Probability & Stats: Machine Learning Foundations

Probability / Stats: Machine Learning Foundations

Real-world, code-oriented learning for programmers to use prob/stats in all of CS, Data Science and Machine Learning

Created by : Rated :4.5 617 views

Last Updated: 2024-07-21 12:07:29 English Course Type: Self Learning

Necessary concepts in stats and probability
Important concepts in the subject necessary for Data Science and/or ML
Distributions and their importance
Entropy - the foundation of all Machine Learning
Intro to Bayesian Inference
Applying concepts through code

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. "Seem 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, 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 theory that isn't relevant to computer science (and is 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! Take a look at the promo for this course (and contents list below) for the topics you will learn as well as the preview lectures to get an idea of the interactive style of learning.

Remember: The reason you pay for this course is support. I reply within the day. See any of my course reviews for proof of that. So make sure you post any questions you have or any problems you face. I want all my students to finish this course. Let’s get through this together.

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

Beginner ML 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

Section 1 : Diving in with code 8 Lectures 01:32:11
Lecture 1 :
prob-cs-01-00-promo-v2 Preview
Lecture 2 :
Code Env Setup and Python Crash Course
Lecture 3 :
Getting Started with code- Feel of data
Lecture 4 :
Foundations, data types and representing data
Lecture 5 :
Practical note: one-hot vector encoding
Lecture 6 :
Exploring data types in code
Lecture 7 :
Central Tendency
Lecture 8 :
Resources
Section 2 : Measures of Spread 2 Lectures 00:20:52
Lecture 1 :
Dispersion and spread in data
Lecture 2 :
Dispersion exploration through code
Section 3 : Applications and Rules for Probability 6 Lectures 01:20:31
Lecture 1 :
Introduction to Uncertainty , Probability Intuition
Lecture 2 :
Simulating Coins Flip for probability
Lecture 3 :
Conditional probability; the most important concept in stats
Lecture 4 :
Applying conditional probability - Bayes rule
Lecture 5 :
Application of Bayes rule in real world - Spam detection
Lecture 6 :
Spam detection - implementation issues
Section 4 : Counting 1 Lectures 00:16:46
Lecture 1 :
Rules for counting (Mostly optional)
Section 5 : Random Variables - Rationale and Applications 7 Lectures 01:31:51
Lecture 1 :
Quantifying Events random Variables
Lecture 2 :
Two random variables - joint probabilities
Lecture 3 :
Distributions - rationale and importance
Lecture 4 :
Discrete distributions through code
Lecture 5 :
Continuous distributions - probability densities
Lecture 6 :
Continuous distributions code
Lecture 7 :
Case study - sleep analysis, structure and code
Section 6 : Visualization in Intuition Building 2 Lectures 00:24:34
Lecture 1 :
Visualization joint distributions - The Road of ML Success
Lecture 2 :
Dependence and variance of two random variables
Section 7 : Applications to the Real World 6 Lectures 01:10:52
Lecture 1 :
Expected values - decision making through probabilities
Lecture 2 :
Entropy Basics
Lecture 3 :
Applying entropy - coding decision trees for machine learning
Lecture 4 :
Foundations of Bayesian inference
Lecture 5 :
Bayesian inference code through PyMC3
Lecture 6 :
Resources

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Dr. Mohammed Nauman,

4803 Course Views

9 Courses

I have a PhD in Computer Sciences and a PostDoc from the Max Planck Institute for Software Systems. I have been programming since early 2000 and have worked with many different languages, tools and platforms. I have an extensive research experience with many state-of-the-art models to my name. My research in Android security has led to some major shifts in the Android permission model.