Here’s a compact, practical set of top resources to learn AI hands‑on — organized so you can pick a path and start building immediately.

Foundational interactive courses

• Andrew Ng’s Machine Learning (Coursera) — classical ML algorithms with Octave/Python exercises. Great first course.
• DeepLearning.AI TensorFlow Developer / Deep Learning Specialization (Coursera) — practical neural‑net building with exercises and projects.
• fast.ai — “Practical Deep Learning for Coders” (course + notebooks). Very project focused; you’ll train state‑of‑the‑art models quickly.
• Stanford CS231n (Convolutional Neural Nets for Visual Recognition) and CS224n (NLP) — lecture videos + assignments (good for deeper theory + code).

Interactive platforms & hands‑on sandboxes

• Kaggle Learn + Kaggle Notebooks — guided micro‑courses and free GPU notebooks; tons of public notebooks to fork and run.
• Google Colab — free GPU/TPU notebooks; ideal for experiments and following tutorials.
• Hugging Face Courses + Hugging Face Spaces — learn transformers and deploy demo apps in Gradio or Streamlit; model hub with ready models.
• Microsoft Learn / Azure Notebooks or AWS SageMaker Studio Lab — cloud notebooks for larger experiments.

Practical books & free textbooks

• Hands-On Machine Learning with Scikit‑Learn, Keras, and TensorFlow (Aurélien Géron) — very practical, notebook‑based.
• Dive into Deep Learning (Aston Zhang et al.) — interactive Jupyter/MXNet/PyTorch examples; freely available.
• Deep Learning (Goodfellow, Bengio, Courville) — definitive theory reference (less hands‑on code).

Core libraries, tooling & tutorials

• PyTorch tutorials (official) — includes beginner → advanced hands‑on notebooks.
• TensorFlow/Keras tutorials — production and research workflows.
• scikit‑learn — essential for classical ML tasks and pipelines.
• Hugging Face Transformers & Datasets — for modern NLP and multimodal models.
• Weights & Biases, TensorBoard — experiment tracking and visualization.
• Docker + FastAPI / Streamlit / Gradio — for packaging and deploying small demo apps.

Datasets & benchmarks

• Kaggle Datasets — many real datasets with notebooks and kernels.
• Hugging Face Datasets — ready‑to‑use, streaming datasets for ML.
• UCI Machine Learning Repository — classic tabular datasets.
• OpenML and Papers With Code — link papers to code and datasets.

Project ideas to practice (increasing complexity)

• Binary/multi‑class classification with scikit‑learn (Titanic, tabular data).
• Image classifier with transfer learning (ResNet, MobileNet) on CIFAR or your own images.
• Text classification / sentiment analysis with transformers (Hugging Face).
• Build an end‑to‑end app: train → serve with FastAPI → front end with Streamlit/Gradio.
• Simple RL agents with OpenAI Gym / Stable Baselines3.
• Fine‑tune an LLM for a retrieval‑augmented generation (RAG) demo.

Competitions & community learning

• Kaggle competitions — force you to iterate and learn evaluation, feature engineering, ensembling.
• DrivenData — socially impactful data challenges.
• GitHub — follow well‑maintained repos and reproduce the code.
• Discord/Reddit (r/MachineLearning, r/learnmachinelearning), Hugging Face forums — ask for help, find study partners.

MLOps & production skills

• Basics: model serialization (ONNX), containerization (Docker), CI/CD, monitoring.
• Tools to learn: MLflow, TFX, BentoML, Seldon, Prometheus (for monitoring).

Learning path (suggested 3‑6 months if self‑paced)

• Month 0–1: Python + linear algebra basics + Coursera Andrew Ng / scikit‑learn projects.
• Month 1–3: Deep learning fundamentals (fast.ai or DeepLearning.AI) + hands‑on PyTorch/TensorFlow projects.
• Month 3–6: Specialized projects (NLP/vision/RL), deploy a demo app, participate in a Kaggle competition, learn MLOps basics.

Extra tips

• Always reproduce notebook results locally or in Colab; change hyperparameters and dataset splits to learn effects.
• Version control notebooks (nbdime, Jupytext) and start tracking experiments early (W&B or MLflow).
• Focus on a small set of projects and finish them — deployed demo + README is more valuable than many half‑done experiments.

If you’d like, I can: recommend a 6‑week weekly plan tailored to your background (beginner/intermediate/advanced), or list one‑click starter notebooks for a specific domain (vision, NLP, tabular). Which would you prefer?