Machine Learning Roadmap

A comprehensive path to becoming a Machine Learning engineer. From fundamentals to deploying production models.

Overview

This roadmap covers everything you need to become proficient in Machine Learning and Deep Learning.

Phase 1: Mathematics Foundation (4-6 weeks)

Linear Algebra

• Vectors and matrices
• Matrix operations
• Eigenvalues and eigenvectors
• Applications in ML

Calculus

• Derivatives and gradients
• Chain rule
• Partial derivatives
• Optimization

Probability & Statistics

• Probability distributions
• Bayes theorem
• Statistical inference
• Hypothesis testing

Resources:

• 📚 Khan Academy - Linear Algebra & Calculus
• 📘 "Mathematics for Machine Learning" (Free PDF)
• 🎥 3Blue1Brown - Essence of Linear Algebra

Phase 2: Python for ML (3-4 weeks)

NumPy & Pandas

• Array operations
• Data manipulation
• Statistical functions

Data Visualization

• Matplotlib
• Seaborn
• Plotly

Practice: Kaggle datasets exploration

Phase 3: Machine Learning Fundamentals (8-10 weeks)

Supervised Learning

• Linear/Logistic Regression
• Decision Trees
• Random Forests
• Support Vector Machines
• Gradient Boosting (XGBoost, LightGBM)

Unsupervised Learning

• K-Means Clustering
• Hierarchical Clustering
• PCA
• t-SNE

Model Evaluation

• Cross-validation
• Metrics (Accuracy, Precision, Recall, F1, AUC-ROC)
• Confusion Matrix
• Overfitting/Underfitting

Resources:

• 📚 Andrew Ng's Machine Learning Course (Coursera)
• 💻 Scikit-learn Documentation
• 🏆 Practice: Kaggle Competitions

Phase 4: Deep Learning (10-12 weeks)

Neural Networks Basics

• Perceptrons
• Activation functions
• Backpropagation
• Optimization algorithms

Frameworks

• TensorFlow
• PyTorch
• Keras

Architectures

• CNNs (Computer Vision)
• RNNs, LSTMs (Sequential Data)
• Transformers (NLP)
• GANs (Generative Models)

Projects:

• Image classification
• Object detection
• Text generation
• Sentiment analysis

Phase 5: Advanced Topics (Ongoing)

MLOps

• Model deployment
• Docker & Kubernetes
• Monitoring & Logging
• CI/CD for ML

Production ML

• Feature engineering at scale
• Model versioning
• A/B testing
• Model serving

Resources:

• 📚 Fast.ai Courses
• 💻 Papers with Code
• 🎥 Stanford CS229

Recommended Learning Path

1. Months 1-2: Mathematics + Python
2. Months 3-5: ML Fundamentals + Projects
3. Months 6-8: Deep Learning + Framework mastery
4. Months 9-12: Advanced topics + Capstone project

Community Support

• Join our ML study group
• Weekly paper reading sessions
• Project showcases
• Kaggle team competitions

Start your ML journey today! 🚀