenCV: Keypoints and Description Vectors
8. 7 NovImage Feature Extraction in OpenCV: Edges and Corners
9. 6 Nov365 Data Science Offers All Courses 100% Free for 2 Weeks
10. 3 NovK-Nearest Neighbors Classification Using OpenCV
11. 1 NovFast and Cheap Fine-Tuned LLM Inference with LoRA Exchange (LoRAX)

October 2023

1. 31 OctK-Means Clustering for Image Classification Using OpenCV
2. 29 OctHow to Transform Images and Create Video with OpenCV
3. 24 OctHow to Read and Display Videos Using OpenCV
4. 22 OctHow to Read, Write, Display Images in OpenCV and Converting Color Spaces
5. 21 OctA Gentle Introduction to OpenCV: An Open Source Library for Computer Vision and Machine Learning

September 2023

1. 26 SepUsing ggplot2 for Visualization in R
2. 23 SepExploring Data using dplyr in R
3. 15 SepA Gentle Introduction to Using R Markdown
4. 9 SepStatistical Tests in R
5. 5 SepGenerating Random Numbers in R
6. 2 SepPlotting Graphs in R

August 2023

1. 29 AugLogic, Flow Control, and Functions in R
2. 27 AugBuilt-in Datasets in R
3. 25 AugOperations on Vectors in R
4. 22 AugSurviving in the R Environment
5. 20 AugA Gentle Introduction to Lists and Data Frames in R
6. 18 AugA Gentle Introduction to Vectors in R
7. 15 AugCelebrating Devart’s 26th Birthday with an Exclusive 20% Discount on Data Connectivity Tools!
8. 15 AugAn Introduction to R

July 2023

1. 26 JulMOSTLY AI: The most accurate synthetic data generator
2. 24 JulBuilding Your mini-ChatGPT at Home
3. 4 JulAdvanced Techniques for Research with ChatGPT
4. 3 JulUsing the Natural Language Understanding Capability of ChatGPT

June 2023

1. 22 JunCreating a PowerPoint Presentation using ChatGPT
2. 20 JunGenerating Diagrams with ChatGPT
3. 18 JunUsing ChatGPT as Your Programming Assistant
4. 17 JunUsing ChatGPT as Your Personalized Teacher
5. 16 JunPrompt Engineering for Effective Interaction with ChatGPT
6. 14 JunMastering Summarization with ChatGPT
7. 8 JunUsing ChatGPT for Translation Effectively
8. 7 JunStrategies for Effective Brainstorming with ChatGPT
9. 5 JunChatGPT as Your Expert Helper
10. 1 JunA Gentle Introduction to Hallucinations in Large Language Models

May 2023

1. 28 MayA Gentle Introduction to Prompt Engineering
2. 26 MayWriting an Essay with ChatGPT
3. 23 MayWhat Are Zero-Shot Prompting and Few-Shot Prompting
4. 21 MayGet a Taste of LLMs from GPT4All
5. 19 MayWhat are Large Language Models
6. 2 MayActivation Functions in PyTorch

April 2023

1. 24 AprPyTorch Tutorial: How to Develop Deep Learning Models with Python
2. 4 AprDeep Learning with PyTorch (9-Day Mini-Course)

March 2023

1. 28 MarBuilding a Logistic Regression Classifier in PyTorch
2. 25 MarTraining Logistic Regression with Cross-Entropy Loss in PyTorch
3. 17 MarBuilding an Image Classifier with a Single-Layer Neural Network in PyTorch
4. 13 MarText Generation with LSTM in PyTorch
5. 10 MarLSTM for Time Series Prediction in PyTorch
6. 7 MarHandwritten Digit Recognition with LeNet5 Model in PyTorch
7. 5 MarBuilding a Convolutional Neural Network in PyTorch
8. 2 MarVisualizing a PyTorch Model

February 2023

1. 28 FebManaging a PyTorch Training Process with Checkpoints and Early Stopping
2. 26 FebUnderstand Model Behavior During Training by Visualizing Metrics
3. 23 FebTraining a PyTorch Model with DataLoader and Dataset
4. 21 FebUsing Learning Rate Schedule in PyTorch Training
5. 19 FebUsing Dropout Regularization in PyTorch Models
6. 16 FebLoss Functions in PyTorch Models
7. 14 FebUsing Activation Functions in Deep Learning Models
8. 12 FebSave and Load Your PyTorch Models
9. 9 FebHow to Grid Search Hyperparameters for PyTorch Models
10. 7 FebUse PyTorch Deep Learning Models with scikit-learn
11. 5 FebBuilding a Regression Model in PyTorch
12. 3 FebBuilding a Binary Classification Model in PyTorch
13. 1 FebBuilding a Multiclass Classification Model in PyTorch

January 2023

1. 30 JanHow to Evaluate the Performance of PyTorch Models
2. 28 JanCreating a Training Loop for PyTorch Models
3. 27 JanDevelop Your First Neural Network with PyTorch, Step by Step
4. 26 JanBuilding Multilayer Perceptron Models in PyTorch
5. 23 JanUsing Autograd in PyTorch to Solve a Regression Problem
6. 22 JanManipulating Tensors in PyTorch
7. 9 JanNeural Network with More Hidden Neurons
8. 7 JanBuilding a Single Layer Neural Network in PyTorch
9. 6 JanBuilding a Softmax Classifier for Images in PyTorch
10. 3 JanBuilding Transformer Models with Attention Crash Course. Build a Neural Machine Translator in 12 Days

2022 118

December 2022

1. 31 DecIntroduction to Softmax Classifier in PyTorch
2. 30 DecInitializing Weights for Deep Learning Models
3. 18 DecMaking Predictions with Logistic Regression in PyTorch
4. 16 DecTraining a Multi-Target Multilinear Regression Model in PyTorch
5. 14 DecMulti-Target Predictions with Multilinear Regression in PyTorch
6. 9 DecTraining a Single Output Multilinear Regression Model in PyTorch
7. 8 DecMaking Predictions with Multilinear Regression in PyTorch
8. 8 DecHow do you generate synthetic data for machine learning and why do you need it?
9. 7 DecTraining and Validation Data in PyTorch
10. 6 DecUsing Optimizers from PyTorch
11. 1 DecMini-Batch Gradient Descent and DataLoader in PyTorch

November 2022

1. 26 NovImplementing Gradient Descent in PyTorch
2. 24 NovTraining a Linear Regression Model in PyTorch
3. 24 NovMaking Linear Predictions in PyTorch
4. 19 NovLoading and Providing Datasets in PyTorch
5. 17 NovUsing Dataset Classes in PyTorch
6. 11 NovCalculating Derivatives in PyTorch
7. 9 NovTwo-Dimensional Tensors in Pytorch
8. 7 NovOne-Dimensional Tensors in Pytorch
9. 2 Nov365 Data Science courses free until November 21
10. 1 NovAttend the Data Science Symposium 2022, November 8 in Cincinnati

October 2022

1. 28 OctA Brief Introduction to BERT
2. 27 OctData Engineering for ML: Optimize for Cost Efficiency
3. 20 OctInteractive Machine Learning Live Course with Dr. Kirk Borne
4. 19 OctInferencing the Transformer Model
5. 18 OctPlotting the Training and Validation Loss Curves for the Transformer Model
6. 18 OctAttend the Data Science Symposium 2022
7. 12 OctTraining the Transformer Model
8. 9 OctJoining the Transformer Encoder and Decoder Plus Masking
9. 6 OctImplementing the Transformer Decoder from Scratch in TensorFlow and Keras
10. 4 OctImplementing the Transformer Encoder from Scratch in TensorFlow and Keras
11. 3 OctThe Vision Transformer Model

