Here’s a concise, practical list of the best resources and how to use them to practice and build real machine‑learning skills — organized so you can pick what fits your level and goals.

Foundational learning (theory + practice)

• Coursera — Andrew Ng’s “Machine Learning” (Stanford) and “Deep Learning Specialization.” Great introductions and practical exercises with Octave/Python.
• fast.ai — Practical deep learning courses (free). Focus on building models quickly and understanding end‑to‑end workflows.
• MIT OpenCourseWare — “Introduction to Deep Learning” and “6.034 Artificial Intelligence” for rigorous lecture material.
• Stanford CS229 / CS231n lecture notes and assignments — theory and practical CV exercises (excellent for deeper understanding).

Books (shortlist to read and apply)

• Hands-On: “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” (Aurélien Géron) — projects and code-first learning.
• Theory: “Pattern Recognition and Machine Learning” (Bishop) — strong probabilistic view.
• Deep Learning: “Deep Learning” (Goodfellow, Bengio, Courville) — comprehensive reference.
• Practical ML engineering: “Machine Learning Engineering” (Andriy Burkov or others) and “Designing Data-Intensive Applications” for production topics.

Interactive / coding practice platforms

• Kaggle — datasets + kernels + competitions. Start with Titanic and regression/classification playgrounds, then progress to timed competitions and notebooks.
• Google Colab — free GPUs; great for experimenting and sharing notebooks.
• DrivenData — competitions focused on social impact problems.
• AICrowd — competitions with diverse tasks (RL, CV, NLP).
• LeetCode (ML tag) and HackerRank — smaller algorithmic problems for data-processing and ML system design.

Project & dataset sources (build a portfolio)

• UCI Machine Learning Repository — classic datasets for experimentation.
• Kaggle Datasets — wide selection and real-world examples.
• Hugging Face Datasets — standardized datasets for NLP and multimodal work.
• Open Images / COCO / Pascal VOC — for object detection / segmentation projects.
• Common Crawl — for large-scale web/NLP practice. Project ideas: end-to-end projects (data ingestion → cleaning → modeling → deployment): churn prediction, recommendation system, object detector, NLP summarizer, time-series forecasting pipeline.

Structured practice & curriculum paths

• DataCamp / Codecademy (beginner hands-on labs) — short interactive modules.
• nanodegrees (Udacity) — project-focused pathways (ML Engineer, Deep Learning).
• Create 4 portfolio projects: one classical ML (tabular), one CV, one NLP, one productionized model with deployment (API + monitoring).

Competitions & challenges

• Start with Kaggle Playgrounds/Titanic → move to Novice/Getting Started → Progress to Code competitions.
• Use competitions to practice feature engineering, cross-validation, ensembling, and model interpretability.
• Timebox learning: spend many short iterations improving baseline models vs. chasing marginal gains.

Practice tasks to sharpen skills

• Feature engineering exercises: create derived features, handle missing data, categorical encoding.
• Cross-validation & leakage: build robust CV strategies, practice time-series splits.
• Model selection & tuning: grid/random search, Bayesian optimization (Optuna).
• Interpretability: SHAP, LIME, partial dependence plots.
• Productionization: dockerize models, create REST API (FastAPI), basic CI/CD, model monitoring (drift detection).

Tools & libraries to master

• Python: NumPy, pandas, scikit-learn.
• Deep learning: TensorFlow/Keras and PyTorch (PyTorch preferred for research/fast prototyping).
• NLP: Hugging Face Transformers, spaCy.
• CV: OpenCV, torchvision, Detectron2 (advanced).
• ML Ops: MLflow, Kubeflow, DVC, Docker, FastAPI, AWS/GCP/Azure basics.
• Visualization: Matplotlib, Seaborn, Plotly, TensorBoard.

Reading & staying current

• arXiv Sanity / weekly arXiv alerts — follow important papers.
• Blogs: Distill, Two Minute Papers, Sebastian Ruder, Lil’Log.
• Newsletters: The Batch (Andrew Ng), Import AI, Machine Learning Weekly.
• Twitter/X / LinkedIn — follow researchers and practitioners (use selectively).

Learning plan / cadence (example 3‑month roadmap)

• Weeks 1–2: fundamentals — basic statistics, linear regression, logistic regression, Python/pandas practice.
• Weeks 3–6: supervised learning, tree models, scikit-learn projects, feature engineering.
• Weeks 7–10: deep learning basics, build CNN and Transformer toy projects (Colab).
• Weeks 11–12: enter a Kaggle competition and deploy one project as an API.
• Ongoing: read one paper/month, refactor a project for production, contribute to open source.

Assessment & portfolio tips

• Document each project: problem statement, data pipeline, baseline, experiments, evaluation metrics, results, lessons learned.
• Use GitHub with notebooks, clean README, and optionally a short demo video or hosted demo.
• Show end-to-end skills: data collection, modeling, evaluation, and deployment + monitoring.

Communities & mentorship

• Kaggle forums, Stack Overflow, Reddit (r/MachineLearning, r/learnmachinelearning), CrossValidated (StackExchange).
• Local meetups, conferences (workshops), or university open seminars.
• Peer code reviews or mentorship (pair projects) to accelerate progress.

If you want, I can:

• recommend a 12‑week personalized weekly plan based on your current experience level (beginner / intermediate / advanced),
• suggest 3 concrete starter projects with dataset links and evaluation criteria,
• or produce a checklist for deploying a model to production.

Which of those would you like next?