Introduction to Machine Learning

By Scott Pletcher

Let us guide you through the sometimes intimidating world of machine learning in an entertaining and very non-scary way.

5 hours
  • 56 Lessons
  • 8 Course Quizzes

About the course

Hello Cloud Gurus!

Have you recently been thrown into your first machine learning project and need to get up to speed? Perhaps you’re struggling with all the mathematical jargon in other machine learning introductory courses?

Let machine learning Guru Scott Pletcher guide you through the sometimes intimidating world of machine learning in an entertaining and very non-scary way. Many “introductory” ML courses attempt to explain concepts using differential equations and cryptic Greek symbols–but not this course. This course is specifically designed for people without deep math backgrounds, and Scott cuts through the jargon with simile and metaphor to equip you with concepts and understanding you can put to work immediately.

Machine learning has certainly garnered lots of attention in recent years as organizations struggle to remain competitive in the Information Age arms race. Start-ups, established companies, and cloud providers are rapidly releasing new features and services aimed at ML practitioners. Unfortunately, the ability to effectively use these services to create genuine and repeatable business value is somewhat limited by the availability of skilled practitioners. Compounding matters, much of the available ML training in the market presumes a level of advanced mathematics knowledge which can be intimidating to novices.

In this course, you’ll learn:

  • Distinction between artificial intelligence, machine learning, data science, and statistical analysis.
  • The various types of machine learning with real-world examples such as regression, classification, decision trees, and deep learning. We’ll even train a self-driving car!
  • How to evaluate and frame business problems for potential machine learning applications.
  • Brief survey of available tools, datasets, and resources common in the machine learning space.
  • Limitations, potential pitfalls, and ethical considerations around machine learning.

This is an introductory course so don’t expect to walk away solving math theorems like in Good Will Hunting. However, do expect to come away with much greater machine learning confidence and understanding, pulling back that complexity curtain to unveil how all this modern-day magic really works. (Spoiler alert: it’s really not magic at all…)

So Cloud Gurus, let’s take that first step and get started on your machine learning journey.

  • Chapter 1 4 Lessons Welcome 10:06

    An Important Note About A Cloud Guru and Linux Academy Courses

    1:18

    Introduction

    2:37

    About This Course

    4:43

    About The Challenge Lessons

    1:28
  • Chapter 2 7 Lessons Back in the Day... 54:38

    Introduction

    0:46

    Origins

    9:02

    Machine Learning

    3:41

    Machine Learning Models

    7:41

    Why it Works: Logic Gates

    9:37

    Challenge Lesson: Logic Gates

    8:51

    Chapter 2 Quiz

    15:00 Quiz
  • Chapter 3 8 Lessons The Raw Ingredients 53:34

    Ingredients

    0:32

    All About That Data

    4:48

    Data Types

    8:24

    Data Preparation

    3:27

    Data Sets

    5:23

    Why it Works: Feature Scaling

    9:12

    Challenge Lesson: Data Exploration

    6:48

    Chapter 3 Quiz

    15:00 Quiz
  • Chapter 4 9 Lessons "Original Recipe": Supervised and Unsupervised Learning 51:04

    Introduction

    0:25

    Learning Time!

    4:59

    Supervision Required

    3:56

    Why it Works: Regression

    9:16

    Why it Works: Classification

    2:35

    Unsupervised Learning

    3:07

    Why it Works: Clustering

    3:58

    Why Do We Call Them "Algorithms"?

    7:48

    Chapter 4 Quiz

    15:00 Quiz
  • Chapter 5 7 Lessons "New and Improved": Reinforcement and Ensemble Learning 42:45

    Introduction

    0:28

    When Enough is Enough

    3:57

    Luke, Use the Reinforce!

    3:41

    Reinforcement Learning

    4:38

    Ensemble Learning

    3:58

    Challenge Lesson: Self-Driving Car

    11:03

    Chapter 5 Quiz

    15:00 Quiz
  • Chapter 6 8 Lessons A New Way of Thinking 47:53

    Introduction

    0:27

    What's Old is New Again

    5:20

    Deep Learning

    4:37

    Why It Works: Neural Networks

    4:26

    Convolutional Neural Networks

    3:18

    Even More Neural Networks!

    4:43

    Challenge Lesson: DIY Neural Net

    10:02

    Chapter 6 Quiz

    15:00 Quiz
  • Chapter 7 7 Lessons The Toolbox 42:39

    Introduction

    0:16

    You Belong Here Too!

    2:37

    The Tools of the Trade

    4:16

    Lingua Franca

    5:24

    Heavy Equipment

    6:54

    Challenge Lesson: My First Jupyter Notebook

    8:12

    Chapter 7 Quiz

    15:00 Quiz
  • Chapter 8 6 Lessons Unleash the Kraken! Components of an ML Project 37:09

    Introduction

    0:27

    I've Been Framed!

    7:15

    Training Camp

    5:16

    Deployment

    6:19

    Ok, Now What?

    2:52

    Chapter 8 Quiz

    15:00 Quiz
  • Chapter 9 6 Lessons A Cautionary Tale 45:15

    Introduction

    0:30

    Bias

    11:03

    Risk

    8:18

    Expectations

    4:42

    Ethics

    5:42

    Chapter 9 Quiz

    15:00 Quiz
  • Chapter 10 2 Lessons Look At You Now! 5:04

    Congratulations Are In Order

    0:48

    What's Next?

    4:16

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