Looking for something like Deep ML? 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 4 are open source.
Deep-ML provides hands-on exercises in machine learning and data science fundamentals.
| # | Alternative | Pricing | Open source | Platforms |
|---|---|---|---|---|
| 1 | Coding Questions | — | Not stated | Web |
| 2 | Data Science Ipython Notebooks | — | Not stated | — |
| 3 | Awesome Data Analysis | — | Yes | — |
| 4 | Virgilio | — | Not stated | Web |
| 5 | Awesome DataScience | — | Yes | — |
| 6 | Virgilio | — | Not stated | — |
| 7 | ML YouTube Courses | — | Yes | — |
| 8 | ML Course Notes | — | Not stated | — |
| 9 | Roadmap | — | Not stated | Android, iOS, Linux |
| 10 | Developer Roadmap | — | Not stated | Linux |
StrataScratch offers over 1,000 data science interview questions across SQL, Python, statistics, machine learning, and data projects.
Data Science IPython Notebooks collects notebooks about data science, including TensorFlow deep learning.
Awesome Data Analysis collects more than 500 resources for data analysis and data science.
Virgilio organizes data science e-learning resources in a structured project overview.
This Awesome DataScience repository collects learning material and resources about data science.
An open-source initiative, Virgilio guides learners through data science resources and skill development.
A curated repository helps readers discover machine learning and AI courses published on YouTube.
A collaborative collection shares course and lecture notes on machine learning, natural language processing, and AI.
Roadmap.sh publishes community-created developer roadmaps, guides, articles, and courses for learning technical skills.
Developer Roadmap provides interactive roadmaps, guides, articles, and educational content for developers.
This machine learning roadmap lays out a learning path that starts with mathematics and calculus fundamentals.
The first edition of R for Data Science teaches data import, transformation, visualization, and modeling with R.
This online, work-in-progress third edition explains data analysis and visualization with ggplot2.
OSSU Data Science provides a free study path based on an undergraduate data science curriculum.
A series of Jupyter notebooks teaches machine learning and deep learning in Python with Scikit-Learn, Keras, and TensorFlow.
Hackr.io catalogs technology courses, projects, user-submitted resources, and learning topics.
The iAI KAIST page shares deep learning materials that visitors are permitted to use, modify, and distribute.
Deep Learning Drizzle collects readings and notes about deep learning for study.
Google Codelabs provides guided, hands-on tutorials for building applications and adding features across developer technologies.
Machine Learning Systems is a two-volume textbook about how machine learning systems are built, covering the engineering behind AI.
Machine Learning Visualized organizes its material as a collection of visual learning resources in a repository.
Codecademy’s Machine Learning Specialist path covers Python, SQL, pandas, scikit-learn, Matplotlib, and TensorFlow.
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.
Alternatives are suggested from the purpose and features described in our listings. Different pages for the same product appear once. Read each description to check whether it fits your task. How we choose alternatives · Suggest an alternative
