ML Cookbook
A complete hands-on guide to machine learning, from fundamentals to advanced topics
Learning Roadmap
Week 1-2: ML Fundamentals
├── 01. ML Pipeline → Data splitting, preprocessing, evaluation basics
├── 02. Linear Regression → Mathematical foundations of regression
└── 03. Logistic Regression → Classification and evaluation metrics
Week 3-4: Trees & Ensemble
├── 04. Decision Tree → Tree-based model principles
├── 05. Ensemble → Random Forest, XGBoost
└── 06. Feature Engineering → Feature creation and selection
Week 5-6: Unsupervised Learning
├── 07. Clustering → K-Means clustering
├── 08. Dimensionality → PCA, t-SNE, UMAP
└── 09. Anomaly Detection → Outlier detection
Week 7-8: Advanced Topics
├── 10. Imbalanced Data → Handling imbalanced datasets
├── 11. Time Series → Time series forecasting
├── 12. Neural Network → Deep learning basics
├── 13. CNN → Image classification
└── 14. NLP → Text analysisQuick Start
Features
- 14 Core Topics - From ML basics to deep learning
- Hands-on Practice - Code directly with Jupyter notebooks
- Interview Prep - Interview questions for each topic
- Premium Content - Practice problems, solutions, advanced interview materials
Required Libraries
pip install numpy pandas matplotlib seaborn scikit-learn
pip install xgboost lightgbm # Ensemble
pip install statsmodels # Time Series
pip install tensorflow # Deep Learning (optional)Author
SOTAAZ (opens in a new tab) - AI & Data Engineering Tutorials