Machine Learning Dojo

📚 Core Introductory Series (6 Series)

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Machine Learning Introduction (ML-B01)

First Steps in Machine Learning - Starting with Python, NumPy, and Pandas Fundamentals

Beginner 80-100 min 4 Chapters, 40 Examples

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Supervised Learning Introduction (ML-B02)

Complete Guide to Regression, Classification, and Ensemble Methods

Beginner-Intermediate 80-100 min 4 Chapters, 57 Examples

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Unsupervised Learning Introduction (ML-B03)

Fundamentals of Clustering, Dimensionality Reduction, and Anomaly Detection

Beginner-Intermediate 70-90 min 4 Chapters, 37 Examples

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Neural Networks Introduction (ML-B04)

From Perceptron to CNN Image Classification - PyTorch Practical Guide

Beginner-Intermediate 120-140 min 5 Chapters, 60 Examples

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Feature Engineering Introduction (ML-B05)

Practical Techniques in Data Preprocessing and Feature Design

Intermediate 85-105 min 4 Chapters, 46 Examples

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Model Evaluation Introduction (ML-B06)

Cross-Validation, Evaluation Metrics, and Hyperparameter Tuning

Intermediate 90-110 min 4 Chapters, 52 Examples

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Statistics for Machine Learning Introduction (ML-B07)

Descriptive Statistics, Probability Distributions, and Bayesian Statistics Fundamentals

Beginner 120-150 min 5 Chapters

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Deep Learning Fundamentals Introduction (ML-B08)

Neural Networks, Activation Functions, and Learning Algorithms

Beginner-Intermediate 150-180 min 5 Chapters

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🤖 LLM & Generative AI Series (4 Series)

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LLM Basics Introduction (ML-L01)

Large Language Model Architecture, Transformer, and Tokenization Fundamentals

Beginner-Intermediate 120-150 min 5 Chapters

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Prompt Engineering Introduction (ML-L02)

Prompt Design, Few-shot Learning, and Chain-of-Thought Techniques

Beginner 90-120 min 5 Chapters

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PyTorch Basics Introduction (ML-L03)

Tensor Operations, Automatic Differentiation, and Neural Network Construction

Beginner 100-120 min 5 Chapters

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PyTorch Geometric Introduction (ML-L04)

Graph Neural Networks, GCN, and Molecular Structure Analysis

Intermediate 120-150 min 4 Chapters

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🚀 Deep Learning Advanced Series (6 Series)

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Convolutional Neural Networks (CNN) Introduction (ML-A01)

From Image Recognition Fundamentals to Transfer Learning and Object Detection

Intermediate 115-140 min 5 Chapters, 48 Examples

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Recurrent Neural Networks (RNN) Introduction (ML-A02)

Practical Time Series Data and Sequence Processing

Intermediate 110-135 min 5 Chapters, 38 Examples

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Transformer Introduction (ML-A03)

From Attention Mechanism to Large Language Models

Intermediate-Advanced 140-170 min 5 Chapters, 46 Examples

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Generative Models Introduction (ML-A04)

Theory and Implementation of VAE, GAN, and Diffusion Models

Advanced 135-165 min 5 Chapters, 42 Examples

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Graph Neural Networks (GNN) Introduction (ML-A05)

Representation Learning for Graph-Structured Data and Applications

Advanced 135-165 min 5 Chapters, 46 Examples

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Reinforcement Learning Introduction (ML-A06)

Implementation Guide from Q-Learning to DQN and PPO

Advanced 130-160 min 5 Chapters, 37 Examples

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⚙️ Practical MLOps Series (4 Series)

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MLOps Introduction (ML-C01)

Workflow from Model Development to Production Deployment

Intermediate 100-120 min 5 Chapters, 55 Examples

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Model Interpretability Introduction (ML-C02)

Black Box Analysis with SHAP, LIME, and Grad-CAM

Intermediate 90-110 min 4 Chapters, 30 Examples

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Large-Scale Data Processing Introduction (ML-C03)

Practical Use of Spark, Dask, and Distributed Learning

Intermediate-Advanced 110-130 min 5 Chapters, 37 Examples

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AutoML Introduction (ML-C04)

Streamlining Model Construction with Automated Machine Learning

Intermediate 90-110 min 4 Chapters, 25 Examples

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🎯 Application Domain Series (9 Series)

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Natural Language Processing (NLP) Introduction (ML-D01)

Practical from Text Processing to BERT and GPT

Intermediate 120-150 min 5 Chapters, 40 Examples

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📗

Computer Vision Introduction (ML-D02)

From Image Processing to Segmentation and Object Detection

Intermediate 120-150 min 5 Chapters, 40 Examples

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Speech Processing and Speech Recognition Introduction (ML-D03)

Speech AI from Acoustic Features to Whisper

Intermediate 90-110 min 5 Chapters, 30 Examples

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Time Series Analysis Introduction (ML-D04)

Time Series Forecasting with ARIMA, LSTM, and Transformer

Intermediate 90-110 min 5 Chapters, 35 Examples

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Recommendation Systems Introduction (ML-D05)

From Collaborative Filtering to Deep Learning Recommendations

Intermediate 90-110 min 4 Chapters, 30 Examples

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Anomaly Detection Introduction (ML-D06)

Implementation from Statistical Methods to AutoEncoder

Intermediate 90-110 min 4 Chapters, 30 Examples

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Network Analysis Introduction (ML-D07)

From Graph Theory to Practical Network Analysis

Intermediate 100-120 min 5 Chapters, 40 Examples

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AI Agents Introduction (ML-D08)

ReAct, Function Calling, and Multi-Agent Systems

Intermediate-Advanced 120-150 min 4 Chapters, 23 Examples

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RAG Introduction (ML-D09)

Vector DB, Embeddings, and Retrieval Augmented Generation

Intermediate-Advanced 120-150 min 4 Chapters, 23 Examples

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🛠️ Practical Techniques Series (5 Series)

📙

Ensemble Learning Practical (ML-P01)

From Bagging and Boosting to Latest Methods

Intermediate 90-110 min 4 Chapters, 30 Examples

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Hyperparameter Tuning (ML-P02)

Grid Search, Bayesian Optimization, and AutoML

Intermediate 60-80 min 4 Chapters, 26 Examples

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📙

Model Deployment (ML-P03)

REST API, Docker, and Cloud Deployment

Intermediate 80-100 min 4 Chapters, 32 Examples

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📙

Meta-Learning (ML-P04)

MAML, Few-Shot Learning, and Transfer Learning

Advanced 80-100 min 4 Chapters, 25 Examples

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📙

Mathematics of Machine Learning (ML-P05)

Probability Statistics, Linear Algebra, and Optimization Theory

Advanced 150-180 min 5 Chapters, 30 Examples

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