ML Study Roadmap

🔹 Phase 1: Build Core Foundations

Math Refresher (Just Enough!)
- Linear Algebra: Vectors, matrices, dot products (used in neural networks).
- Probability & Statistics: Mean, variance, distributions, Bayes’ theorem.
- Calculus: Derivatives, gradients (for understanding optimization).
- Tip: Don’t dive deep—focus on intuition. Use resources like Khan Academy or 3Blue1Brown’s “Essence of Linear Algebra” .
Python for ML
- You likely know Python, but ensure you’re comfortable with:
  - NumPy (arrays, vectorization)
  - Pandas (data manipulation)
  - Matplotlib/Seaborn (visualization)
- Practice: Clean and explore a dataset (e.g., Titanic ).

🔹 Phase 2: Learn ML Fundamentals

Core Concepts
- Supervised vs. unsupervised learning
- Overfitting/underfitting, bias-variance tradeoff
- Train/validation/test splits, cross-validation
- Evaluation metrics (accuracy, precision, recall, F1, MSE, etc.)
Key Algorithms (Start Simple!)
- Linear/Logistic Regression
- Decision Trees & Random Forests
- k-Nearest Neighbors (k-NN)
- k-Means Clustering
- Avoid jumping into deep learning too early!
Hands-On Practice
- Use scikit-learn (your best friend for classical ML).
- Work through beginner Kaggle competitions (e.g., House Prices ).
- Goal: Build 3–5 end-to-end projects (data → model → evaluation).

🔹 Phase 3: Dive into Deep Learning (Optional but Recommended)

Neural Networks Basics
- Perceptrons, activation functions, backpropagation
- Frameworks: TensorFlow or PyTorch (PyTorch is more Pythonic; TensorFlow has better production tooling).

Key Architectures
- CNNs (for images)
- RNNs/LSTMs (for sequences)
- Transformers (modern NLP)
Practice
- Try TensorFlow Tutorials or PyTorch Tutorials .
- Build a CNN for MNIST or CIFAR-10.

🔹 Phase 4: Productionize & Integrate (Leverage Your Full-Stack Skills!)

ML Engineering
- Model deployment (Flask/FastAPI + Docker)
- APIs for ML models (e.g., REST endpoint for predictions)
- Monitoring, logging, versioning (MLflow, Weights & Biases)

Cloud Platforms
- Deploy models on AWS SageMaker, GCP AI Platform, or Azure ML.
- Use serverless (e.g., AWS Lambda) for lightweight inference.
MLOps Basics
- CI/CD for ML pipelines
- Data/model versioning (DVC, Git LFS)

🔹 Recommended Resources

Courses:
- Andrew Ng’s ML Course (Coursera) (theory-focused)
- fast.ai (practical, top-down approach)
- Google’s ML Crash Course (free & hands-on)
Books:
- Hands-On Machine Learning (Aurélien Géron) – Best for coders!
- Python Machine Learning (Sebastian Raschka)
Practice:
- Kaggle
- Hugging Face (for NLP)

🔹 Your Full-Stack Superpower

You can build full ML-powered apps (e.g., a React frontend + Flask API + trained model).
Focus on end-to-end projects:
- Example: "Build a web app that predicts house prices using user inputs."
- This makes your portfolio stand out!