September 2022

1. 29 SepHow to Implement Multi-Head Attention from Scratch in TensorFlow and Keras
2. 26 SepHow to Implement Scaled Dot-Product Attention from Scratch in TensorFlow and Keras
3. 23 SepThe Transformer Positional Encoding Layer in Keras, Part 2
4. 22 SepTransformX by Scale AI is Oct 19-21: Register for free!
5. 19 SepA Gentle Introduction to Positional Encoding in Transformer Models, Part 1
6. 17 SepThe Transformer Model
7. 14 SepThe Transformer Attention Mechanism
8. 11 SepUnderstanding Simple Recurrent Neural Networks in Keras
9. 8 SepAn Introduction to Recurrent Neural Networks and the Math That Powers Them
10. 4 SepThe Luong Attention Mechanism
11. 3 SepThe Bahdanau Attention Mechanism
12. 1 SepAdding a Custom Attention Layer to a Recurrent Neural Network in Keras

August 2022

1. 30 AugJoin Doug Turnbull’s ‘ML Powered Search’ Live Cohort
2. 29 AugA Tour of Attention-Based Architectures
3. 26 AugThe Attention Mechanism from Scratch
4. 24 AugWhat Is Attention?
5. 22 AugA Bird’s Eye View of Research on Attention
6. 18 AugHow to Calculate Precision, Recall, F1, and More for Deep Learning Models
7. 18 AugLast call: Stefan Krawcyzk’s ‘Mastering MLOps’ Live Cohort
8. 16 AugHow to Make Predictions with Keras
9. 13 AugWhy Initialize a Neural Network with Random Weights?
10. 11 AugWhen to Use MLP, CNN, and RNN Neural Networks
11. 9 AugDifference Between a Batch and an Epoch in a Neural Network
12. 3 AugUsing Depthwise Separable Convolutions in Tensorflow

July 2022

1. 28 JulMastering MLOps: Live Model Deployment & Inference Course with Stefan Krawczyk
2. 27 JulTepper Wants to Nerd Out On Data With You
3. 20 JulImage Augmentation with Keras Preprocessing Layers and tf.image
4. 16 JulImage Augmentation for Deep Learning with Keras
5. 15 JulLoss Functions in TensorFlow
6. 14 JulHigh-Fidelity Synthetic Data for Data Engineers and Data Scientists Alike
7. 12 JulUnderstanding the Design of a Convolutional Neural Network
8. 11 JulA Gentle Introduction to the tensorflow.data API
9. 8 JulUsing Learning Rate Schedules for Deep Learning Models in Python with Keras
10. 5 JulBinary Classification Tutorial with the Keras Deep Learning Library
11. 5 JulDropout Regularization in Deep Learning Models with Keras
12. 4 JulUsing Activation Functions in Neural Networks
13. 2 JulHow to Checkpoint Deep Learning Models in Keras
14. 1 JulHow to Grid Search Hyperparameters for Deep Learning Models in Python with Keras

June 2022

1. 29 JunThree Ways to Build Machine Learning Models in Keras
2. 28 JunEvaluate the Performance of Deep Learning Models in Keras
3. 27 JunUsing Autograd in TensorFlow to Solve a Regression Problem
4. 25 JunOverview of Some Deep Learning Libraries
5. 21 JunHow to Build Multi-Layer Perceptron Neural Network Models with Keras
6. 18 JunHow to Save and Load Your Keras Deep Learning Model
7. 17 JunYour First Deep Learning Project in Python with Keras Step-by-Step
8. 14 JunUsing Normalization Layers to Improve Deep Learning Models
9. 9 JunIntroduction to the Python Deep Learning Library TensorFlow
10. 1 JunPython for Machine Learning (7-day mini-course)

May 2022

1. 28 MayDeveloping a Python Program Using Inspection Tools
2. 21 MayMonkey Patching Python Code
3. 18 MayLogging in Python
4. 14 MayProfiling Python Code
5. 9 MayStatic Analyzers in Python
6. 5 MaySetting Breakpoints and Exception Hooks in Python
7. 2 MayUsing Kaggle in Machine Learning Projects

April 2022

1. 29 AprTechniques to Write Better Python Code
2. 28 AprTake Your Machine Learning Skills Global
3. 27 AprGoogle Colab for Machine Learning Projects
4. 25 AprMultiprocessing in Python
5. 24 AprInteractive Course on Optimizing Search Engines With Ricardo Baeza-Yates Starting May 10
6. 22 AprWeb Frameworks for Your Python Projects
7. 20 AprA First Course on Deploying Python Projects
8. 19 Apr10 seats remaining | A series of live ML strategy workshops
9. 18 AprGuide to Iteratively Tuning GNNs
10. 18 AprManaging Data for Machine Learning Projects
11. 15 AprWeb Crawling in Python
12. 13 AprMassaging Data Using Pandas
13. 12 AprVery Deep Neural Networks Explained in 40 Seconds
14. 12 AprScientific Functions in NumPy and SciPy
15. 5 AprWin tickets to The AI Summit London 2022
16. 1 AprA Gentle Introduction to Decorators in Python

March 2022

1. 31 MarA Guide to Getting Datasets for Machine Learning in Python
2. 29 MarInteractive ML Strategy course with Foster Provost starting April 7
3. 29 MarA Guide to Obtaining Time Series Datasets in Python
4. 21 MarData Visualization in Python with matplotlib, Seaborn, and Bokeh
5. 15 MarCalculus for Machine Learning (7-day mini-course)
6. 15 MarExploring the Python Ecosystem
7. 2 MarA Gentle Introduction to Unit Testing in Python
8. 1 MarData Science Notebook Life-Hacks I Learned From Ploomber
9. 1 MarA Gentle Introduction to Serialization for Python

February 2022

1. 25 FebCommand Line Arguments for Your Python Script
2. 22 FebEasier Experimenting in Python
3. 16 FebDuck Typing, Scope, and Investigative Functions in Python
4. 9 FebComments, Docstrings, and Type Hints in Python Code
5. 1 FebMore Special Features in Python

January 2022

1. 6 JanPython Debugging Tools

2021 123

December 2021

1. 31 DecAnomaly Detection with Isolation Forest and Kernel Density Estimation
2. 29 DecRunning and Passing Information to a Python Script
3. 23 DecUnderstanding Traceback in Python
4. 19 DecFunctional Programming in Python
5. 14 DecPython Classes and Their Use in Keras
6. 8 DecSome Language Features in Python
7. 3 DecMethod of Lagrange Multipliers: The Theory Behind Support Vector Machines (Part 3: Implementing An SVM From Scratch In Python)
8. 1 DecMethod of Lagrange Multipliers: The Theory Behind Support Vector Machines (Part 2: The Non-Separable Case)

November 2021

1. 25 NovApplication of differentiations in neural networks
2. 24 NovMethod of Lagrange Multipliers: The Theory Behind Support Vector Machines (Part 1: The Separable Case)
3. 16 NovVisualizing the vanishing gradient problem
4. 14 NovUsing CNN for financial time series prediction

October 2021

1. 27 OctFace Recognition using Principal Component Analysis
2. 25 OctUsing Singular Value Decomposition to Build a Recommender System
3. 21 OctA Gentle Introduction to Vector Space Models
4. 20 OctPrincipal Component Analysis for Visualization
5. 11 OctOptimization for Machine Learning Crash Course

