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Machine Learning

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About this learning path

Machine Learning

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. — Tom M. Mitchell

It comes from

  • ↑ Artificial Intelligence
  • includes the learning paths

  • - Python
  • - Statistics
  • - Calculus
  • and can be deepened into

  • ↓ Neural Networks
  • ↓ Supervised Learning
  • ↓ Unsupervised Learning
  • ↓ Reinforcement Learning
  • ↓ Active Learning
  • Web resources
    Start immediately and learn at your own pace.

    Flexible schedule
    Set and adhere to flexible deadlines.

    Approx. 4 months to complete
    Recommended 12 hours/week

    Mostly English

    Curator

    There is no curator for
    Machine Learning yet!

    This How it Works

    You go station by station. In each station you'll find resources containing:

    1. Information

    2. Educational content

    3. Places to exchange infos

    4. Applications, Tools

    ( 5. Entertaining content )

    After studying a resource, you check it.

    Between stations, sometimes there are learning paths. They need to be studied as well as they pose a qualification for upcoming content. After station 1, the learning path for Python needs to be studied.
    After station 2, the learning path for Statistics needs to be studied.
    After station 3, the learning path for Calculus needs to be studied.

    After finishing this learning path, you can specialize further or apply what you learned in another learning path.

    Learning Path

    Station

    1

    »

    Linear Regression

    »

    Statistical classification

    »

    Clustering

    »

    Popular Machine Learning Algorithms Used in Data Science Video

    »

    Machine learning problem framing Lessons

    »

    Maschinelles Lernen Video Lecture

    »

    Machine Learning Clustering

     

    Machine Learning Playground

    Station

    2

    »

    Overfitting

    »

    Bias-variance tradeoff

    »

    Goodhart's law Quote

    »

    Curse of dimensionality

    »

    Feature extraction

     

    Scikit-Learn Software libray

    »

    Chris Albon

    »

    Data Preparation and Feature Engineering in ML Lessons

    »

    Machine Learning Recipes with Josh Gordon Playlist

    »

    Machine Learning with Python Playlist

    Station

    3

    »

    Gradient Descent Method

    »

    Loss Function Method

    »

    TensorFlow Framework

    »

    Seedbank Database

    »

    Machine Learning Video Lecture

    »

    Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow

    »

    Testing and Debugging in Machine Learning Lessons

     

    The AI Podcast Interviews

    Station

    4

    »

    Machine Learning Yearning E-Book

    »

    colah's blog Articles

    »

    Sebastian Ruder Articles

    »

    when trees fall Columns

    »

    An Introduction to Statistical Learning Book

    »

    Visualizing Data using T-SNE

    »

    Adversarial machine learning

    »

    Collaborative Filtering Technology

    »

    Mathematics for Machine Learning Specialization

    »

    Python Machine Learning Non-fictional

     

    Kaggle Platform

     

    Lex Fridman Research Scientist