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Machine Learning

curated by Camillo

23 people already started

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 Mitchel

Machine Learning comes from the learning path of Artificial Intelligence . Machine Learning is a broad research field with many successful applications in real life. This paths presents an overview of this field. From module to module the paths lets you dive deeper into the technical details. At end you should be able to figure out yourself in what direction you like to specialize. Python is hereby the most common tool to build ML models. You should get familiar with it. The learning path of Machine Learning integrates concepts of Python & Calculus which you should dive into.
The Machine Learning path can be specialized into Artificial Neural Networks & Supervised Learning & Unsupervised Learning & Reinforcement Learning .
Machine Learning is particulary useful for Data Science .

Part of this learning path

Python

Calculus

Web resources
Start immediately and learn at your own pace.

Flexible schedule
Set and adhere to flexible deadlines.

Approx. 3 months to complete
Recommended 12 hours/week

Mostly English

Curator

Camillo Rohe curates
Machine Learning

This How it Works

You work from module to module. In each module 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.

Learning Path

Module

1

»

Outline of machine learning Outline

»

Statistical classification

»

Regression analysis Method

»

Clustering

»

Machine learning problem framing Lessons

»

Maschinelles Lernen Video Lecture

»

Teachable Machine Web Application

»

Machine Learning Playground

Module

2

»

Overfitting

»

Bias-variance tradeoff

»

Goodhart's law Quote

»

Curse of dimensionality

»

Feature extraction

»

Machine Learning Clustering

»

Data Preparation and Feature Engineering in ML Lessons

»

Machine Learning Recipes with Josh Gordon Playlist

»

Machine Learning with Python Playlist

 

Scikit-Learn Software libray

Module

3

»

Gradient Descent Method

»

Loss Function Method

»

Machine Learning Video Lecture

»

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

»

Python Data Science Handbook E-Book

»

Testing and Debugging in Machine Learning Lessons

»

PyTorch Framework

»

TensorFlow Framework

»

Chris Albon

»

Seedbank Database

Module

4

»

Machine Learning Yearning E-Book

»

colah's blog Articles

»

Sebastian Ruder Articles

»

when trees fall Columns

»

An Introduction to Statistical Learning Book

»

Adversarial machine learning

»

Collaborative Filtering Technology

»

Mathematics for Machine Learning Specialization

»

Python Machine Learning Non-fictional

 

Kaggle Platform

 

Papers With Code Plattform

 

Lex Fridman Research Scientist