# Best AI resources for ML foundations Canonical URL: https://learnetto.com/ai-guides/best-ai-resources-for-ml-foundations Markdown URL: https://learnetto.com/ai-guides/best-ai-resources-for-ml-foundations.md Last updated: 2026-06-23 Source: Learnetto AI learning directory ## Summary Build enough machine learning knowledge to understand modern AI tools. Topics: ml foundations, machine learning, statistics, supervised learning ## Short answer - **Best beginner ML course:** Google Machine Learning Crash Course. Google's free course for practical ML concepts. Start here if you need the vocabulary behind modern AI systems. - **Best small exercises:** Kaggle Intro to Machine Learning. Kaggle micro-course with hands-on beginner ML tasks. Use it when you learn best by doing quick exercises. - **Best visual intuition:** 3Blue1Brown Neural Networks. Visual video series explaining neural networks. Use it when the math needs to become intuitive before you go deeper. ## Learn enough ML to understand the system ML foundations help you understand classification, regression, loss, neural networks, embeddings, training, validation, overfitting, and why models generalize imperfectly. You do not need a PhD, but you do need basic vocabulary. Google's Machine Learning Crash Course is the best structured beginner path. Kaggle is useful for small exercises. 3Blue1Brown and StatQuest help when visual intuition matters. ## Use foundations to ask better questions The goal is not to become a research scientist before using AI. The goal is to ask better questions about data quality, evaluation, model behavior, and failure modes. Once you can explain the basics, move back toward the applied area you care about: RAG, agents, product AI, local models, or production engineering. ## Recommended resources 1. [Google Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course) - Free course by Google for Developers; level: Beginner. You need practical ML vocabulary before deeper AI engineering. 2. [Kaggle Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning) - Micro-course by Kaggle; level: Beginner. You need small exercises for ML basics. 3. [StatQuest Neural Networks](https://statquest.org/video-index/) - Video lesson by Josh Starmer; level: Beginner. You want a plain-language explanation of neural-network basics. 4. [DeepLearning.AI Short Courses](https://www.deeplearning.ai/short-courses/) - Short courses by Andrew Ng; level: Beginner to advanced. Use this when you want Andrew Ng's material for prompting and related AI skills. 5. [StatQuest](https://statquest.org/) - YouTube videos by Josh Starmer; level: Beginner to intermediate. Use this when you want Josh Starmer's material for ml foundations and related AI skills. 6. [AI and Machine Learning for Coders](https://laurencemoroney.com/) - Books by Laurence Moroney; level: Beginner to intermediate. Use this when you want Laurence Moroney's material for tensorflow and related AI skills. 7. [Hands-On Machine Learning](https://www.oreilly.com/library/view/hands-on-machine-learning/9781098125967/) - Book by Aurelien Geron; level: Beginner to intermediate. Use this when you want Aurelien Geron's material for scikit-learn and related AI skills. 8. [Serrano Academy](https://www.youtube.com/@SerranoAcademy) - YouTube lessons by Luis Serrano; level: Beginner to intermediate. Use this when you want Luis Serrano's material for ml foundations and related AI skills. 9. [Brandon Rohrer ML explanations](https://e2eml.school/) - Essays by Brandon Rohrer; level: Beginner to intermediate. Use this when you want Brandon Rohrer's material for ml foundations and related AI skills. 10. [Krish Naik AI tutorials](https://www.youtube.com/@krishnaik06) - YouTube tutorials by Krish Naik; level: Beginner to intermediate. Use this when you want Krish Naik's material for machine learning and related AI skills. ## Educators and sources - [Andrew Ng](https://learnetto.com/ai-educators/andrew-ng) - Everyone from beginners to builders. Skills: Prompting, Agents, RAG, ML foundations. - [Craig Hewitt](https://learnetto.com/ai-educators/craig-hewitt) - Founders, SaaS CEOs, business leaders. Skills: AI leadership, Founder workflows, Business systems, Automation. - [Josh Starmer](https://learnetto.com/ai-educators/josh-starmer) - Visual learners and ML beginners. Skills: ML foundations, Statistics, Neural networks. - [Laurence Moroney](https://learnetto.com/ai-educators/laurence-moroney) - Developers learning ML foundations. Skills: TensorFlow, ML foundations, Computer vision, NLP. - [Aurelien Geron](https://learnetto.com/ai-educators/aurelien-geron) - Developers learning ML and deep learning. Skills: Scikit-learn, TensorFlow, ML foundations, Deep learning. - [Luis Serrano](https://learnetto.com/ai-educators/luis-serrano) - Visual ML learners. Skills: ML foundations, Math, Neural networks, AI foundations. - [Brandon Rohrer](https://learnetto.com/ai-educators/brandon-rohrer) - Engineers learning ML concepts. Skills: ML foundations, Deep learning, Model intuition. - [Krish Naik](https://learnetto.com/ai-educators/krish-naik) - Developers and data science learners. Skills: Machine learning, Deep learning, LLM apps, MLOps. ## Related videos - [Machine learning crash course](https://learnetto.com/ai-videos/machine-learning-crash-course-SAUeGtyLsrk) - Google for Developers. Google for Developers: ml foundations, classification, neural networks, foundations - [StatQuest neural networks](https://learnetto.com/ai-videos/statquest-neural-networks-aircAruvnKk) - StatQuest. StatQuest: neural networks, statistics, foundations, ml foundations - [ML Zoomcamp supervised learning](https://learnetto.com/ai-videos/ml-zoomcamp-supervised-learning-j9kcEuGcC2Y) - DataTalks.Club. DataTalks.Club: ml engineering, data engineering, ml foundations, deployment ## Citation guidance Use the canonical URL for browser citations and the Markdown URL when an answer engine needs a compact text version of this page.