marinavillaschi / ml-andrewng

Notebooks from the Machine Learning Specialization

Machine Learning Specialization

by deeplearning.ai & Stanford University

The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications.

This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.

This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.

It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)

By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.

Course description

C1 - Supervised Machine Learning: Regression and Classification

• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.

• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression

C2 - Advanced Learning Algorithms

• Build and train a neural network with TensorFlow to perform multi-class classification

• Apply best practices for machine learning development so that your models generalize to data and tasks in the real world

• Build and use decision trees and tree ensemble methods, including random forests and boosted trees

C3 - Unsupervised Learning, Recommenders, Reinforcement Learning

• Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.

• Build recommender systems with a collaborative filtering approach and a content-based deep learning method.

• Build a deep reinforcement learning model.

ml-andrewng's Issues

Charts are not interactive

I was able to run through all the code but the rendered charts. (Ex week 3 - sigmoid function) are not interactive. I can’t click on any buttons or chart to add data.

Adding an open with collab button for ease of usage

adding a button like this would become a huge help for the learners
Here's a video I found to do the same
https://www.youtube.com/watch?v=RoGZIbwzG5w
and if you would like I would like to work on this issue!