Probability / Stats: The Foundations of Machine Learning

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Codestars • over 2 million students worldwide!,Dr. Mohammad Nauman

6:34:44

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1 - Diving in with code

1 - Code env setup and Python crash course.mp4

18:55

1 - prob-cs-notebooks.zip

1 - prob-cs-paper-notes.zip

2 - Getting started with code Feel of data.mp4

12:05

2 - prob-cs-notebooks.zip

2 - prob-cs-paper-notes.zip

3 - Foundations data types and representing data.mp4

21:36

4 - Practical note onehot vector encoding.mp4

04:54

5 - Exploring data types in code.mp4

12:26

6 - Central tendency mean median mode.mp4

19:41

7 - Section Review Tasks.html

2 - Measures of Spread

8 - Dispersion and spread in data variance standard deviation.mp4

09:43

9 - Dispersion exploration through code.mp4

11:10

10 - Section Review Tasks.html

3 - Applications and Rules for Probability

11 - Intro to uncertainty probability intuition.mp4

12:22

12 - Simulating coin flips for probability.mp4

17:24

13 - Conditional probability the most important concept in stats.mp4

22:06

14 - Applying conditional probability Bayes rule.mp4

09:48

15 - Application of Bayes rule in real world Spam detection.mp4

08:29

16 - Spam detection implementation issues.mp4

10:16

17 - Section Review Tasks.html

4 - Counting

18 - Rules for counting Mostly optional.mp4

16:42

19 - Section Review Tasks.html

5 - Random Variables Rationale and Applications

20 - Quantifying events random variables.mp4

10:17

21 - Two random variables joint probabilities.mp4

13:53

22 - Distributions rationale and importance.mp4

18:36

23 - Discrete distributions through code.mp4

05:11

24 - Continuous distributions probability densities.mp4

20:16

25 - Continuous distributions code.mp4

05:11

26 - Case study sleep analysis structure and code.mp4

18:23

27 - Section Review Tasks.html

6 - Visualization in Intuition Building

28 - Visualizing joint distributions the road to ML success.mp4

13:11

29 - Dependence and variance of two random variables.mp4

11:21

30 - Section Review Tasks.html

7 - Applications to the Real World

31 - Expected values decision making through probabilities.mp4

06:42

32 - Entropy The most important application of expected values.mp4

18:54

33 - Applying entropy coding decision trees for machine learning.mp4

27:03

34 - Foundations of Bayesian inference.mp4

11:48

35 - Bayesian inference code through PyMC3.mp4

06:21

36 - Section Review Tasks.html

8 - Extra Resources

37 - Bonus Lecture.html

Description

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

What You'll Learn?

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
Exceptional SUPPORT: Questions answered within the day. Try it!

Who is this for?

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

What You Need to Know?

Basic coding knowledge

No maths background needed (beyond basic arithmetic)

Crash course of Python provided in the contents

More details

Description
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. Letâ€™s get through this together.Â
Who this course is for:
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

User Reviews

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Best-selling Udemy instructor Rob Percival wants to revolutionize the way people learn to code by making it simple, logical, fun and, above all, accessible. But as just one man, Rob couldn’t create all the courses his students - more than half a million of them - wanted. That’s why Rob created Codestars. Together, the instructors that make up the Codestars team create courses on all the topics that students want to learn in the way that students want to learn them: courses that are well-structured, super interactive, and easy to understand. Codestars wants to make it as easy as possible for learners of all ages and levels to build functional websites and apps.

Dr. Mohammad Nauman

Instructor's Courses

Great hands-on courses for beginners All my courses are 100% hands-on with practical examples and demos. No lengthy theoretical discussions about boring topics. We dive into the practical and only see theory if needed (and only the minimum amount). Learn by doingStep-by-step tutorials and problem-based learning.Get excellent supportOne-on-one support by me ... All questions answered within 24 hours. I really want you to succeed! More about me 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.I love teaching and the most important reason I upload on Udemy is to make sure people can find my content. If you have any problem with finances and you want to take my courses, please visit my site (link on the left). I am more than willing to give out coupons that will make the course more affordable for you. You can see all the different areas I've worked with on my site as well as on my github page.

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