Machine Learning Roadmap
A structured path to master machine learning concepts and applications.
Phase 1: Prerequisites
Mathematics
- Linear algebra (vectors, matrices)
- Calculus (derivatives, gradients)
- Probability & statistics
- Optimization basics
Programming
- Python fundamentals
- NumPy and Pandas
- Data visualization (Matplotlib, Seaborn)
- Jupyter notebooks
Phase 2: Core ML Concepts
Supervised Learning
- Linear regression
- Logistic regression
- Decision trees
- Random forests
- Support vector machines
Model Evaluation
- Train/test split
- Cross-validation
- Metrics (accuracy, precision, recall, F1)
- Confusion matrix
- ROC curves
Feature Engineering
- Feature scaling
- Feature selection
- Dimensionality reduction
- PCA and t-SNE
Phase 3: Advanced Topics
Unsupervised Learning
- K-means clustering
- Hierarchical clustering
- DBSCAN
- Gaussian mixture models
Ensemble Methods
- Bagging
- Boosting
- Gradient boosting
- XGBoost, LightGBM
Neural Networks
- Perceptrons
- Feedforward networks
- Backpropagation
- Activation functions
Phase 4: Practical ML
Frameworks
- Scikit-learn
- TensorFlow
- PyTorch
- Keras
MLOps
- Model versioning
- Model deployment
- Monitoring and maintenance
- A/B testing
Resources
- Courses: Andrew Ng ML Course, Fast dot ai
- Books: Hands-On Machine Learning by Aurélien Géron
- Practice: Kaggle competitions
Timeline
Estimated completion: 4-8 months with hands-on projects