ZWMiller / machine_learning_from_scratch
A place to hold various "from scratch" machine learning algorithms developed in Python as pedagogical tools.
☆60Updated 6 years ago
Related projects ⓘ
Alternatives and complementary repositories for machine_learning_from_scratch
- My presentation at ODSC India 2018 about Deep Learning with Apache Spark☆27Updated 6 years ago
- A complete daily plan for studying to become a machine learning engineer.☆50Updated 8 years ago
- TPOT Sample☆27Updated 5 years ago
- This is the presentation on - What are the key points one should consider if they will be appearing in Data Science job interview☆41Updated 6 years ago
- ☆31Updated 5 years ago
- Talks at PyData Bangalore meetups☆36Updated 4 years ago
- My competitions approach☆18Updated 2 years ago
- Codes used for the hack session in DHS 2019☆53Updated 4 years ago
- Detailed notes and code to learn the basics of machine learning with scikit-learn.☆33Updated 8 years ago
- Contains slides and hands-on tutorials for understanding and implementing Transformers in Natural Language Processing. Uses the HuggingFa…☆27Updated 4 years ago
- List of Machine Learning & Data Science Conferences☆84Updated 4 years ago
- ☆102Updated 6 years ago
- Codes, notes and guides on Udacity's machine learning nanodegree.☆83Updated 8 years ago
- A step-by-step guide to get started with Applied Machine Learning☆138Updated 6 years ago
- Interview stuff for friends☆82Updated last year
- ☆40Updated 6 years ago
- A Data Visualization Project examining Movie Data.☆16Updated 5 years ago
- Delta believes in building technical capacity all over the world. We believe that data is powerful, and that anybody should be able to ha…☆24Updated 6 years ago
- Jupyter notebooks for learning Python and Data Science, companion to Data Science Solutions book.☆36Updated 4 years ago
- Tutorial given at PyData LA 2018☆97Updated 2 months ago
- Contains all tutorials and hands-on examples for the ODSC 2019 Workshop☆37Updated 4 years ago
- Notebooks for tutorial on Numpy.☆61Updated 4 years ago
- Tutorial on Using Pandas☆82Updated 7 years ago
- Lecture slides and quizzes for Leskovec, Rajaraman, and Ullman's "Mining of Massive Datasets" Stanford course☆82Updated 6 years ago
- A flexible neural network framework for running experiments and trying ideas.☆79Updated 4 years ago
- Official Public site for CS109a Fall 2018☆34Updated 6 years ago
- Content for the Model Interpretability Tutorial at Pycon US 2019☆41Updated 3 months ago
- a python based module (bot) to generate kaggle baseline kernels☆26Updated 6 years ago