Machine Learning Guide

The Machine Learning Guide teaches the high-level fundamentals of machine learning and artificial intelligence. Join Tyler Renelle as he teaches the fundamentals and concepts of Machine Learning. 

 

Audio may seem an inferior medium, but it's a great supplement during exercise, a commute, or chores. Tyler will guide you through what Machine Learning is. Where your other resources provide the machine learning trees, Tyler Renelle provides the forest. Consider this your Machine Learning Syllabus.

Via this podcast course, you'll get an introduction to all things machine learning. There's no need for prior programming or math knowledge, just curiousity!

This introductory course will get you familiar with artificial intelligence, various mathematic requirements for machine learning, languages and frameworks, Shallow Algos, etc. It's a great way to get started in the field or to supplement and reinforce other studies. And it's lots of fun!

Podcasts have the ability to introduce topics in engaging and innovative ways. Combine that with our interactive platform of questions and exercises, and you'll find your understanding of machine learning greatly improved. Learn all about algorithms and artifical intelligence in no time. 

You'll learn:

  • Basic intuition

  • Algorithms and math

  • Explore languages and frameworks such as NLP, deep learning, Monte Carlo models, and more.

Tyler Renelle

CTO and Creator of HabitRPG, ML Engineer

Lessons

1

What exactly is machine learning? In this course, Tyler Renelle teaches high level fundamentals of machine learning and artificial intelligence. This course will be a "middle" level overview: you'll be able to learn about big ideas and take an audio peek at some of the details. There's no math or programming experience needed. So long as machine learning is of interest to you, this class is for you!

3

Inspiration

Ready to be inspired? The advent of technology has always left some afraid for their jobs and others excited about the future. But how is AI different from other developing technologies? In this lesson, you will learn about artificial intelligence in the context of the larger economy and the future of human development. 

We will cover: 

  • the AI-induced economic revolution

  • Singularity

  • AI Consciousness

  • The Scare

5

Linear Regression

What are the actual nuts and bolts of a machine learning algorithm? In this lesson, we'll learn about the most fundamental algorithmic example -- linear regression -- against an example of a housing market dataset. 

We'll take a deeper look at: 

  • Hypothesis functions

  • Cost functions

  • Gradient descents 

2

What is AI/ML?

What is artificial intelligence, and how does it relate to machine learning? These two phrases are often thrown about, and sometimes seem to be used interchangeably. In this lesson, you will learn: 

  • The different subfields and applications of artificial intelligence (AI)

  • How the technologies that apply artificial intelligence techniques change how we perceive AI 

You'll also get a brief background on why great thinkers and computer experts started thinking about this field in the first place. 

Introduction

4

Algorithms - Intuition

How does machine learning actually facilitate a machine's learning? What do these algorithms have in common? In this lesson, we'll dive deeper into the three computational steps we discussed last lesson (infer/predict, error/loss, and train/learn) with an example and learn about three types of machine learning:

  • Supervised learning

  • Unsupervised learning

  • Reinforced learning

6

Certificates & Degrees

So you're interested in studying more about machine learning. What's next? In this lesson, we'll discuss strategies for deeper exploration in the field that suits hobbyists and professionals alike: 

  • Certificates and online courses

  • Side projects

  • Accredited degrees

7

Logistic Regression

Your first classifier: Logistic Regression.

That plus Linear Regression and you're a 101 supervised learner! 

9

Deep Learning

Deep learning and neural networks. How to stack our logistic regression units into a multi-layer perceptron. 

8

Math

Introduction to the branches of mathematics used in machine learning: linear algebra, statistics, calculus.

10

Languages & Frameworks

Languages and frameworks comparison.

 

Languages: Python, R, MATLAB/Octave, Julia, Java/Scala, C/C++. 

Frameworks: Hadoop/Spark, Deeplearning4J, Theano, Torch, TensorFlow.

11

Checkpoint

Checkpoint - start learning the material offline!

12

Shallow Algos 1

13

Shallow Algos 2

14

Shallow Algos 3

15

Performance

16

Consciousness

17

Checkpoint

18

Natural Language Processing 1

19

Natural Language Processing 2

20

Natural Language Processing 3

21

New Series: Machine Learning Applied

22

Deep NLP 1

23

Deep NLP 2

24

Tech Stack

25

Convolutional Neural Networks

26

Project Bitcoin Trader

27

Hyperparameters 1

28

Hyperparameters 2

29

Reinforcement Learning Intro