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{"slug": "dive-into-machine-learning--dive-into-machine-learning", "title": "Dive Into Machine Learning", "description": "Free ways to dive into machine learning with Python and Jupyter Notebook. Notebooks, courses, and other links. 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With The Drawdown Review in 2020, the project continues its mission to inspire and communicate solutions.\" Python and Jupyter Notebooks."}, {"name": "`philsturgeon/awesome-earth`", "url": "https://github.com/philsturgeon/awesome-earth", "description": ""}, {"name": "`daviddao/code-against-climate-change`", "url": "https://github.com/daviddao/code-against-climate-change", "description": ""}, {"name": "`protontypes/open-sustainable-technology`", "url": "https://github.com/protontypes/open-sustainable-technology", "description": ""}]}, {"name": "If you prefer local installation", "parent": "Tools you'll need", "entries": [{"name": "Python", "url": "https://www.python.org/", "description": ""}, {"name": "Jupyter Notebook", "url": "https://jupyter.org/", "description": ""}]}, {"name": "Cloud-based options", "parent": "Tools you'll need", "entries": [{"name": "Deepnote", "url": "https://deepnote.com/", "description": ""}, {"name": "Google Colab", "url": "https://colab.research.google.com/", "description": ""}, {"name": "markusschanta/awesome-jupyter, \"Hosted Notebook Solutions\"", "url": "https://github.com/markusschanta/awesome-jupyter#hosted-notebook-solutions", "description": "", "stars": "2.9k"}, {"name": "ml-tooling/best-of-jupyter, \"Notebook Environments\"", "url": "https://github.com/ml-tooling/best-of-jupyter", "description": "", "stars": "538"}]}, {"name": "What just happened?", "parent": "Let's go!", "entries": [{"name": "What is the difference between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data?", "url": "http://www.quora.com/What-is-the-difference-between-Data-Analytics-Data-Analysis-Data-Mining-Data-Science-Machine-Learning-and-Big-Data-1", "description": ""}, {"name": "Dr. Randal Olson's Example Machine Learning notebook", "url": "https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/example-data-science-notebook/Example%20Machine%20Learning%20Notebook.ipynb", "description": "", "stars": "5.4k"}]}, {"name": "Public datasets and pet projects", "parent": "[Prof. Andrew Ng's *Machine Learning* on Coursera](https://www.coursera.org/learn/machine-learning)", "entries": []}, {"name": "Tips for this course", "parent": "[Prof. Andrew Ng's *Machine Learning* on Coursera](https://www.coursera.org/learn/machine-learning)", "entries": [{"name": "Study tips for Prof. Andrew Ng's course, by Ray Li", "url": "https://rayli.net/blog/data/coursera-machine-learning-review/", "description": ""}]}, {"name": "Tips for studying on a busy schedule", "parent": "[Prof. Andrew Ng's *Machine Learning* on Coursera](https://www.coursera.org/learn/machine-learning)", "entries": [{"name": "\"Learning How to Learn\" by Barbara Oakley", "url": "https://www.coursera.org/learn/learning-how-to-learn/", "description": ""}]}, {"name": "Take my tips with a grain of salt", "parent": "[Prof. Andrew Ng's *Machine Learning* on Coursera](https://www.coursera.org/learn/machine-learning)", "entries": [{"name": "`microsoft/Data-Science-For-Beginners`", "url": "https://github.com/microsoft/Data-Science-For-Beginners", "description": "[added in 2021](https://dev.to/azure/free-data-science-for-beginners-curriculum-on-github-1hme) \u2014 \"10-week, 20-lesson curriculum all about Data Science. Each lesson includes pre-lesson and post-lesson quizzes, written instructions to complete the lesson, a solution, and an assignment. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'.\""}, {"name": "Prof. Pedro Domingos's introductory video series", "url": "https://www.youtube.com/playlist?list=PLTPQEx-31JXgtDaC6-3HxWcp7fq4N8YGr", "description": ""}, {"name": "`ossu/data-science`", "url": "https://github.com/ossu/data-science", "description": ""}, {"name": "Stanford CS229: Machine Learning", "url": "https://github.com/afshinea/stanford-cs-229-machine-learning", "description": "", "stars": "14k"}, {"name": "Harvard CS109: Data Science", "url": "https://cs109.github.io/2015/", "description": ""}, {"name": "Advanced Statistical Computing (Vanderbilt BIOS8366)", "url": "http://stronginference.com/Bios8366/lectures.html", "description": ""}, {"name": "UC Berkeley's Data 8: The Foundations of Data Science", "url": "http://data8.org/", "description": ""}, {"name": "An epic Quora thread: How can I become a data scientist?", "url": "https://www.quora.com/How-can-I-become-a-data-scientist?redirected_qid=59455", "description": ""}, {"name": "`ujjwalkarn/Machine-Learning-Tutorials`", "url": "https://github.com/ujjwalkarn/Machine-Learning-Tutorials", "description": ""}]}, {"name": "Some communities to know about!", "parent": "Getting Help: