Probability / Statistics - The Foundations of Machine Learning

Probability / Statistics - The Foundations of Machine Learning

by Dr. Mohammad Nauman
Released June 2022
Publisher(s): Packt Publishing
ISBN: 9781803241197

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Video description

The objective of this course is to give you a solid foundation 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 math 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. 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 will get to this immensely important concept rather quickly and give it 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!

What You Will Learn

  • Learn all necessary concepts in stats and probability
  • Learn important concepts for data science and/or machine learning
  • Understand distributions and their importance
  • Learn about Entropy, which is the foundation of all machine learning
  • Introduction to Bayesian Inference
  • Learn to apply concepts through code

Audience

About The Author

Dr. Mohammad Nauman: Dr. Mohammad Nauman has a PhD in computer science and a PostDoc from the Max Planck Institute for software systems. He has been programming since early 2000 and has worked with many different languages, tools, and platforms. He holds extensive research experience with many state-of-the-art models. His research in Android security has led to some major shifts in the Android permission model.

He loves teaching and the most important reason he teaches online is to make sure that maximum people can learn through his content. Hope you have fun learning with him!

Table of contents

  1. Chapter 1 : Diving in with Code
    1. Introduction
    2. Code Environment Setup and Python Crash Course
    3. Getting Started with Code: Feel of Data
    4. Foundations, Data Types, and Representing Data
    5. Practical Note: One-Hot Vector Encoding
    6. Exploring Data Types in Code
    7. Central Tendency, Mean, Median, and Mode
  2. Chapter 2 : Measures of Spread
    1. Dispersion and Spread in Data, Variance, Standard Deviation
    2. Dispersion Exploration Through Code
  3. Chapter 3 : Applications and Rules for Probability
    1. Introduction to Uncertainty, Probability Intuition
    2. Simulating Coin Flips for Probability
    3. Conditional Probability, the Most Important Concept in Stats
    4. Applying Conditional Probability - Bayes Rule
    5. Application of Bayes Rule in the Real World - Spam Detection
    6. Spam Detection - Implementation Issues
  4. Chapter 4 : Counting
    1. Rules for Counting (Mostly Optional)
  5. Chapter 5 : Random Variables - Rationale and Applications
    1. Quantifying Events - Random Variables
    2. Two Random Variables - Joint Probabilities
    3. Distributions - Rationale and Importance
    4. Discrete Distributions Through Code
    5. Continuous Distributions with the Help of an Example
    6. Continuous Distributions Code
    7. Case Study: Sleep Analysis, Structure, and Code
  6. Chapter 6 : Visualization in Intuition Building
    1. Visualizing Joint Distributions - The Road to ML Success
    2. Dependence and Variance of Two Random Variables
  7. Chapter 7 : Applications to the Real World
    1. Expected Values - Decision Making Through Probabilities
    2. Entropy - The Most Important Application of Expected Values
    3. Applying Entropy - Coding Decision Trees for Machine Learning
    4. Foundations of Bayesian Inference
    5. Bayesian Inference Code Through PyMC3

Product information

  • Title: Probability / Statistics - The Foundations of Machine Learning
  • Author(s): Dr. Mohammad Nauman
  • Release date: June 2022
  • Publisher(s): Packt Publishing
  • ISBN: 9781803241197