Complete Guide

Complete ML and AI Tutorial

Machine learning is reshaping every industry. This guide covers all 198 ML and AI tutorials on TheCodeForge — from regression and classification to neural networks, transformers and deployment.

Learning Roadmap

Beginner → Understand statistics, linear algebra and Python for data science

Intermediate → Build ML models with scikit-learn, handle data and evaluate results

Advanced → Work with neural networks, transformers and deploy models to production

198

Topics

Beginner

Intermediate

Advanced

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ML Basics (26)Math for ML (7)Algorithms (21)Deep Learning (23)Reinforcement Learning (12)From Scratch (4)Tools (12)NLP (11)MLOps (14)TensorFlow & Keras (10)Scikit-Learn (8)PyTorch (7) (3)Prompt Engineering (5)LLM Basics (8)LLM APIs (3)RAG (5)AI Agents (5)Agent Frameworks (4)Multi-Agent (3)Context Engineering (4)Observability (3)

ML Basics

Introduction to Machine Learning Beginner Supervised vs Unsupervised Learning Beginner ML Workflow — Data to Deployment Beginner Overfitting and Underfitting Intermediate Train Test Split and Cross Validation Intermediate Feature Engineering Basics Intermediate Data Preprocessing in ML Intermediate Bias and Variance Trade-off Intermediate Regularisation in Machine Learning Intermediate Hyperparameter Tuning Advanced Confusion Matrix and Classification Metrics Intermediate Recommender Systems Basics Intermediate Machine Learning for Beginners: What It Is and How to Start Beginner Z-Score Formula: Standardization, Anomaly Detection and Statistics Intermediate Machine Learning Roadmap 2026 – From Complete Beginner to Job-Ready Beginner How to Set Up Your Machine Learning Environment in 2026 (Beginner Guide) Beginner Mathematics for Machine Learning – Explained Without Tears Beginner Supervised vs Unsupervised vs Reinforcement Learning – Simple Explanation Beginner Data Cleaning and Preprocessing for Absolute Beginners Beginner How to Visualize Machine Learning Results (Matplotlib & Seaborn) Beginner How to Choose the Right Algorithm as a Beginner Intermediate Understanding Loss Functions and Gradient Descent Visually Intermediate Your First Machine Learning Project – Complete Step-by-Step (2026) Beginner Common Machine Learning Mistakes Beginners Make (And How to Fix Them) Beginner From Machine Learning to LLMs – What Should You Learn Next? Intermediate Exploratory Data Analysis (EDA) Beginner

Math for ML

Linear Algebra for Machine Learning Beginner Calculus for Machine Learning Intermediate Probability for Machine Learning Beginner Bayes' Theorem in Machine Learning Intermediate Eigenvalues and Eigenvectors Explained Intermediate Singular Value Decomposition (SVD) Advanced Hypothesis Testing for Data Science Intermediate

Algorithms

Linear Regression Intermediate Logistic Regression Intermediate Decision Trees Intermediate Random Forest Algorithm Explained Intermediate Support Vector Machine Advanced K-Nearest Neighbours Intermediate K-Means Clustering Intermediate Naive Bayes Classifier Intermediate Gradient Boosting and XGBoost Advanced Principal Component Analysis Advanced DBSCAN Clustering Advanced Dimensionality Reduction Techniques Advanced Ensemble Methods in ML Advanced Machine Learning Algorithms: Complete 2026 Guide Beginner Time Series Forecasting Intermediate Feature Selection: Filter, Wrapper, Embedded Intermediate Anomaly and Outlier Detection Intermediate Hidden Markov Models (HMM) Advanced Association Rule Mining with Apriori Intermediate t-SNE and UMAP for Visualization Intermediate Hierarchical Clustering Intermediate

Deep Learning

What is a Neural Network? Explained Simply Intermediate Activation Functions in Neural Networks Intermediate Backpropagation Explained Advanced Convolutional Neural Networks Advanced Recurrent Neural Networks and LSTM Advanced Transformers and Attention Mechanism Advanced Transfer Learning Advanced GANs — Generative Adversarial Networks Advanced Object Detection — YOLO Advanced Autoencoders Explained Advanced Attention is All You Need — Paper Advanced Batch Normalisation Advanced Dropout and Regularisation in NNs Advanced Reinforcement Learning Basics Advanced Diffusion Models Explained Advanced ResNet and Residual Connections Advanced U-Net Architecture for Segmentation Advanced Variational Autoencoders (VAE) Advanced Seq2Seq and Encoder-Decoder Models Intermediate Optimizers: SGD, Momentum, RMSprop, Adam Intermediate Positional Encoding in Transformers Advanced Knowledge Distillation Advanced Normalization: BatchNorm, LayerNorm, GroupNorm, RMSNorm Intermediate

Reinforcement Learning

Markov Decision Processes (MDPs) Intermediate Q-Learning Explained Intermediate Temporal Difference Learning (SARSA vs Q-Learning) Intermediate Deep Q-Networks (DQN) Advanced Policy Gradient Methods Advanced Actor-Critic Methods Advanced Proximal Policy Optimization (PPO) Advanced Multi-Armed Bandits Intermediate DDPG, TD3 and SAC for Continuous Control Advanced Trust Region Policy Optimization (TRPO) Advanced Monte Carlo Methods in Reinforcement Learning Intermediate Model-Based RL and the Dyna Architecture Advanced

