Data Science Ipython Notebooks
github.com/donnemartin/data-science-ipython-no…Data Science IPython Notebooks collects notebooks about data science, including TensorFlow deep learning.
Looking for something like Deep Learning Drizzle? 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.
Deep Learning Drizzle collects readings and notes about deep learning for study.
| # | Alternative | Pricing | Open source | Platforms |
|---|---|---|---|---|
| 1 | Data Science Ipython Notebooks | — | Not stated | — |
| 2 | Deep ML | — | Not stated | Web |
| 3 | IAILab | — | Not stated | Web |
| 4 | ML Course Notes | — | Not stated | — |
| 5 | Handson ML3 | — | Yes | — |
| 6 | Machine Learning for Software Engineers | — | Not stated | — |
| 7 | LLM Course | — | Yes | — |
| 8 | ML Engineering | — | Not stated | — |
| 9 | Awesome DataScience | — | Yes | — |
| 10 | Coding Questions | — | Not stated | Web |
Data Science IPython Notebooks collects notebooks about data science, including TensorFlow deep learning.
Deep-ML provides hands-on exercises in machine learning and data science fundamentals.
The iAI KAIST page shares deep learning materials that visitors are permitted to use, modify, and distribute.
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.
A daily study plan for people working toward becoming machine learning engineers.
This LLM course combines learning roadmaps with Google Colab notebooks for studying large language models.
This open book collects methods, tools, and instructions for training, fine-tuning, and running machine learning models.
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.
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.
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.
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.
ProjectLearn teaches programming through projects across web, mobile, game development, machine learning, and artificial intelligence.
Google Codelabs provides guided, hands-on tutorials for building applications and adding features across developer technologies.
Algorithms Cheatsheet Resources collects reference material for programming and algorithms.
Awesome YouTubers collects channels that teach a range of subjects through video.
GeeksforGeeks provides programming tutorials, courses, interview preparation, and data structure practice.
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