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๐Ÿ“š Learn ML with clean code, simplified math and illustrative visuals. As you learn, work on interesting projects and share them on for the community to discover and learn from!
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As you learn ML, it's important to work on projects, so check out Made With ML for inspiration and to create a profile to showcase your own projects!

  • ๐Ÿ” Discover ML projects with code on niche topics that interest you.
  • ๐Ÿ›  Build projects of your own and share it with the community.
  • ๐Ÿ‘ฉโ€๐Ÿ’ป Showcase your profile on your resume or apply directly to ML managers.

Showcase your projects because everyone has Coursera, Kaggle, and fastai on their resumes so you need to differentiate yourself by showing what you can do using those fantastic resources. Check out this article on how to stand out with a MWML profile.

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  • ๐Ÿ“š Illustrative ML notebooks available in both TensorFlow 2.0 + Keras and PyTorch.
    • Should I pick TensorFlow or PyTorch? Choice of framework doesnโ€™t matter! Check out the basic lessons and choose what you find more intuitive/suitable but the most important thing is to work on projects and share them with the community.
    • Do I need to know both TensorFlow or PyTorch? It is very important to at least know how to read both frameworks because cutting edge research continues to use both frameworks. Luckily, they're both very easy to learn and very easy to rewrite in the other framework.
  • ๐Ÿ’ป These are not a set of tutorials where we just load a bunch of packages and apply it on preloaded datasets. We explain every concept in the notebooks with clean code, simple math and visualizations to make them as intuitive as possible.
  • ๐Ÿ““ If you prefer Jupyter Notebooks or want to add/fix content, check out the notebooks directory.


  • Learn Python basics with notebooks.
  • Use data science libraries like NumPy and Pandas.
  • Learn the basics of deep learning frameworks like TensorFlow and PyTorch.
๐Ÿ““ Notebooks ๐Ÿ Python ๐Ÿ”ข NumPy
๐Ÿผ Pandas TensorFlow PyTorch


๐Ÿ“ˆ Linear Regression
๐Ÿ“Š Logistic Regression
๏ธ๐ŸŽ› Multilayer Perceptrons
๐Ÿ”Ž Data & Models
๐Ÿ›  Utilities
๏ธโœ‚๏ธ Preprocessing
๏ธ๐Ÿ–ผ Convolutional Neural Networks
๐Ÿ‘‘ Embeddings
๐Ÿ“— Recurrent Neural Networks


  • Create a RESTful ML application using Fast API to create applications.
  • Perform unit tests on ML functions and implement appropriate logging throughout the application.
  • Walk through modeling and set fallbacks for inference in production.
๐ŸŽ APIs (video releasing very soon)
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  • Learn how to collect data and organize it using SQL.
  • Showcase your applications using a simple Boostrap front-end.
๐ŸŒ Web scraping ๐Ÿ”‹ SQL ๐ŸŽจ Bootstrap


  • Standardize and scale your ML applications with Docker and Kubernetes.
  • Deploy simple and scalable ML workflows using MLFlow.
๐Ÿณ Docker ๐Ÿšข Kubernetes ๐ŸŒŠ MLFlow


  • Dive into architectural and interpretable advancements in neural networks.
  • Implement state-of-the-art NLP techniques.
  • Learn about popular deep learning algorithms used for generation, time-series, etc.
๐Ÿง Attention ๐Ÿ“˜ Language Modeling ๐Ÿค— Transformers ๐Ÿคฏ SHA-RNN
๐ŸŽญ Generative Adversarial Networks ๐Ÿ”ฎ Autoencoders ๐Ÿ•ท๏ธ Graph Neural Networks โฑ Temporal CNNs
๐Ÿ’ Reinforcement Learning ๐ŸŽฏ One-shot Learning ๐ŸŽฑ Bayesian Deep Learning ๐Ÿ™ Causal Inference


  • Learn how to use deep learning for computer vision tasks.
  • Implement techniques for natural language tasks.
  • Derive insights from unlabeled data using unsupervised learning.
๐Ÿ“ธ Image Recognition ๐Ÿ–ผ๏ธ Image Segmentation ๐ŸŽจ Image Generation
๐Ÿ“– Text classification ๐Ÿ’ฌ Named Entity Recognition ๐Ÿง  Knowledge Graphs
๐Ÿ˜๏ธ Topic Modeling ๐Ÿก Clustering ๐Ÿ•ต๏ธ Anomaly Detection


  • Learn about miscellaneous topics that are at the forefront of ML research and application.
โฐ Time-series ๐ŸŽค Speech Recognition ๐Ÿ›’ Recommendation Systems
๐Ÿ—ƒ๏ธ Interpretability โœ‚๏ธ Model Compression โœ๏ธ Data Annotation
โš–๏ธ Imbalanced Datasets ๐Ÿ‘ป Missing Values ๐Ÿ“Š Data Visualization

Statistical Learning

  • Learn the basics of statistics that paved the way for all the topics above.
  • Implement statistical learning methods in scikit-learn.
๐Ÿงช Hypothesis Testing โค๏ธ Maximum Likelihood Estimation ๐Ÿ‘ถ Naive Bayes
๐Ÿ“ˆ Linear Regression ๐Ÿ“Š Logistic Regression ๐Ÿฆบ Support Vector Machines
๐ŸŒณ Random Forests ๐Ÿ˜ Nearest Neighbors ๐Ÿฟ Gaussian Processes
๐Ÿฅ… Matrix Decomposition ๐ŸŽฉ Hidden Markov Models ๐Ÿฆ  Survival Analysis
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