Complete Guide

Complete ML and AI Tutorial

Machine learning is reshaping every industry. This guide covers all 90 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

Topics

Beginner

Intermediate

Advanced

Jump to section

ML Basics (12)Algorithms (13)Deep Learning (15)Tools (9)NLP (8)MLOps (8)TensorFlow & Keras (10)Scikit-Learn (8)PyTorch (7)

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

Algorithms

Linear Regression Intermediate Logistic Regression Intermediate Decision Trees Intermediate Random Forest 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

Deep Learning

Introduction to Neural Networks 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

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

NLP

Introduction to NLP 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

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

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

Start from the beginning

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

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