Machine Learning — Roadmap (Beginner → Expert)
A practical learning path: what to learn first, which libraries to master, project ideas, and how to progress from fundamentals to production-ready ML systems.
Start Here — Foundations
Learn Python basics (syntax, functions, OOP), linear algebra (vectors, matrices), probability & statistics, and calculus basics. These are the foundations that make ML understandable.
Core ML Concepts
Understand supervised vs unsupervised learning, overfitting/underfitting, bias-variance tradeoff, cross-validation, evaluation metrics (accuracy, precision, recall, F1, ROC-AUC), and feature engineering.
Practical Tools & Environment
Get comfortable with NumPy, pandas, Matplotlib/Seaborn for EDA, Jupyter notebooks, and version control with Git. Learn how to prepare datasets and run experiments reproducibly.
Intermediate — Models & Libraries
Learn scikit-learn thoroughly (regression, classification, clustering, pipelines), then move to deep learning with TensorFlow/Keras or PyTorch. Study regularization, hyperparameter tuning, and model selection.
Advanced Topics
Dive into CNNs, RNNs/Transformers, sequence models, generative models, reinforcement learning, probabilistic models, and scalable ML (distributed training). Learn model interpretability and fairness.
Deployment & Production
Learn model serving (FastAPI, TorchServe, TensorFlow Serving), containerization (Docker), CI/CD, monitoring, and MLOps tools (MLflow, DVC). Understand latency, throughput, and cost trade-offs.
Libraries & Tools to Master
NumPy
Numerical computing and linear algebra foundations.
pandas
Data manipulation and preprocessing for tabular data.
Matplotlib / Seaborn
Visualization for EDA and model diagnostics.
scikit-learn
Classic ML algorithms, pipelines, and model evaluation.
TensorFlow / Keras
High-level deep learning framework for production and research.
PyTorch
Flexible deep learning library used widely in research and production.
XGBoost / LightGBM / CatBoost
Gradient boosting libraries for tabular data competitions and production.
Hugging Face
Transformers and NLP tooling for state-of-the-art language models.
Beginner Projects
House price prediction, Titanic survival classifier, basic image classifier (MNIST), and EDA notebooks—focus on end-to-end workflow.
Intermediate Projects
Building CNNs for custom datasets, text classification with transformers, time-series forecasting, and model tuning with cross-validation.
Advanced Projects
Deploying models as APIs, scaling training on GPUs, building recommender systems, productionizing pipelines, and working with large language models.
Practical Tips
Read papers, reproduce tutorials, join competitions (Kaggle), maintain a project portfolio, write clean experiments, and practice mathematical intuition with small coding exercises.
Recommended Path
1) Python & Math → 2) EDA & scikit-learn → 3) Deep learning basics → 4) Specialize (NLP, CV, RL) → 5) Production & MLOps.