DEVELOPER & DATA SCIENCE • TRACK 3
Master core machine learning algorithms, deep learning neural networks, and Python data science libraries. 100% free verified access.
STANFORD & DEEPLEARNING.AI • FREE AUDIT
⏱️ 3 Months (Self-Paced) • 🐍 Python & NumPy • 🏆 Stanford Online
The definitive foundational ML curriculum rebuilt by Andrew Ng. Covers regression, classification, neural networks, decision trees, clustering, and recommender systems with Python & NumPy.
Audit Free on Coursera ↗HARVARD CS50 • 100% FREE AUDIT
⏱️ 7 Weeks • 🎓 Harvard University • 🐍 Python AI Projects
Explore foundational AI concepts and algorithms: A* search, minimax, propositional logic, Bayesian networks, machine learning, and transformer architectures using Python.
Access Free on Harvard CS50 ↗FAST.AI • 100% OPEN & FREE
⏱️ 8-10 Weeks • 🔥 PyTorch & fastai • 💻 Free Cloud GPUs
Jeremy Howard’s celebrated top-down curriculum. Train state-of-the-art computer vision and NLP models on free GPUs (Kaggle/Colab) using PyTorch with practical coding from Day 1.
Start Free on fast.ai ↗KAGGLE • FREE BADGES & CERTIFICATES
⏱️ 3-4 Hours Each • 📜 Free Badges • ⚡ Kaggle Notebooks
Hands-on 3-hour micro-courses with interactive browser notebooks: Intro to Machine Learning, Intermediate ML, Feature Engineering, Deep Learning with TensorFlow, and Computer Vision.
Start Free on Kaggle ↗Clean syntax, virtual environments, list comprehensions, object-oriented concepts, and package managers (pip/conda).
NumPy vectorization, Pandas DataFrames, and visualization libraries (Matplotlib, Seaborn) for exploratory analysis.
Scikit-Learn, PyTorch, and Hugging Face Transformers for training and fine-tuning neural network models.
Google Colab and Kaggle Notebooks provide free access to NVIDIA T4 / P100 GPUs for deep learning training.
Follow our structured week-by-week learning blueprint to go from Python basics to deploying production models.
View 6-Month ML Engineer Roadmap →