From Scratch

Neural Network from Scratch in NumPy Intermediate Build an Autograd Engine from Scratch Advanced Build GPT from Scratch Advanced Build a BPE Tokenizer from Scratch Advanced

Tools

scikit-learn Tutorial Intermediate TensorFlow Basics Intermediate PyTorch Basics Intermediate Keras for Deep Learning Intermediate Jupyter Notebook Guide Beginner Hugging Face Transformers Advanced OpenCV Basics Intermediate LangChain for LLM Applications Advanced ONNX — Open Neural Network Exchange Advanced Best AI Tools for Developers in 2026 (Curated & Ranked) Intermediate My 2026 Developer Productivity Stack (Tools & Workflow) Intermediate Build a Simple Image Classifier Without Writing Much Code (Teachable Machine + Export to Next.js) Beginner

NLP

Natural Language Processing (NLP) Explained Intermediate Text Preprocessing in NLP Intermediate Word Embeddings — Word2Vec GloVe Advanced Sentiment Analysis Intermediate Named Entity Recognition Advanced Text Classification with ML Intermediate BERT and Transformer Fine-tuning Advanced Question Answering with Transformers Advanced Text Summarization: Extractive and Abstractive Intermediate Topic Modeling with LDA Intermediate Neural Machine Translation Intermediate

MLOps

Introduction to MLOps Advanced Model Deployment with Flask Advanced ML Model Evaluation Metrics Intermediate A/B Testing in ML Advanced Docker for ML Models Advanced Feature Stores Explained Advanced Experiment Tracking with MLflow Advanced Model Monitoring and Drift Detection Advanced How to Deploy Your First ML Model with Flask or FastAPI (Beginner) Beginner CI/CD for Machine Learning Intermediate ML Pipeline Orchestration with Airflow and Prefect Intermediate Distributed Training: Data and Model Parallelism Advanced Testing Machine Learning Systems Intermediate Data and Model Versioning with DVC Intermediate

TensorFlow & Keras

Introduction to TensorFlow Beginner TensorFlow vs PyTorch — Which to Learn First Beginner Introduction to Keras Beginner Building Your First Neural Network with Keras Beginner Keras Sequential vs Functional API Intermediate Image Classification with TensorFlow and Keras Intermediate Keras Callbacks — ModelCheckpoint and EarlyStopping Intermediate Transfer Learning with TensorFlow Intermediate Saving and Loading Models in TensorFlow Intermediate TensorFlow Lite for Mobile Deployment Advanced

Scikit-Learn

Introduction to Scikit-Learn Beginner Scikit-Learn Pipeline Explained Beginner Train Test Split and Cross Validation in Scikit-Learn Beginner Linear Regression with Scikit-Learn Beginner Classification with Scikit-Learn Intermediate Feature Engineering and Preprocessing in Scikit-Learn Intermediate Hyperparameter Tuning with GridSearchCV Intermediate Clustering with K-Means in Scikit-Learn Intermediate

PyTorch

Introduction to PyTorch Beginner PyTorch Tensors Explained Beginner Building a Neural Network in PyTorch Intermediate Autograd and Backpropagation in PyTorch Intermediate Training Loop in PyTorch Explained Intermediate PyTorch DataLoader and Datasets Intermediate CNN Image Classification with PyTorch Advanced

Introduction to Neural Networks Intermediate Introduction to NLP Intermediate Random Forest Intermediate

Prompt Engineering

Prompt Engineering Beginner Chain of Thought Prompting Intermediate Few-Shot vs Zero-Shot Prompting Beginner Role-Based System Prompts for LLMs Beginner Prompt Templates and Best Practices Intermediate

LLM Basics

LLM Context Window Explained Beginner Mixture of Experts (MoE) in LLMs Advanced LLM Tokenization Explained Beginner RLHF — Reinforcement Learning from Human Feedback Advanced LLM Fine-Tuning Guide Advanced LLM Quantization: GPTQ, AWQ and GGUF Advanced Multimodal LLMs and Vision-Language Models Advanced LoRA and PEFT Fine-Tuning Advanced

LLM APIs

OpenAI API Python Guide Beginner LLM Function Calling Explained Intermediate Structured Outputs with LLMs Intermediate

RAG

Vector Databases Explained Beginner RAG Pipeline Explained Intermediate RAG Chunking Strategies Intermediate Embeddings and Semantic Search Intermediate Advanced RAG Techniques Advanced

AI Agents

AI Agents Explained Beginner ReAct Agent Pattern Intermediate Agent Memory Types Intermediate Tool Use in AI Agents Intermediate Model Context Protocol (MCP) Explained Intermediate

Agent Frameworks

LangGraph Tutorial Intermediate CrewAI Multi-Agent Tutorial Intermediate Agent Orchestration Patterns Advanced Agentic Planning Strategies Advanced

Multi-Agent

Multi-Agent Systems Explained Intermediate Agent Communication Patterns Advanced A2A Protocol for AI Agents Advanced

Context Engineering

Context Engineering for LLMs Intermediate Token Budgeting for LLMs Intermediate LLM Memory Management Intermediate Context Compression Techniques Advanced

Observability

LLM Observability Tools Intermediate LLM Evaluation Frameworks Intermediate LLM Latency Optimization Advanced

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Every tutorial starts with a plain-English analogy — then real code, then interview questions.

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