Deep ML
deep-ml.comDeep-ML provides hands-on exercises in machine learning and data science fundamentals.
Looking for something like Coding Questions? 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 5 are open source.
StrataScratch offers over 1,000 data science interview questions across SQL, Python, statistics, machine learning, and data projects.
| # | Alternative | Pricing | Open source | Platforms |
|---|---|---|---|---|
| 1 | Deep ML | — | Not stated | Web |
| 2 | Virgilio | — | Not stated | Web |
| 3 | Data Science Ipython Notebooks | — | Not stated | — |
| 4 | Awesome DataScience | — | Yes | — |
| 5 | Virgilio | — | Not stated | — |
| 6 | ML YouTube Courses | — | Yes | — |
| 7 | ML Course Notes | — | Not stated | — |
| 8 | R for Data Science | — | Not stated | Web |
| 9 | Codecademy's Data Science | — | Not stated | Web |
| 10 | Tpoint Tech | — | Not stated | Android, Web |
Deep-ML provides hands-on exercises in machine learning and data science fundamentals.
Virgilio organizes data science e-learning resources in a structured project overview.
Data Science IPython Notebooks collects notebooks about data science, including TensorFlow deep learning.
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.
The first edition of R for Data Science teaches data import, transformation, visualization, and modeling with R.
Codecademy’s Machine Learning Specialist path covers Python, SQL, pandas, scikit-learn, Matplotlib, and TensorFlow.
Tpoint Tech publishes free online tutorials and interview questions across programming languages and technology subjects.
Awesome Data Analysis collects more than 500 resources for data analysis and data science.
OSSU Data Science provides a free study path based on an undergraduate data science curriculum.
GeeksforGeeks provides programming tutorials, courses, interview preparation, and data structure practice.
HackerEarth's Compete section offers programming challenges from companies for users to test their skills.
A series of Jupyter notebooks teaches machine learning and deep learning in Python with Scikit-Learn, Keras, and TensorFlow.
Machine Learning Systems is a two-volume textbook about how machine learning systems are built, covering the engineering behind AI.
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.
Scientific Python Lectures is a set of learning materials about Python for scientific computing.
A public list collects open-source frontend coding challenges from companies for testing development skills.
Mallet provides machine learning tools for language processing, according to its project title.
Sourcecodester publishes free source code projects and tutorials in programming languages including Python, PHP, and Java.
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