September 2021

1. 26 SepHow to Learn Python for Machine Learning
2. 22 SepTraining-validation-test split and cross-validation done right
3. 15 SepA Gentle Introduction to Particle Swarm Optimization

August 2021

1. 27 AugLagrange Multiplier Approach with Inequality Constraints
2. 24 AugA Gentle Introduction To Sigmoid Function
3. 22 AugCalculus in Action: Neural Networks
4. 19 AugA Gentle Introduction to Taylor Series
5. 17 AugA Gentle Introduction To Approximation
6. 16 AugThe Chain Rule of Calculus – Even More Functions
7. 13 AugThe Chain Rule of Calculus for Univariate and Multivariate Functions
8. 11 AugA Gentle Introduction To Method Of Lagrange Multipliers
9. 9 AugA Gentle Introduction to Optimization / Mathematical Programming
10. 6 AugA Gentle Introduction to the Laplacian
11. 4 AugA Gentle Introduction To Hessian Matrices
12. 2 AugA Gentle Introduction to the Jacobian

July 2021

1. 30 JulHigher-Order Derivatives
2. 28 JulA Gentle Introduction To Gradient Descent Procedure
3. 26 JulA Gentle Introduction To Partial Derivatives and Gradient Vectors
4. 23 JulA Gentle Introduction To Vector Valued Functions
5. 21 JulDifferential and Integral Calculus – Differentiate with Respect to Anything
6. 19 JulA Gentle Introduction to Multivariate Calculus
7. 15 JulApplications of Derivatives
8. 14 JulA Gentle Introduction to Continuous Functions
9. 12 JulA Gentle Introduction to Indeterminate Forms and L’Hospital’s Rule
10. 9 JulThe Power, Product and Quotient Rules
11. 7 JulDerivative of the Sine and Cosine
12. 5 JulA Gentle Introduction to Slopes and Tangents
13. 1 JulA Gentle Introduction to Derivatives of Powers and Polynomials

June 2021

1. 29 JunA Gentle Introduction to Function Derivatives
2. 27 JunA Gentle Introduction to Evaluating Limits
3. 24 JunA Gentle Introduction to Limits and Continuity
4. 22 JunWhat you need to know before you get started: A brief tour of Calculus Pre-Requisites
5. 21 JunCalculus in Machine Learning: Why it Works
6. 18 JunKey Concepts in Calculus: Rate of Change
7. 17 JunWhat is Calculus?
8. 15 JunDifferential Evolution from Scratch in Python
9. 13 JunModeling Pipeline Optimization With scikit-learn
10. 10 JunGradient Descent With AdaGrad From Scratch
11. 8 JunGradient Descent Optimization With AMSGrad From Scratch
12. 6 JunGradient Descent Optimization With AdaMax From Scratch
13. 3 JunA Gentle Introduction to Premature Convergence
14. 1 JunWhy Optimization Is Important in Machine Learning

May 2021

1. 30 MayA Gentle Introduction to Function Optimization
2. 27 MayOne-Dimensional (1D) Test Functions for Function Optimization
3. 25 MayLine Search Optimization With Python
4. 23 MayGradient Descent With RMSProp from Scratch
5. 20 MayDual Annealing Optimization With Python
6. 18 MayA Gentle Introduction to the BFGS Optimization Algorithm
7. 16 MayEssence of Bootstrap Aggregation Ensembles
8. 13 MayA Gentle Introduction to Ensemble Diversity for Machine Learning
9. 11 MayA Gentle Introduction to Multiple-Model Machine Learning
10. 9 MayEssence of Boosting Ensembles for Machine Learning
11. 6 MayEnsemble Machine Learning With Python (7-Day Mini-Course)
12. 4 MayHow to Develop a Weighted Average Ensemble With Python
13. 2 MayStrong Learners vs. Weak Learners in Ensemble Learning

April 2021

1. 29 AprA Gentle Introduction to Mixture of Experts Ensembles
2. 27 AprGrowing and Pruning Ensembles in Python
3. 25 AprDynamic Ensemble Selection (DES) for Classification in Python
4. 22 AprEssence of Stacking Ensembles for Machine Learning
5. 20 AprHow to Combine Predictions for Ensemble Learning
6. 18 AprA Gentle Introduction to Ensemble Learning Algorithms
7. 15 AprHow to Implement Gradient Descent Optimization from Scratch
8. 13 AprWhat Is a Gradient in Machine Learning?
9. 11 AprGradient Descent With Adadelta from Scratch
10. 8 AprWhat Is Semi-Supervised Learning
11. 6 AprDevelop a Neural Network for Cancer Survival Dataset
12. 4 AprNeural Network Models for Combined Classification and Regression
13. 1 AprIterated Local Search From Scratch in Python

March 2021

1. 30 MarDevelop a Neural Network for Woods Mammography Dataset
2. 28 MarTune XGBoost Performance With Learning Curves
3. 25 MarTwo-Dimensional (2D) Test Functions for Function Optimization
4. 23 MarHow to Manually Optimize Machine Learning Model Hyperparameters
5. 21 MarA Gentle Introduction to XGBoost Loss Functions
6. 18 MarGradient Descent Optimization With Nadam From Scratch
7. 16 MarGradient Descent With Nesterov Momentum From Scratch
8. 14 MarDevelop a Neural Network for Banknote Authentication
9. 11 MarXGBoost for Regression
10. 9 MarBasin Hopping Optimization in Python
11. 7 MarRandom Search and Grid Search for Function Optimization
12. 4 MarHow to Update Neural Network Models With More Data
13. 2 MarSimple Genetic Algorithm From Scratch in Python

February 2021

1. 28 FebDifferential Evolution Global Optimization With Python
2. 25 FebEvolution Strategies From Scratch in Python
3. 23 FebSensitivity Analysis of Dataset Size vs. Model Performance
4. 21 FebPrediction Intervals for Deep Learning Neural Networks
5. 18 FebSimulated Annealing From Scratch in Python
6. 16 FebNo Free Lunch Theorem for Machine Learning
7. 14 FebA Gentle Introduction to Stochastic Optimization Algorithms
8. 11 FebHow to Develop a Neural Net for Predicting Disturbances in the Ionosphere
9. 9 FebHow to Use Optimization Algorithms to Manually Fit Regression Models
10. 7 FebFunction Optimization With SciPy
11. 4 FebGradient Descent With Momentum from Scratch
12. 2 FebWeight Initialization for Deep Learning Neural Networks

January 2021

1. 31 JanDifference Between Backpropagation and Stochastic Gradient Descent
2. 28 JanLocal Optimization Versus Global Optimization
3. 26 JanHow to Develop a Neural Net for Predicting Car Insurance Payout
4. 24 JanHow to Use Nelder-Mead Optimization in Python
5. 21 JanHow to Get Started With Recommender Systems
6. 19 JanRegression Metrics for Machine Learning
7. 17 JanHow to Choose an Activation Function for Deep Learning
8. 14 JanVisualization for Function Optimization in Python
9. 12 JanCode Adam Optimization Algorithm From Scratch
10. 10 Jan3 Books on Optimization for Machine Learning
11. 7 JanUnivariate Function Optimization in Python
12. 5 JanA Gentle Introduction to Machine Learning Modeling Pipelines
13. 3 JanSemi-Supervised Learning With Label Spreading

