Machine Learning Learning Path 🤖

A comprehensive guide to learning Machine Learning from fundamentals to advanced concepts. Track your progress by checking off completed items.

Prerequisites

• Python programming basics
• Linear algebra fundamentals
• Basic statistics and probability
• Calculus basics (derivatives, gradients)
• Basic command line usage
• Git version control

1. Foundations 📚

Mathematics and Statistics

• Linear algebra
  • Matrices, vectors, eigenvalues/eigenvectors
  • Matrix operations and transformations
  • Principal Component Analysis (PCA) mathematics
• Statistics
  • Probability distributions
  • Hypothesis testing
  • Confidence intervals
  • Bayesian statistics
• Calculus
  • Derivatives and partial derivatives
  • Chain rule
  • Gradient descent optimization
  • Backpropagation mathematics

Python for Data Science

• NumPy
  • Array operations
  • Broadcasting
  • Linear algebra operations
• Pandas
  • Data manipulation
  • Data cleaning
  • Feature engineering
• Data visualization
  • Matplotlib
  • Seaborn
  • Plotly

2. Machine Learning Basics 🌱

Supervised Learning

• Linear Regression
  • Simple and multiple regression
  • Polynomial regression
  • Regularization (L1, L2)
• Classification
  • Logistic regression
  • Decision trees
  • Random forests
  • Support Vector Machines (SVM)
• Model Evaluation
  • Cross-validation
  • Metrics (accuracy, precision, recall, F1)
  • ROC curves and AUC
  • Confusion matrices

Unsupervised Learning

• Clustering
  • K-means
  • Hierarchical clustering
  • DBSCAN
• Dimensionality Reduction
  • PCA
  • t-SNE
  • UMAP

3. Intermediate Concepts 🚀

Feature Engineering and Selection

• Feature scaling and normalization
• Handling missing data
• Feature selection methods
• Automated feature engineering

Model Optimization

• Hyperparameter tuning
  • Grid search
  • Random search
  • Bayesian optimization
• Ensemble methods
  • Bagging
  • Boosting (AdaBoost, XGBoost, LightGBM)
  • Stacking

4. Deep Learning 🧠

Neural Networks

• Feed-forward neural networks
• Activation functions
• Loss functions
• Optimization algorithms

Deep Learning Architectures

• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Long Short-Term Memory (LSTM)
• Transformers

Deep Learning Frameworks

• PyTorch
• TensorFlow/Keras
• Model deployment basics

5. Advanced Topics 🎯

Advanced Machine Learning

• Reinforcement Learning
• GANs (Generative Adversarial Networks)
• Transfer Learning
• Meta-Learning

MLOps and Production

• Model versioning
• Model serving
• Monitoring and maintenance
• A/B testing

Specialized Applications

• Natural Language Processing
• Computer Vision
• Time Series Analysis
• Recommendation Systems

6. Projects and Practice 💪

• Build an end-to-end ML pipeline
• Participate in Kaggle competitions
• Create a portfolio project
• Contribute to open-source ML projects

Learning Resources 📖

Books

• "Introduction to Statistical Learning"
• "Deep Learning" by Goodfellow, Bengio, and Courville
• "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow"

Online Courses

• Fast.ai Practical Deep Learning
• Stanford CS229 Machine Learning
• Coursera Machine Learning Specialization
• deeplearning.ai specializations

Platforms

• Kaggle
• Google Colab
• GitHub