Deep ML
deep-ml.comDeep-ML provides hands-on exercises in machine learning and data science fundamentals.
Looking for something like Solutions? 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 1 are open source.
Solutions are provided for exercises from the second edition of Hadley Wickham’s R programming book.
| # | Alternative | Pricing | Open source | Platforms |
|---|---|---|---|---|
| 1 | Deep ML | — | Not stated | Web |
| 2 | GeeksforGeeks | — | Not stated | Linux, Web |
| 3 | Sourcecodester | — | Not stated | Web |
| 4 | Tpoint Tech | — | Not stated | Android, Web |
| 5 | ProjectLearn | — | Not stated | Android, Web |
| 6 | Advanced R | — | Not stated | Web |
| 7 | ML Engineering | — | Not stated | — |
| 8 | Codelabs | — | Not stated | Android, iOS, Web |
| 9 | ItSourceCode | — | Not stated | Web |
| 10 | Coding Questions | — | Not stated | Web |
Deep-ML provides hands-on exercises in machine learning and data science fundamentals.
GeeksforGeeks provides programming tutorials, courses, interview preparation, and data structure practice.
Sourcecodester publishes free source code projects and tutorials in programming languages including Python, PHP, and Java.
Tpoint Tech publishes free online tutorials and interview questions across programming languages and technology subjects.
ProjectLearn teaches programming through projects across web, mobile, game development, machine learning, and artificial intelligence.
This bookdown site presents a second-edition book about programming and analysis with the R language.
This open book collects methods, tools, and instructions for training, fine-tuning, and running machine learning models.
Google Codelabs provides guided, hands-on tutorials for building applications and adding features across developer technologies.
ItSourceCode offers free source code, capstone projects, and programming tutorials in several languages.
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.
Scientific Python Lectures is a set of learning materials about Python for scientific computing.
Learn AI From Scratch presents a draft curriculum covering foundational topics for building an understanding of artificial intelligence.
The iAI KAIST page shares deep learning materials that visitors are permitted to use, modify, and distribute.
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.
CSRankings lists computer science research rankings and accepts support from visitors.
Deep Learning Drizzle collects readings and notes about deep learning for study.
Virgilio organizes data science e-learning resources in a structured project overview.
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.
Roadmap.sh publishes community-created developer roadmaps, guides, articles, and courses for learning technical skills.
Algorithms Cheatsheet Resources collects reference material for programming and algorithms.
System Designer teaches software architecture through lessons, quizzes, and case studies.
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