Everything AI/ML
viveknaskar.github.io/everything-ai-mlThis curated AI and machine learning guide organizes over 228 learning resources across 30 sections.
Looking for something like ML Visualized? Here are 24 alternatives, ranked by how closely they match it on purpose, features and platforms. Of these, 1 are free or have a free plan and 7 are open source.
Machine Learning Visualized organizes its material as a collection of visual learning resources in a repository.
| # | Alternative | Pricing | Open source | Platforms |
|---|---|---|---|---|
| 1 | Everything AI/ML | — | Not stated | Web |
| 2 | ML YouTube Courses | — | Yes | — |
| 3 | Virgilio | — | Not stated | Web |
| 4 | Awesome DataScience | — | Yes | — |
| 5 | Coding Questions | — | Not stated | Web |
| 6 | mlsysbook | Free | Not stated | Web |
| 7 | Deep ML | — | Not stated | Web |
| 8 | Machine Learning Roadmap | — | Not stated | Web |
| 9 | Codecademy's Data Science | — | Not stated | Web |
| 10 | ProjectLearn | — | Not stated | Android, Web |
This curated AI and machine learning guide organizes over 228 learning resources across 30 sections.
A curated repository helps readers discover machine learning and AI courses published on YouTube.
Virgilio organizes data science e-learning resources in a structured project overview.
This Awesome DataScience repository collects learning material and resources about data science.
StrataScratch offers over 1,000 data science interview questions across SQL, Python, statistics, machine learning, and data projects.
Machine Learning Systems is a two-volume textbook about how machine learning systems are built, covering the engineering behind AI.
Deep-ML provides hands-on exercises in machine learning and data science fundamentals.
This machine learning roadmap lays out a learning path that starts with mathematics and calculus fundamentals.
Codecademy’s Machine Learning Specialist path covers Python, SQL, pandas, scikit-learn, Matplotlib, and TensorFlow.
ProjectLearn teaches programming through projects across web, mobile, game development, machine learning, and artificial intelligence.
Scientific Python Lectures is a set of learning materials about Python for scientific computing.
The iAI KAIST page shares deep learning materials that visitors are permitted to use, modify, and distribute.
A Python learning playground and cheat sheet organizes example scripts by topic, with explanations for the code.
Mallet provides machine learning tools for language processing, according to its project title.
A curated collection gathers learning resources for generative AI, machine learning, LLMs, RAG, and related topics.
A collaborative collection shares course and lecture notes on machine learning, natural language processing, and AI.
Jupyter notebooks implement and derive machine learning algorithms from first principles using NumPy.
A daily study plan for people working toward becoming machine learning engineers.
This tutorial walks through training and deploying a machine learning REST API that predicts cryptocurrency prices in ten steps.
A series of Jupyter notebooks teaches machine learning and deep learning in Python with Scikit-Learn, Keras, and TensorFlow.
This open book collects methods, tools, and instructions for training, fine-tuning, and running machine learning models.
Learn AI From Scratch presents a draft curriculum covering foundational topics for building an understanding of artificial intelligence.
Deep Learning Drizzle collects readings and notes about deep learning for study.
Roadmap.sh publishes community-created developer roadmaps, guides, articles, and courses for learning technical skills.
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