2020 158

December 2020

1. 31 DecMultinomial Logistic Regression With Python
2. 29 DecSemi-Supervised Learning With Label Propagation
3. 27 DecHistogram-Based Gradient Boosting Ensembles in Python
4. 24 DecFeature Selection with Stochastic Optimization Algorithms
5. 22 DecHow to Choose an Optimization Algorithm
6. 20 DecEnsemble Learning Algorithm Complexity and Occam’s Razor
7. 17 DecWhat Is Meta-Learning in Machine Learning?
8. 15 DecCalculus Books for Machine Learning
9. 13 DecDynamic Classifier Selection Ensembles in Python
10. 10 DecPerceptron Algorithm for Classification in Python
11. 8 DecAutoencoder Feature Extraction for Regression
12. 6 DecAutoencoder Feature Extraction for Classification
13. 3 DecHow to Manually Optimize Neural Network Models
14. 1 DecBooks on Genetic Programming

November 2020

1. 29 NovBlending Ensemble Machine Learning With Python
2. 26 NovHow to Develop Random Forest Ensembles With XGBoost
3. 24 NovHow to Develop a Light Gradient Boosted Machine (LightGBM) Ensemble
4. 22 NovExtreme Gradient Boosting (XGBoost) Ensemble in Python
5. 19 NovA Gentle Introduction to PyCaret for Machine Learning
6. 17 NovHow to Develop a Feature Selection Subspace Ensemble in Python
7. 15 NovDevelop a Bagging Ensemble with Different Data Transformations
8. 12 NovMultivariate Adaptive Regression Splines (MARS) in Python
9. 10 NovHow to Identify Overfitting Machine Learning Models in Scikit-Learn
10. 8 NovDevelop an Intuition for How Ensemble Learning Works
11. 5 NovStochastic Hill Climbing in Python from Scratch
12. 3 NovCurve Fitting With Python
13. 1 NovRandom Forest for Time Series Forecasting

October 2020

1. 29 OctHow to Develop a Random Subspace Ensemble With Python
2. 27 OctError-Correcting Output Codes (ECOC) for Machine Learning
3. 25 OctWhy Use Ensemble Learning?
4. 22 OctA Gentle Introduction to Ensemble Learning
5. 20 Oct6 Books on Ensemble Learning
6. 18 OctSoftmax Activation Function with Python
7. 15 OctHow to Develop LARS Regression Models in Python
8. 13 OctNearest Shrunken Centroids With Python
9. 11 OctHow to Develop LASSO Regression Models in Python
10. 8 OctHow to Develop Ridge Regression Models in Python
11. 6 OctHow to Develop Elastic Net Regression Models in Python
12. 4 OctRobust Regression for Machine Learning in Python
13. 1 OctGaussian Processes for Classification With Python

September 2020

1. 29 SepRadius Neighbors Classifier Algorithm With Python
2. 27 SepLinear Discriminant Analysis With Python
3. 24 SepHow to Hill Climb the Test Set for Machine Learning
4. 22 SepHow to Train to the Test Set in Machine Learning
5. 20 SepMulti-Core Machine Learning in Python With Scikit-Learn
6. 17 SepAutomated Machine Learning (AutoML) Libraries for Python
7. 15 SepCombined Algorithm Selection and Hyperparameter Optimization (CASH Optimization)
8. 13 SepHyperparameter Optimization With Random Search and Grid Search
9. 10 SepHyperOpt for Automated Machine Learning With Scikit-Learn
10. 8 SepTPOT for Automated Machine Learning in Python
11. 6 SepAuto-Sklearn for Automated Machine Learning in Python
12. 3 SepScikit-Optimize for Hyperparameter Tuning in Machine Learning
13. 1 SepHow to Use AutoKeras for Classification and Regression

August 2020

1. 30 AugMulti-Label Classification with Deep Learning
2. 27 AugDeep Learning Models for Multi-Output Regression
3. 25 AugTime Series Forecasting With Prophet in Python
4. 23 AugHow to Set Axis for Rows and Columns in NumPy
5. 20 AugHypothesis Test for Comparing Machine Learning Algorithms
6. 18 AugHow to Calculate the Bias-Variance Trade-off with Python
7. 16 AugWhy Do I Get Different Results Each Time in Machine Learning?
8. 13 AugPlot a Decision Surface for Machine Learning Algorithms in Python
9. 11 AugA Gentle Introduction to Computational Learning Theory
10. 9 AugHow to use Seaborn Data Visualization for Machine Learning
11. 6 AugMulti-Class Imbalanced Classification
12. 4 AugHow to Use XGBoost for Time Series Forecasting
13. 2 AugRepeated k-Fold Cross-Validation for Model Evaluation in Python

July 2020

1. 30 JulHow to Configure k-Fold Cross-Validation
2. 28 JulNested Cross-Validation for Machine Learning with Python
3. 26 JulLOOCV for Evaluating Machine Learning Algorithms
4. 23 JulTrain-Test Split for Evaluating Machine Learning Algorithms
5. 21 JulHow to Selectively Scale Numerical Input Variables for Machine Learning
6. 19 JulAdd Binary Flags for Missing Values for Machine Learning
7. 16 JulHow to Create Custom Data Transforms for Scikit-Learn
8. 14 JulHow to Grid Search Data Preparation Techniques
9. 12 JulFramework for Data Preparation Techniques in Machine Learning
10. 9 Jul6 Dimensionality Reduction Algorithms With Python
11. 7 Jul4 Automatic Outlier Detection Algorithms in Python
12. 5 JulHow to Use Feature Extraction on Tabular Data for Machine Learning
13. 2 JulHow to Choose Data Preparation Methods for Machine Learning

June 2020

1. 30 Jun8 Top Books on Data Cleaning and Feature Engineering
2. 28 JunData Preparation for Machine Learning (7-Day Mini-Course)
3. 25 JunFeature Engineering and Selection (Book Review)
4. 23 JunkNN Imputation for Missing Values in Machine Learning
5. 21 JunHow to Avoid Data Leakage When Performing Data Preparation
6. 18 JunTour of Data Preparation Techniques for Machine Learning
7. 16 JunWhat Is Data Preparation in a Machine Learning Project
8. 14 JunWhy Data Preparation Is So Important in Machine Learning
9. 11 JunOrdinal and One-Hot Encodings for Categorical Data
10. 9 JunHow to Use StandardScaler and MinMaxScaler Transforms in Python
11. 7 JunHow to Perform Feature Selection for Regression Data
12. 4 JunHow to Perform Feature Selection With Numerical Input Data
13. 2 JunIterative Imputation for Missing Values in Machine Learning

May 2020

1. 31 MayTest-Time Augmentation For Tabular Data With Scikit-Learn
2. 28 MayHow to Use Polynomial Feature Transforms for Machine Learning
3. 26 MayHow to Scale Data With Outliers for Machine Learning
4. 24 MayRecursive Feature Elimination (RFE) for Feature Selection in Python
5. 21 MayHow to Use Discretization Transforms for Machine Learning
6. 19 MayHow to Use Quantile Transforms for Machine Learning
7. 17 MayHow to Use Power Transforms for Machine Learning
8. 14 MayStatistical Imputation for Missing Values in Machine Learning
9. 12 MayLinear Discriminant Analysis for Dimensionality Reduction in Python
10. 10 MaySingular Value Decomposition for Dimensionality Reduction in Python
11. 7 MayPrincipal Component Analysis for Dimensionality Reduction in Python
12. 5 MayIntroduction to Dimensionality Reduction for Machine Learning
13. 3 MayHow to Develop a Gradient Boosting Machine Ensemble in Python

April 2020

1. 30 AprHow to Develop an AdaBoost Ensemble in Python
2. 28 AprDifference Between Algorithm and Model in Machine Learning
3. 26 AprHow to Develop a Bagging Ensemble with Python
4. 23 AprA Gentle Introduction to Degrees of Freedom in Machine Learning
5. 21 AprHow to Develop an Extra Trees Ensemble with Python
6. 19 AprHow to Develop a Random Forest Ensemble in Python
7. 16 AprHow to Develop Voting Ensembles With Python
8. 14 AprHow to Handle Big-p, Little-n (p >> n) in Machine Learning
9. 12 AprOne-vs-Rest and One-vs-One for Multi-Class Classification
10. 9 AprStacking Ensemble Machine Learning With Python
11. 7 Apr4 Types of Classification Tasks in Machine Learning
12. 5 Apr10 Clustering Algorithms With Python
13. 2 AprWhat Is Argmax in Machine Learning?

