IAILab
iailab.kaist.ac.kr/teaching/deep-learningThe iAI KAIST page shares deep learning materials that visitors are permitted to use, modify, and distribute.
Looking for something like mlsysbook? Here are 24 alternatives, ranked by how closely they match it on purpose, features and platforms. Of these, 5 are open source.
Machine Learning Systems is a two-volume textbook about how machine learning systems are built, covering the engineering behind AI.
| # | Alternative | Pricing | Open source | Platforms |
|---|---|---|---|---|
| 1 | IAILab | — | Not stated | Web |
| 2 | Codecademy's Data Science | — | Not stated | Web |
| 3 | Machine Learning for Software Engineers | — | Not stated | — |
| 4 | Runestone Interactive | — | Not stated | Web |
| 5 | LearnAIFromScratch | — | Not stated | Web |
| 6 | Coding Questions | — | Not stated | Web |
| 7 | Deep ML | — | Not stated | Web |
| 8 | Machine Learning Roadmap | — | Not stated | Web |
| 9 | ML Visualized | — | Not stated | Web |
| 10 | Everything AI/ML | — | Not stated | Web |
The iAI KAIST page shares deep learning materials that visitors are permitted to use, modify, and distribute.
Codecademy’s Machine Learning Specialist path covers Python, SQL, pandas, scikit-learn, Matplotlib, and TensorFlow.
A daily study plan for people working toward becoming machine learning engineers.
Runestone Academy provides free online textbooks for mathematics and computer science.
Learn AI From Scratch presents a draft curriculum covering foundational topics for building an understanding of artificial intelligence.
StrataScratch offers over 1,000 data science interview questions across SQL, Python, statistics, machine learning, and data projects.
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.
Machine Learning Visualized organizes its material as a collection of visual learning resources in a repository.
This curated AI and machine learning guide organizes over 228 learning resources across 30 sections.
ProjectLearn teaches programming through projects across web, mobile, game development, machine learning, and artificial intelligence.
GitHub Student Developer Pack bundles partner tools and services to help students learn coding through hands-on projects.
Mallet provides machine learning tools for language processing, according to its project title.
A curated repository helps readers discover machine learning and AI courses published on YouTube.
Jupyter notebooks implement and derive machine learning algorithms from first principles using NumPy.
A curated collection gathers learning resources for generative AI, machine learning, LLMs, RAG, and related topics.
This tutorial walks through training and deploying a machine learning REST API that predicts cryptocurrency prices in ten steps.
A collaborative collection shares course and lecture notes on machine learning, natural language processing, and AI.
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.
System Designer teaches software architecture through lessons, quizzes, and case studies.
Scientific Python Lectures is a set of learning materials about Python for scientific computing.
Deep Learning Drizzle collects readings and notes about deep learning for study.
Virgilio organizes data science e-learning resources in a structured project overview.
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