Probability Stats The Foundations of 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!
Requirements
Basic coding knowledge
No maths background needed (beyond basic arithmetic)
Crash course of Python provided in the contents
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. Let’s get through this together.
Overview
Section 1: Diving in with code
Lecture 1 Code env setup and Python crash course
Lecture 2 Getting started with code: Feel of data
Lecture 3 Foundations, data types and representing data
Lecture 4 Practical note: one-hot vector encoding
Lecture 5 Exploring data types in code
Lecture 6 Central tendency, mean, median, mode
Lecture 7 Section Review Tasks
Section 2: Measures of Spread
Lecture 8 Dispersion and spread in data, variance, standard deviation
Lecture 9 Dispersion exploration through code
Lecture 10 Section Review Tasks
Section 3: Applications and Rules for Probability
Lecture 11 Intro to uncertainty, probability intuition
Lecture 12 Simulating coin flips for probability
Lecture 13 Conditional probability; the most important concept in stats
Lecture 14 Applying conditional probability – Bayes rule
Lecture 15 Application of Bayes rule in real world – Spam detection
Lecture 16 Spam detection – implementation issues
Lecture 17 Section Review Tasks
Section 4: Counting
Lecture 18 Rules for counting (Mostly optional)
Lecture 19 Section Review Tasks
Section 5: Random Variables – Rationale and Applications
Lecture 20 Quantifying events – random variables
Lecture 21 Two random variables – joint probabilities
Lecture 22 Distributions – rationale and importance
Lecture 23 Discrete distributions through code
Lecture 24 Continuous distributions – probability densities
Lecture 25 Continuous distributions code
Lecture 26 Case study – sleep analysis, structure and code
Lecture 27 Section Review Tasks
Section 6: Visualization in Intuition Building
Lecture 28 Visualizing joint distributions – the road to ML success
Lecture 29 Dependence and variance of two random variables
Lecture 30 Section Review Tasks
Section 7: Applications to the Real World
Lecture 31 Expected values – decision making through probabilities
Lecture 32 Entropy – The most important application of expected values
Lecture 33 Applying entropy – coding decision trees for machine learning
Lecture 34 Foundations of Bayesian inference
Lecture 35 Bayesian inference code through PyMC3
Lecture 36 Section Review Tasks
Section 8: Extra Resources
Lecture 37 Bonus Lecture
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
Course Information:
Udemy | English | 6h 41m | 2.48 GB
Created by: Dr. Mohammad Nauman
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