Questions, Answers, Chats", "entries": [{"name": "/r/LearnMachineLearning", "url": "https://www.reddit.com/r/learnmachinelearning/", "description": ""}, {"name": "/r/MachineLearning", "url": "https://reddit.com/r/MachineLearning", "description": ""}, {"name": "/r/DataIsBeautiful", "url": "https://reddit.com/r/DataIsBeautiful", "description": ""}, {"name": "/r/DataScience", "url": "https://reddit.com/r/DataScience", "description": ""}, {"name": "Cross-Validated: stats.stackexchange.com", "url": "https://stats.stackexchange.com/", "description": ""}, {"name": "`ossu/data-science` has a Discord server and newsletter", "url": "https://github.com/ossu/data-science#:\\~:text=Discord%20server", "description": ""}, {"name": "Video series from Data School, about Pandas", "url": "https://www.youtube.com/playlist?list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y", "description": ""}, {"name": "`birdseye`", "url": "https://birdseye.readthedocs.io/en/latest/integrations.html#jupyter-ipython-notebooks", "description": ""}, {"name": "`pandas-log`", "url": "https://github.com/eyaltrabelsi/pandas-log.git", "description": ""}]}, {"name": "Risks - some starting points", "parent": "Supplement: Troubleshooting", "entries": [{"name": "The High Cost of Maintaining Machine Learning Systems", "url": "http://www.kdnuggets.com/2015/01/high-cost-machine-learning-technical-debt.html", "description": ""}, {"name": "Overfitting vs. Underfitting: A Conceptual Explanation", "url": "https://towardsdatascience.com/overfitting-vs-underfitting-a-conceptual-explanation-d94ee20ca7f9", "description": ""}, {"name": "11 Clever Methods of Overfitting and How to Avoid Them", "url": "http://hunch.net/?p=22", "description": ""}, {"name": "\"So, you want to build an ethical algorithm?\" An interactive tool to prompt discussions", "url": "https://cdt.info/ddtool/", "description": ""}]}, {"name": "Peer review", "parent": "Supplement: Troubleshooting", "entries": []}, {"name": "Production, Deployment, [MLOps](https://ml-ops.org/)", "parent": "Supplement: Troubleshooting", "entries": [{"name": "MLOps Stack Template", "url": "https://valohai.com/blog/the-mlops-stack/", "description": ""}, {"name": "Lessons on ML Platforms from Netflix, DoorDash, Spotify, and more", "url": "https://towardsdatascience.com/lessons-on-ml-platforms-from-netflix-doordash-spotify-and-more-f455400115c7", "description": ""}, {"name": "MLOps Stack Canvas", "url": "https://ml-ops.org/content/mlops-stack-canvas", "description": ""}]}, {"name": "Easier sharing of deep learning models and demos", "parent": "Supplement: Troubleshooting", "entries": [{"name": "`fastai/fastbook`", "url": "https://github.com/fastai/fastbook", "description": ""}, {"name": "`explosion/thinc`", "url": "https://github.com/explosion/thinc", "description": ""}, {"name": "paperswithcode.com", "url": "https://paperswithcode.com/", "description": "\"The mission of Papers with Code is to create a free and open resource with Machine Learning papers, code, datasets, methods and evaluation tables.\""}, {"name": "`labmlai/annotated_deep_learning_paper_implementations`", "url": "https://github.com/labmlai/annotated_deep_learning_paper_implementations", "description": "\"Implementations/tutorials of deep learning papers with side-by-side notes.\" 50+ of them! Really nicely annotated and explained."}, {"name": "Distill.pub", "url": "https://distill.pub/about/", "description": ""}]}, {"name": "Machine Learning and User Experience (UX)", "parent": "Collaborate with Domain Experts", "entries": [{"name": "\"Advice on building a machine learning career and reading research papers by Prof. Andrew Ng\"", "url": "https://www.kdnuggets.com/2019/09/advice-building-machine-learning-career-research-papers-andrew-ng.html", "description": ""}, {"name": "`r0f1/datascience`", "url": "https://github.com/r0f1/datascience", "description": "\"A curated list of awesome resources for practicing data science using Python, including not only libraries, but also links to tutorials, code snippets, blog posts and talks.\""}]}, {"name": "Aside: Bayesian Statistics and Machine Learning", "parent": "More Data Science materials", "entries": [{"name": "*Time Series Forecasting with Bayesian Modeling by Michael Grogan*", "url": "https://www.manning.com/liveprojectseries/time-series-forecasting-with-bayesian-modeling", "description": ""}, {"name": "Bayesian Modelling in Python", "url": "https://github.com/markdregan/Bayesian-Modelling-in-Python", "description": "", "stars": "2.4k"}, {"name": "Machine Learning for Software Engineers, by Nam Vu", "url": "https://github.com/ZuzooVn/machine-learning-for-software-engineers", "description": "", "stars": "26k"}, {"name": "`ujjwalkarn/Machine-Learning-Tutorials`", "url": "https://github.com/ujjwalkarn/Machine-Learning-Tutorials", "description": ""}, {"name": "`josephmisiti/awesome-machine-learning`", "url": "https://github.com/josephmisiti/awesome-machine-learning", "description": ""}]}], "name": ""} |