What is Machine Learning-

What is Machine Learning?, available at Free, has an average rating of 4.26, with 16 lectures, based on 1218 reviews, and has 16819 subscribers.

You will learn about Overview of Supervised, Unsupervised, and Reinforcement Learning This course is ideal for individuals who are People curious about machine learning and data science It is particularly useful for People curious about machine learning and data science.

Enroll now: What is Machine Learning?

Summary

Title: What is Machine Learning?

Price: Free

Average Rating: 4.26

Number of Lectures: 16

Number of Published Lectures: 16

Number of Curriculum Items: 16

Number of Published Curriculum Objects: 16

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Overview of Supervised, Unsupervised, and Reinforcement Learning

Who Should Attend

People curious about machine learning and data science

Target Audiences

People curious about machine learning and data science

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

Course Curriculum

Chapter 1: Introduction and Course Resources

Lecture 1: Section 1: Introduction

Chapter 2: Supervised Machine Learning

Lecture 1: Section 2: Supervised Machine Learning

Chapter 3: Unsupervised Machine Learning

Lecture 1: Section 3: Unsupervised Machine Learning

Chapter 4: Reinforcement Learning

Lecture 1: Section 4: Reinforcement Learning

Chapter 5: Demo of Python Codes

Lecture 1: Section 5.0: Demo of Python Codes

Lecture 2: Section 5.1: Demo of LInear Regression in Google Colab

Lecture 3: Section 5.2: Demo of Binary Classification in Google Colab

Lecture 4: Section 5.3: Demo of Multi-class Classification in Google Colab

Lecture 5: Section 5.4: Demo of MNIST Digits Classification in Google Colab

Lecture 6: Section 5.5: Demo of K Means Clustering in Google Colab

Lecture 7: Section 5.6: Demo of PCA in Google Colab

Lecture 8: Section 5.7: Demo of K Bandit in Google Colab

Lecture 9: Section 5.8: Demo of Maze Strategy in Google Colab

Lecture 10: Section 5.9: Running Codes on a Local Machine using Anaconda Platform

Chapter 6: Concluding Remarks and Useful Resources

Lecture 1: Section 6: Concluding Remarks and Useful Resources

Chapter 7: Optional

Lecture 1: Bonus Lecture (Optional)

Instructors

Satish Reddy
Machine Learning Consultant

Rating Distribution

1 stars: 17 votes

2 stars: 31 votes

3 stars: 157 votes

4 stars: 419 votes

5 stars: 594 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!