March 2020

1. 31 MarGradient Boosting with Scikit-Learn, XGBoost, LightGBM, and CatBoost
2. 29 MarHow to Calculate Feature Importance With Python
3. 26 MarHow to Develop Multi-Output Regression Models with Python
4. 24 Mar4 Distance Measures for Machine Learning
5. 19 MarHow to Perform Data Cleaning for Machine Learning with Python
6. 17 MarNeural Networks are Function Approximation Algorithms
7. 15 MarImbalanced Multiclass Classification with the E.coli Dataset
8. 12 MarImbalanced Multiclass Classification with the Glass Identification Dataset
9. 10 MarImbalanced Classification with the Fraudulent Credit Card Transactions Dataset
10. 8 MarStep-By-Step Framework for Imbalanced Classification Projects
11. 5 MarImbalanced Classification with the Adult Income Dataset
12. 3 MarPredictive Model for the Phoneme Imbalanced Classification Dataset
13. 1 MarImbalanced Classification Model to Detect Mammography Microcalcifications

February 2020

1. 27 FebDevelop a Model for the Imbalanced Classification of Good and Bad Credit
2. 25 FebHow to Calibrate Probabilities for Imbalanced Classification
3. 23 FebA Gentle Introduction to the Fbeta-Measure for Machine Learning
4. 20 FebHow to Develop an Imbalanced Classification Model to Detect Oil Spills
5. 18 FebHow to Develop a Probabilistic Model of Breast Cancer Patient Survival
6. 16 FebWhy Is Imbalanced Classification Difficult?
7. 13 FebOne-Class Classification Algorithms for Imbalanced Datasets
8. 11 FebBagging and Random Forest for Imbalanced Classification
9. 9 FebA Gentle Introduction to Threshold-Moving for Imbalanced Classification
10. 6 FebCost-Sensitive Learning for Imbalanced Classification
11. 4 FebHow to Configure XGBoost for Imbalanced Classification
12. 1 FebHow to Develop a Cost-Sensitive Neural Network for Imbalanced Classification

January 2020

1. 30 JanCost-Sensitive SVM for Imbalanced Classification
2. 28 JanCost-Sensitive Decision Trees for Imbalanced Classification
3. 26 JanCost-Sensitive Logistic Regression for Imbalanced Classification
4. 23 JanTour of Data Sampling Methods for Imbalanced Classification
5. 21 JanHow to Combine Oversampling and Undersampling for Imbalanced Classification
6. 19 JanUndersampling Algorithms for Imbalanced Classification
7. 16 JanSMOTE for Imbalanced Classification with Python
8. 15 JanImbalanced Classification With Python (7-Day Mini-Course)
9. 14 JanRandom Oversampling and Undersampling for Imbalanced Classification
10. 13 JanWhat Is the Naive Classifier for Each Imbalanced Classification Metric?
11. 12 JanHow to Fix k-Fold Cross-Validation for Imbalanced Classification
12. 9 JanA Gentle Introduction to Probability Metrics for Imbalanced Classification
13. 7 JanTour of Evaluation Metrics for Imbalanced Classification
14. 5 JanROC Curves and Precision-Recall Curves for Imbalanced Classification
15. 2 JanHow to Calculate Precision, Recall, and F-Measure for Imbalanced Classification

2019 171

December 2019

1. 31 DecFailure of Classification Accuracy for Imbalanced Class Distributions
2. 29 DecStandard Machine Learning Datasets for Imbalanced Classification
3. 26 DecDevelop an Intuition for Severely Skewed Class Distributions
4. 24 DecBest Resources for Imbalanced Classification
5. 22 DecA Gentle Introduction to Imbalanced Classification
6. 19 DecHow to Use the ColumnTransformer for Data Preparation
7. 18 DecTensorFlow 2 Tutorial: Get Started in Deep Learning with tf.keras
8. 17 DecBest Results for Standard Machine Learning Datasets
9. 16 DecArithmetic, Geometric, and Harmonic Means for Machine Learning
10. 15 DecHow to Transform Target Variables for Regression in Python
11. 12 DecTune Hyperparameters for Classification Machine Learning Algorithms
12. 10 DecHow to Develop Super Learner Ensembles in Python
13. 8 DecDevelop an Intuition for Bayes Theorem With Worked Examples
14. 5 DecHow to Use Out-of-Fold Predictions in Machine Learning
15. 3 DecA Gentle Introduction to the Bayes Optimal Classifier
16. 1 DecA Gentle Introduction to Model Selection for Machine Learning

November 2019

1. 28 NovHow to Use an Empirical Distribution Function in Python
2. 26 NovHow to Choose a Feature Selection Method For Machine Learning
3. 24 NovHow to Perform Feature Selection with Categorical Data
4. 21 Nov3 Ways to Encode Categorical Variables for Deep Learning
5. 19 NovHow to Save and Reuse Data Preparation Objects in Scikit-Learn
6. 17 NovWhat Does Stochastic Mean in Machine Learning?
7. 14 NovHow to Connect Model Input Data With Predictions for Machine Learning
8. 12 NovHow to Save a NumPy Array to File for Machine Learning
9. 10 Nov14 Different Types of Learning in Machine Learning
10. 7 NovA Gentle Introduction to Maximum a Posteriori (MAP) for Machine Learning
11. 5 NovA Gentle Introduction to Markov Chain Monte Carlo for Probability
12. 3 NovA Gentle Introduction to Monte Carlo Sampling for Probability

October 2019

1. 31 OctA Gentle Introduction to Expectation-Maximization (EM Algorithm)
2. 29 OctProbabilistic Model Selection with AIC, BIC, and MDL
3. 27 OctA Gentle Introduction to Logistic Regression With Maximum Likelihood Estimation
4. 24 OctA Gentle Introduction to Linear Regression With Maximum Likelihood Estimation
5. 23 OctDevelop k-Nearest Neighbors in Python From Scratch
6. 22 OctA Gentle Introduction to Maximum Likelihood Estimation for Machine Learning
7. 20 OctA Gentle Introduction to Cross-Entropy for Machine Learning
8. 17 OctNaive Bayes Classifier From Scratch in Python
9. 17 OctHow to Calculate the KL Divergence for Machine Learning
10. 15 OctInformation Gain and Mutual Information for Machine Learning
11. 13 OctA Gentle Introduction to Information Entropy
12. 10 OctA Gentle Introduction to Bayesian Belief Networks
13. 8 OctHow to Implement Bayesian Optimization from Scratch in Python
14. 6 OctHow to Develop a Naive Bayes Classifier from Scratch in Python
15. 3 OctA Gentle Introduction to Bayes Theorem for Machine Learning
16. 2 OctProbability for Machine Learning (7-Day Mini-Course)
17. 1 OctHow to Develop an Intuition for Probability With Worked Examples

September 2019

1. 29 SepHow to Develop an Intuition for Joint, Marginal, and Conditional Probability
2. 26 SepA Gentle Introduction to Joint, Marginal, and Conditional Probability
3. 24 SepA Gentle Introduction to Probability Density Estimation
4. 22 SepContinuous Probability Distributions for Machine Learning
5. 19 SepDiscrete Probability Distributions for Machine Learning
6. 17 SepA Gentle Introduction to Probability Distributions
7. 15 SepWhat Is Probability?
8. 12 SepA Gentle Introduction to Uncertainty in Machine Learning
9. 10 Sep5 Reasons to Learn Probability for Machine Learning
10. 8 SepResources for Getting Started With Probability in Machine Learning
11. 5 SepHow to Develop and Evaluate Naive Classifier Strategies Using Probability
12. 3 SepA Gentle Introduction to Jensen’s Inequality
13. 1 SepA Gentle Introduction to Generative Adversarial Network Loss Functions

August 2019

1. 29 AugHow to Implement the Frechet Inception Distance (FID) for Evaluating GANs
2. 27 AugHow to Implement the Inception Score (IS) for Evaluating GANs
3. 25 AugHow to Evaluate Generative Adversarial Networks
4. 22 AugA Gentle Introduction to BigGAN the Big Generative Adversarial Network
5. 20 Aug9 Books on Generative Adversarial Networks (GANs)
6. 18 AugA Gentle Introduction to StyleGAN the Style Generative Adversarial Network
7. 15 AugWhat is Deep Learning?
8. 15 AugHow to Train a Progressive Growing GAN in Keras for Synthesizing Faces
9. 13 AugHow to Implement Progressive Growing GAN Models in Keras
10. 11 AugA Tour of Machine Learning Algorithms
11. 11 AugA Gentle Introduction to the Progressive Growing GAN
12. 8 AugHow to Develop a CycleGAN for Image-to-Image Translation with Keras
13. 6 AugHow to Implement CycleGAN Models From Scratch With Keras
14. 4 AugA Gentle Introduction to CycleGAN for Image Translation
15. 1 AugHow to Develop a Pix2Pix GAN for Image-to-Image Translation

July 2019

1. 30 JulHow to Implement Pix2Pix GAN Models From Scratch With Keras
2. 28 JulA Gentle Introduction to Pix2Pix Generative Adversarial Network
3. 25 JulHow to Develop a Least Squares Generative Adversarial Network (LSGAN) in Keras
4. 23 JulHow to Implement a Semi-Supervised GAN (SGAN) From Scratch in Keras
5. 21 JulHow to Develop an Information Maximizing GAN (InfoGAN) in Keras
6. 18 JulHow to Develop an Auxiliary Classifier GAN (AC-GAN) From Scratch with Keras
7. 16 JulHow to Develop a Wasserstein Generative Adversarial Network (WGAN) From Scratch
8. 14 JulHow to Implement Wasserstein Loss for Generative Adversarial Networks
9. 11 JulHow to Code the GAN Training Algorithm and Loss Functions
10. 10 JulHow to Get Started With Generative Adversarial Networks (7-Day Mini-Course)
11. 9 JulA Tour of Generative Adversarial Network Models
12. 7 JulHow to Identify and Diagnose GAN Failure Modes
13. 4 JulHow to Develop a Conditional GAN (cGAN) From Scratch
14. 2 JulHow to Explore the GAN Latent Space When Generating Faces

June 2019

1. 30 JunHow to Develop a GAN to Generate CIFAR10 Small Color Photographs
2. 27 JunHow to Develop a GAN for Generating MNIST Handwritten Digits
3. 26 JunHow to Develop a Deep Learning Photo Caption Generator from Scratch
4. 25 JunHow to Develop a 1D Generative Adversarial Network From Scratch in Keras
5. 23 JunHow to use the UpSampling2D and Conv2DTranspose Layers in Keras
6. 20 JunHow to Implement GAN Hacks in Keras to Train Stable Models
7. 18 JunTips for Training Stable Generative Adversarial Networks
8. 16 JunA Gentle Introduction to Generative Adversarial Networks (GANs)
9. 13 Jun18 Impressive Applications of Generative Adversarial Networks (GANs)
10. 11 JunBest Resources for Getting Started With GANs
11. 9 JunOne-Shot Learning for Face Recognition
12. 6 JunHow to Develop a Face Recognition System Using FaceNet in Keras
13. 4 JunHow to Perform Face Recognition With VGGFace2 in Keras
14. 2 JunHow to Perform Face Detection with Deep Learning

May 2019

1. 30 MayA Gentle Introduction to Deep Learning for Face Recognition
2. 28 MayHow to Train an Object Detection Model with Keras
3. 26 MayHow to Perform Object Detection With YOLOv3 in Keras
4. 23 MayHow to Use Mask R-CNN in Keras for Object Detection in Photographs
5. 21 MayA Gentle Introduction to Object Recognition With Deep Learning
6. 19 MayMulti-Label Classification of Satellite Photos of the Amazon Rainforest
7. 16 MayHow to Classify Photos of Dogs and Cats (with 97% accuracy)
8. 14 MayTransfer Learning in Keras with Computer Vision Models
9. 12 MayHow to Develop a CNN From Scratch for CIFAR-10 Photo Classification
10. 9 MayDeep Learning CNN for Fashion-MNIST Clothing Classification
11. 7 MayHow to Develop a CNN for MNIST Handwritten Digit Classification
12. 5 MayHow to Visualize Filters and Feature Maps in Convolutional Neural Networks
13. 2 MayBest Practices for Preparing and Augmenting Image Data for CNNs

April 2019

1. 30 AprA Gentle Introduction to the ImageNet Challenge (ILSVRC)
2. 28 AprA Gentle Introduction to 1×1 Convolutions to Manage Model Complexity
3. 25 AprHow to Develop VGG, Inception and ResNet Modules from Scratch in Keras
4. 23 AprConvolutional Neural Network Model Innovations for Image Classification
5. 21 AprA Gentle Introduction to Pooling Layers for Convolutional Neural Networks
6. 18 AprA Gentle Introduction to Padding and Stride for Convolutional Neural Networks
7. 16 AprHow Do Convolutional Layers Work in Deep Learning Neural Networks?
8. 14 AprHow to Use Test-Time Augmentation to Make Better Predictions
9. 11 AprHow to Configure Image Data Augmentation in Keras
10. 9 AprHow to Load Large Datasets From Directories for Deep Learning in Keras
11. 8 AprHow to Get Started With Deep Learning for Computer Vision (7-Day Mini-Course)
12. 7 AprHow to Load and Visualize Standard Computer Vision Datasets With Keras
13. 4 AprA Gentle Introduction to Channels-First and Channels-Last Image Formats
14. 2 AprHow to Normalize, Center, and Standardize Image Pixels in Keras

March 2019

1. 31 MarHow to Load, Convert, and Save Images With the Keras API
2. 26 MarHow to Evaluate Pixel Scaling Methods for Image Classification With CNNs
3. 25 MarA Gentle Introduction to the Promise of Deep Learning for Computer Vision
4. 24 MarHow to Manually Scale Image Pixel Data for Deep Learning
5. 21 MarHow to Load and Manipulate Images for Deep Learning in Python With PIL/Pillow
6. 19 MarStanford Convolutional Neural Networks for Visual Recognition Course (Review)
7. 18 MarA Gentle Introduction to Computer Vision
8. 17 MarDeepLearning.AI Convolutional Neural Networks Course (Review)
9. 14 Mar8 Books for Getting Started With Computer Vision
10. 12 Mar9 Applications of Deep Learning for Computer Vision
11. 10 MarHow to Develop Competence With Deep Learning for Computer Vision
12. 7 MarHow to Demonstrate Your Basic Skills with Deep Learning
13. 5 Mar3 Levels of Deep Learning Competence
14. 3 MarWhat is a Hypothesis in Machine Learning?

