Probability / Statistics - The Foundations of Machine Learning

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Dr. Mohammad Nauman

6:35:19

137 View

00001 Introduction.mp4

02:17

00002 Code Environment Setup and Python Crash Course.mp4

18:51

00003 Getting Started with Code - Feel of Data.mp4

12:00

00004 Foundations Data Types and Representing Data.mp4

21:31

00005 Practical Note - One-Hot Vector Encoding.mp4

04:49

00006 Exploring Data Types in Code.mp4

12:21

00007 Central Tendency Mean Median and Mode.mp4

19:48

00008 Dispersion and Spread in Data Variance Standard Deviation.mp4

09:38

00009 Dispersion Exploration Through Code.mp4

11:05

00010 Introduction to Uncertainty Probability Intuition.mp4

12:17

00011 Simulating Coin Flips for Probability.mp4

17:19

00012 Conditional Probability the Most Important Concept in Stats.mp4

22:01

00013 Applying Conditional Probability - Bayes Rule.mp4

09:43

00014 Application of Bayes Rule in the Real World - Spam Detection.mp4

08:24

00015 Spam Detection - Implementation Issues.mp4

10:21

00016 Rules for Counting Mostly Optional.mp4

16:37

00017 Quantifying Events - Random Variables.mp4

10:12

00018 Two Random Variables - Joint Probabilities.mp4

13:48

00019 Distributions - Rationale and Importance.mp4

18:31

00020 Discrete Distributions Through Code.mp4

05:06

00021 Continuous Distributions with the Help of an Example.mp4

20:11

00022 Continuous Distributions Code.mp4

05:06

00023 Case Study - Sleep Analysis Structure and Code.mp4

18:28

00024 Visualizing Joint Distributions - The Road to ML Success.mp4

13:06

00025 Dependence and Variance of Two Random Variables.mp4

11:16

00026 Expected Values - Decision Making Through Probabilities.mp4

06:37

00027 Entropy - The Most Important Application of Expected Values.mp4

18:49

00028 Applying Entropy - Coding Decision Trees for Machine Learning.mp4

26:58

00029 Foundations of Bayesian Inference.mp4

11:43

00030 Bayesian Inference Code Through PyMC3.mp4

06:26

Probability-Statistics---The-Foundations-of-Machine-Learning-main.zip

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!

All the resources for this course are available at: https://github.com/PacktPublishing/Probability-Statistics---The-Foundations-of-Machine-Learning

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Machine Learning

Statistics

Probability

Dr. Mohammad Nauman

Instructor's Courses

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!

PacktPub

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Packt is a publishing company founded in 2003 headquartered in Birmingham, UK, with offices in Mumbai, India. Packt primarily publishes print and electronic books and videos relating to information technology, including programming, web design, data analysis and hardware.