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About This Course

Who this course is for:

  • Beginner machine learning and data science developers who need a strong foundation
  • Developers curious about data science and machine learning
  • People looking to find out why probability is the foundation of all modern machine learning
  • Developers who want to know how to harness the power of big data

What you’ll learn: 

  • Necessary concepts in statistics and probability
  • Important concepts in the subject necessary for Data Science and/or Machine Learning
  • Distributions and their importance
  • Entropy – the foundation of all Machine Learning
  • Introduction to Bayesian Inference
  • Applying concepts through code

Requirements: 

  • Basic coding knowledge
  • No maths background needed (beyond basic arithmetic)
  • Crash course of Python provided in the contents

Everyone wants to excel at machine learning and data science these days — and for good reason. Data is the new oil and everyone should be able to work with it. However, it’s very difficult to become great in the field because the latest and greatest models seem too complicated. “Seems complicated” — but they are not! If you have a thorough understanding of probability and statistics, they would be much, much easier to work with! And that’s not all — probability is useful in almost all areas of computer science (simulation, vision, game development, and AI are only a few of these). If you have a strong foundation in this subject, it opens up several doors for you in your career!

That is the objective of this course: to give you the strong foundations needed to excel in all areas of computer science — specifically data science and machine learning. The issue is that most of the probability and statistics courses are too theory-oriented. They get tangled in the maths without discussing the importance of applications. Applications are always given secondary importance.

In this course, we take a code-oriented approach. We apply all concepts through code. In fact, we skip over all the useless theories that aren’t relevant to computer science (and are useful for those pursuing pure sciences). Instead, we focus on the concepts that are more useful for data science, machine learning, and other areas of computer science. For instance, many probability courses skip over Bayesian inference. We get to this immensely important concept rather quickly and give it the due attention as it is widely thought of as the future of analysis!

This way, you get to learn the most important concepts in this subject in the shortest amount of time possible without having to deal with the details of the less relevant topics. Once you have developed an intuition of the important stuff, you can then learn the latest and greatest models even on your own! 

Our Promise to You

By the end of this course, you will have a thorough understanding of probability and statistics.

30 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.

Get started today and learn more about foundations needed to excel in all areas of computer science.

Course Curriculum

6h 31m

Section 1 - Diving In With Code
Code Environment Setup And Python Crash Course 00:00:00
Downloadable Course Resources 00:00:00
Getting Started With Code: Feel Of Data 00:00:00
Foundations, Data Types, And Representing Data 00:00:00
Practical Note: One-Hot Vector Encoding 00:00:00
Exploring Data Types In Code 00:00:00
Central Tendency, Mean, Median, Mode 00:00:00
Section Review Tasks 00:00:00
Section 2 - Measures Of Spread
Dispersion And Spread In Data, Variance, Standard Deviation 00:00:00
Dispersion Exploration Through Code 00:00:00
Section Review Tasks 00:00:00
Section 3 - Applications And Rules For Probability
Introduction To Uncertainty, Probability Intuition 00:00:00
Simulating Coin Flips For Probability 00:00:00
Conditional Probability – The Most Important Concept In Statistics 00:00:00
Applying Conditional Probability – Bayes Rule 00:00:00
Application Of Bayes Rule In Real World – Spam Detection 00:00:00
Spam Detection – Implementation Issues 00:00:00
Section Review Tasks 00:00:00
Section 4 - Counting
Rules For Counting (Mostly Optional) 00:00:00
Section Review Tasks 00:00:00
Section 5 - Random Variables - Rationale And Applications
Quantifying Events – Random Variables 00:00:00
Two Random Variables – Joint Probabilities 00:00:00
Distributions – Rationale And Importance 00:00:00
Discrete Distributions Through Code 00:00:00
Continuous Distributions – Probability Densities 00:00:00
Continuous Distributions Code 00:00:00
Case Study – Sleep Analysis, Structure And Code 00:00:00
Section Review Tasks 00:00:00
Section 6 - Visualization In Intuition Building
Visualizing Joint Distributions – The Road To Machine Learning Success 00:00:00
Dependence And Variance Of Two Random Variables 00:00:00
Section Review Tasks 00:00:00
Section 7 - Applications To The Real World
Expected Values – Decision Making Through Probabilities 00:00:00
Entropy – The Most Important Application Of Expected Values 00:00:00
Applying Entropy – Coding Decision Trees For Machine Learning 00:00:00
Foundations Of Bayesian Inference 00:00:00
Bayesian Inference Code Through PyMC3 00:00:00
Section Review Tasks 00:00:00
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