February 2019

1. 28 FebWhy Training a Neural Network Is Hard
2. 26 FebHow to use Learning Curves to Diagnose Machine Learning Model Performance
3. 25 FebHow to Fix FutureWarning Messages in scikit-learn
4. 24 FebRecommendations for Deep Learning Neural Network Practitioners
5. 21 Feb8 Tricks for Configuring Backpropagation to Train Better Neural Networks
6. 19 FebNeural Networks: Tricks of the Trade Review
7. 17 FebHow to Get Better Deep Learning Results (7-Day Mini-Course)
8. 14 FebA Gentle Introduction to the Challenge of Training Deep Learning Neural Network Models
9. 12 FebHow to Control Neural Network Model Capacity With Nodes and Layers
10. 10 FebFramework for Better Deep Learning
11. 9 FebYour First Machine Learning Project in Python Step-By-Step
12. 7 FebHow to Improve Performance With Transfer Learning for Deep Learning Neural Networks
13. 5 FebHow to Avoid Exploding Gradients With Gradient Clipping
14. 3 FebHow to use Data Scaling Improve Deep Learning Model Stability and Performance

January 2019

1. 31 JanHow to Use Greedy Layer-Wise Pretraining in Deep Learning Neural Networks
2. 29 JanHow to Choose Loss Functions When Training Deep Learning Neural Networks
3. 27 JanLoss and Loss Functions for Training Deep Learning Neural Networks
4. 24 JanUnderstand the Impact of Learning Rate on Neural Network Performance
5. 22 JanHow to Configure the Learning Rate When Training Deep Learning Neural Networks
6. 20 JanHow to Control the Stability of Training Neural Networks With the Batch Size
7. 17 JanHow to Accelerate Learning of Deep Neural Networks With Batch Normalization
8. 16 JanPractical Deep Learning for Coders (Review)
9. 15 JanA Gentle Introduction to Batch Normalization for Deep Neural Networks
10. 13 Jan3 Must-Own Books for Deep Learning Practitioners
11. 10 JanHow to Fix the Vanishing Gradients Problem Using the ReLU
12. 8 JanA Gentle Introduction to the Rectified Linear Unit (ReLU)
13. 6 JanEnsemble Neural Network Model Weights in Keras (Polyak Averaging)
14. 3 JanSnapshot Ensemble Deep Learning Neural Network in Python
15. 1 JanImpact of Dataset Size on Deep Learning Model Skill And Performance Estimates

2018 152

December 2018

1. 30 DecStacking Ensemble for Deep Learning Neural Networks in Python
2. 27 DecHow to Develop a Weighted Average Ensemble for Deep Learning Neural Networks
3. 25 DecHow to Develop a Horizontal Voting Deep Learning Ensemble to Reduce Variance
4. 23 DecHow to Create a Bagging Ensemble of Deep Learning Models in Keras
5. 20 DecHow to Develop an Ensemble of Deep Learning Models in Keras
6. 18 DecEnsemble Learning Methods for Deep Learning Neural Networks
7. 16 DecHow to Avoid Overfitting in Deep Learning Neural Networks
8. 13 DecHow to Improve Deep Learning Model Robustness by Adding Noise
9. 11 DecTrain Neural Networks With Noise to Reduce Overfitting
10. 9 DecUse Early Stopping to Halt the Training of Neural Networks At the Right Time
11. 6 DecA Gentle Introduction to Early Stopping to Avoid Overtraining Neural Networks
12. 4 DecHow to Reduce Overfitting With Dropout Regularization in Keras
13. 2 DecA Gentle Introduction to Dropout for Regularizing Deep Neural Networks

November 2018

1. 29 NovHow to Reduce Generalization Error With Activity Regularization in Keras
2. 27 NovA Gentle Introduction to Activation Regularization in Deep Learning
3. 25 NovHow to Reduce Overfitting Using Weight Constraints in Keras
4. 22 NovA Gentle Introduction to Weight Constraints in Deep Learning
5. 20 NovHow to Use Weight Decay to Reduce Overfitting of Neural Network in Keras
6. 18 NovUse Weight Regularization to Reduce Overfitting of Deep Learning Models
7. 15 NovHow to Grid Search Deep Learning Models for Time Series Forecasting
8. 13 NovHow to Develop LSTM Models for Time Series Forecasting
9. 11 NovHow to Develop Convolutional Neural Network Models for Time Series Forecasting
10. 8 NovHow to Develop Multilayer Perceptron Models for Time Series Forecasting
11. 6 NovHow to Use the TimeseriesGenerator for Time Series Forecasting in Keras
12. 4 NovA Gentle Introduction to LSTM Autoencoders
13. 1 NovLSTM Model Architecture for Rare Event Time Series Forecasting

October 2018

1. 30 OctComparing Classical and Machine Learning Algorithms for Time Series Forecasting
2. 28 OctDeep Learning Models for Univariate Time Series Forecasting
3. 25 OctHow to Grid Search Naive Methods for Univariate Time Series Forecasting
4. 23 OctHow to Grid Search SARIMA Hyperparameters for Time Series Forecasting
5. 21 OctHow to Grid Search Triple Exponential Smoothing for Time Series Forecasting in Python
6. 18 OctHow to Develop Multivariate Multi-Step Time Series Forecasting Models for Air Pollution
7. 16 OctHow to Develop Multi-Step Time Series Forecasting Models for Air Pollution
8. 14 OctHow to Develop Baseline Forecasts for Multi-Site Multivariate Air Pollution Time Series Forecasting
9. 11 OctHow to Load, Visualize, and Explore a Multivariate Multistep Time Series Dataset
10. 9 OctMulti-Step LSTM Time Series Forecasting Models for Power Usage
11. 7 OctConvolutional Neural Networks for Multi-Step Time Series Forecasting
12. 4 OctMulti-step Time Series Forecasting with Machine Learning for Electricity Usage
13. 2 OctAutoregression Forecast Model for Household Electricity Consumption

September 2018

1. 30 SepEvaluate Naive Models for Forecasting Household Electricity Consumption
2. 27 SepHow to Load and Explore Household Electricity Usage Data
3. 25 SepDeep Learning Models for Human Activity Recognition
4. 23 SepLSTMs for Human Activity Recognition Time Series Classification
5. 20 Sep1D Convolutional Neural Network Models for Human Activity Recognition
6. 18 SepEvaluate Machine Learning Algorithms for Human Activity Recognition
7. 16 SepHow to Model Human Activity From Smartphone Data
8. 13 SepHow to Develop a Framework to Spot-Check Machine Learning Algorithms in Python
9. 11 SepA Gentle Introduction to a Standard Human Activity Recognition Problem
10. 9 SepIndoor Movement Time Series Classification with Machine Learning Algorithms
11. 6 SepProbabilistic Forecasting Model to Predict Air Pollution Days
12. 4 SepA Gentle Introduction to Probability Scoring Methods in Python
13. 3 SepHow to Get Started with Deep Learning for Time Series Forecasting (7-Day Mini-Course)
14. 2 SepHow and When to Use a Calibrated Classification Model with scikit-learn

