What is Machine Learning?

Name: What is Machine Learning?
Rating: 4.2 (1459 reviews)

An overview of Supervised, Unsupervised, and Reinforcement Learning with Python Demos

Created bySatish Reddy

Last updated 12/2022

English

What you'll learn

Overview of Supervised, Unsupervised, and Reinforcement Learning

Course content

7 sections • 16 lectures • 1h 57m total length

Section 1: Introduction5:42
Introduction to the course and directions to course resources

Section 5.0: Demo of Python Codes2:10
This section gives an overview of Section 5 of this course on demo of Python codes.
Section 5.1: Demo of LInear Regression in Google Colab10:15
Demo of using linear regression for house price prediction
Section 5.2: Demo of Binary Classification in Google Colab5:37
Demo of binary classification
Section 5.3: Demo of Multi-class Classification in Google Colab5:28
Demo of multiclass classification
Section 5.4: Demo of MNIST Digits Classification in Google Colab7:11
Demo of MNIST digits classification
Section 5.5: Demo of K Means Clustering in Google Colab4:42
Demo of K Means Clustering in Google Colab
Section 5.6: Demo of PCA in Google Colab3:38
Demo of PCA (Principal Component Analysis) in Google Colab
Section 5.7: Demo of K Bandit in Google Colab8:19
Demo of K Bandit Simulation in Google Colab
Section 5.8: Demo of Maze Strategy in Google Colab4:44
Demo of Maze Strategy using Q Learning algorithm in Google Colab
Section 5.9: Running Codes on a Local Machine using Anaconda Platform9:35
Instructions on running Python on a local machine using the Anaconda platform - both Anaconda Prompt and Jupyter Notebooks. Note: need only install Anaconda platform on your machine. Course codes will run "out of the box" and there is no need to install any other packages.

Requirements

Interest in machine learning

Description

Course Outcome:

Learners completing this course will be able to give definitions and explain the types of problems that can be solved by the 3 broad areas of machine learning: Supervised, Unsupervised, and Reinforcement Learning.

Course Topics and Approach:

This course gives a gentle introduction to the 3 broad areas of machine learning: Supervised, Unsupervised, and Reinforcement Learning. The goal is to explain the key ideas using examples with many plots and animations and little math, so that the material can be accessed by a wide range of learners. The lectures are supplemented by Python demos, which show machine learning in action. Learners are encouraged to experiment with the course demo codes. Additionally, information about machine learning resources is provided, including sources of data and publicly available software packages.

Course Audience:

This course has been designed for ALL LEARNERS!!!

Course does not go into detail into the underlying math, so no specific math background is required
No previous experience with machine learning is required
No previous experience with Python (or programming in general) is required to be able to experiment with the course demo codes

Teaching Style and Resources:

Course includes many examples with plots and animations used to help students get a better understanding of the material
All resources, including course codes, Powerpoint presentations, info on additional resources, can be downloaded from the course Github site

Python Demos:

There are several options for running the Python demos:

Run online using Google Colab (With this option, demo codes can be run completely online, so no downloads are required. A Google account is required.)
Run on local machine using the Anaconda platform (This is probably best approach for those who would like to run codes locally, but don't have python on their local machine. Demo video shows where to get free community version of Anaconda platform and how to run the codes.)
Run on local machine using python (This approach may be most suitable for those who already have python on their machines)

2021.09.28 Update

Section 5: update course codes, Powerpoint presentations, and videos so that codes are compatible with more recent versions of the Anaconda platform and plotting package

Who this course is for:

People curious about machine learning and data science

What is Machine Learning?

What you'll learn

Explore related topics

Course content

Introduction and Course Resources1 lecture • 6min

Supervised Machine Learning1 lecture • 14min

Unsupervised Machine Learning1 lecture • 13min

Reinforcement Learning1 lecture • 14min

Demo of Python Codes10 lectures • 1hr 2min

Concluding Remarks and Useful Resources1 lecture • 7min

Optional1 lecture • 1min

Requirements

Description

Who this course is for: