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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
Course Overview
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.
Pre-requisites
- Basic knowledge of coding
- No maths background needed (beyond basic arithmetic)
- Crash course of Python provided in the contents
Target Audience
- 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
Curriculum 32 Lectures 06:37:37
-
Section 1 : Diving in with code
- 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
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Section 2 : Measures of Spread
- Lecture 1 :
- Dispersion and spread in data
- Lecture 2 :
- Dispersion exploration through code
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Section 3 : Applications and Rules for Probability
- 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
- Lecture 1 :
- Rules for counting (Mostly optional)
-
Section 5 : Random Variables - Rationale and Applications
- 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
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Section 6 : Visualization in Intuition Building
- Lecture 1 :
- Visualization joint distributions - The Road of ML Success
- Lecture 2 :
- Dependence and variance of two random variables
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Section 7 : Applications to the Real World
- 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
Our learners work at
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Instructor
2815 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.
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