August 2018

1. 30 AugHow to Use ROC Curves and Precision-Recall Curves for Classification in Python
2. 28 AugHow to Predict Room Occupancy Based on Environmental Factors
3. 26 AugPredict Whether a Persons Eyes are Open or Closed Using Brain Waves
4. 23 AugHow to Model Volatility with ARCH and GARCH for Time Series Forecasting in Python
5. 21 Aug4 Common Machine Learning Data Transforms for Time Series Forecasting
6. 19 AugA Gentle Introduction to Exponential Smoothing for Time Series Forecasting in Python
7. 16 AugA Gentle Introduction to SARIMA for Time Series Forecasting in Python
8. 14 Aug17 Statistical Hypothesis Tests in Python (Cheat Sheet)
9. 12 AugHow to Reduce Variance in a Final Machine Learning Model
10. 9 AugHow to Develop a Skillful Machine Learning Time Series Forecasting Model
11. 7 AugTaxonomy of Time Series Forecasting Problems
12. 5 Aug11 Classical Time Series Forecasting Methods in Python (Cheat Sheet)
13. 2 AugStatistics for Machine Learning (7-Day Mini-Course)

July 2018

1. 29 JulHow to Code the Student’s t-Test from Scratch in Python
2. 26 JulHow to Configure the Number of Layers and Nodes in a Neural Network
3. 24 JulHow to Calculate McNemar’s Test to Compare Two Machine Learning Classifiers
4. 17 JulThe Role of Randomization to Address Confounding Variables in Machine Learning
5. 15 JulAll of Statistics for Machine Learning
6. 12 JulA Gentle Introduction to Statistical Power and Power Analysis in Python
7. 10 JulA Gentle Introduction to Effect Size Measures in Python
8. 8 JulHow to Calculate Nonparametric Rank Correlation in Python
9. 5 JulStatistics in Plain English for Machine Learning
10. 3 JulHow to Generate Random Numbers in Python
11. 1 JulStatistics for Evaluating Machine Learning Models

June 2018

1. 28 JunThe Close Relationship Between Applied Statistics and Machine Learning
2. 26 JunWhat is Statistics (and why is it important in machine learning)?
3. 24 Jun10 Examples of How to Use Statistical Methods in a Machine Learning Project
4. 21 JunControlled Experiments in Machine Learning
5. 19 JunStatistical Significance Tests for Comparing Machine Learning Algorithms
6. 14 JunA Gentle Introduction to the Chi-Squared Test for Machine Learning
7. 12 JunHow to Calculate the 5-Number Summary for Your Data in Python
8. 12 JunA Gentle Introduction to Statistical Sampling and Resampling
9. 10 JunHow to Calculate Critical Values for Statistical Hypothesis Testing with Python
10. 7 JunA Gentle Introduction to Statistical Data Distributions
11. 5 JunA Gentle Introduction to Data Visualization Methods in Python
12. 3 JunA Gentle Introduction to Estimation Statistics for Machine Learning

May 2018

1. 31 MayA Gentle Introduction to Statistical Tolerance Intervals in Machine Learning
2. 29 MayPrediction Intervals for Machine Learning
3. 27 MayConfidence Intervals for Machine Learning
4. 24 MayA Gentle Introduction to the Bootstrap Method
5. 22 MayA Gentle Introduction to k-fold Cross-Validation
6. 20 MayHow to Transform Data to Better Fit The Normal Distribution
7. 17 MayHow to Calculate Parametric Statistical Hypothesis Tests in Python
8. 15 MayHow to Calculate Nonparametric Statistical Hypothesis Tests in Python
9. 13 MayA Gentle Introduction to Statistical Hypothesis Testing
10. 10 MayA Gentle Introduction to Normality Tests in Python
11. 8 MayA Gentle Introduction to Nonparametric Statistics
12. 6 MayStatistics Books for Machine Learning
13. 3 MayA Gentle Introduction to the Central Limit Theorem for Machine Learning
14. 1 MayA Gentle Introduction to the Law of Large Numbers in Machine Learning

April 2018

1. 29 AprA Gentle Introduction to Calculating Normal Summary Statistics
2. 26 AprHow to Calculate Correlation Between Variables in Python
3. 24 AprHow to Remove Outliers for Machine Learning
4. 22 AprIntroduction to Random Number Generators for Machine Learning in Python
5. 19 AprHow To Know if Your Machine Learning Model Has Good Performance
6. 17 AprThe Model Performance Mismatch Problem (and what to do about it)
7. 15 AprHow to Get the Most From Your Machine Learning Data
8. 12 AprAnalytical vs Numerical Solutions in Machine Learning
9. 10 AprMachine Learning Development Environment
10. 5 AprHow to Make Predictions with scikit-learn
11. 3 AprSo, You are Working on a Machine Learning Problem…
12. 1 AprHow to Think About Machine Learning

March 2018

1. 29 MarComparing 13 Algorithms on 165 Datasets (hint: use Gradient Boosting)
2. 27 MarWhy Machine Learning Does Not Have to Be So Hard
3. 25 MarBasics of Mathematical Notation for Machine Learning
4. 22 MarLinear Algebra for Machine Learning (7-Day Mini-Course)
5. 20 MarComputational Linear Algebra for Coders Review
6. 15 MarLinear Algebra for Deep Learning
7. 13 MarA Gentle Introduction to Sparse Matrices for Machine Learning
8. 11 MarA Gentle Introduction to Broadcasting with NumPy Arrays
9. 8 Mar10 Examples of Linear Algebra in Machine Learning
10. 6 MarNo Bullshit Guide To Linear Algebra Review
11. 4 MarHow to Solve Linear Regression Using Linear Algebra
12. 1 MarHow to Calculate Principal Component Analysis (PCA) from Scratch in Python

February 2018

1. 27 FebA Gentle Introduction to Expected Value, Variance, and Covariance with NumPy
2. 25 FebHow to Calculate the SVD from Scratch with Python
3. 22 FebLinear Algebra Cheat Sheet for Machine Learning
4. 20 FebTop Resources for Learning Linear Algebra for Machine Learning
5. 18 FebGentle Introduction to Eigenvalues and Eigenvectors for Machine Learning
6. 15 FebA Gentle Introduction to Matrix Factorization for Machine Learning
7. 13 FebA Gentle Introduction to Tensors for Machine Learning with NumPy
8. 11 FebA Gentle Introduction to Matrix Operations for Machine Learning
9. 8 FebMatrix Types in Linear Algebra for Machine Learning
10. 6 FebIntroduction to Matrices and Matrix Arithmetic for Machine Learning
11. 4 FebGentle Introduction to Vector Norms in Machine Learning
12. 1 FebA Gentle Introduction to Vectors for Machine Learning

January 2018

1. 30 JanA Gentle Introduction to NumPy Arrays in Python
2. 28 Jan5 Reasons to Learn Linear Algebra for Machine Learning
3. 25 JanA Gentle Introduction to Linear Algebra
4. 23 JanWhy Do Machine Learning Algorithms Work on New Data?
5. 21 JanHow to Run Deep Learning Experiments on a Linux Server
6. 18 JanA Standard Multivariate, Multi-Step, and Multi-Site Time Series Forecasting Problem
7. 16 JanHow to Install XGBoost for Python on macOS
8. 14 JanHow to Generate Test Datasets in Python with scikit-learn
9. 11 JanHow to Develop a Multichannel CNN Model for Text Classification
10. 9 JanHow to Develop a Neural Machine Translation System from Scratch
11. 7 JanHow to Prepare a French-to-English Dataset for Machine Translation
12. 4 JanHow to Implement a Beam Search Decoder for Natural Language Processing
13. 2 JanHow to Configure an Encoder-Decoder Model for Neural Machine Translation