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Access to all recorded content till date**

Advanced ML breadth course*

Discord Channel for Doubts + Discussions

Interview Insights + Group AMA Sessions

Code Walkthrough to reinforce learning

7 years of Industry exposure as an applied scientist in top-tier companies.

This 12-month course is everything you need in ML to kickstart your machine learning career.

*Give us the flexibility to extend the timelines up to 6 months because of the vast tentative plan we have. This is year 1 of our offering hence the uncertainty of timelines. To reward this uncertainty we are running year 1 at a significant discount.

** All recordings will be completed tentatively by August 2025. By October 2024 we have 33 weeks worth of content.

Learn from Industry Professional!

The instructor has 7 years of industry exposure as an applied scientist in top-tier companies like Microsoft and Amazon. He has been working with very smart colleagues on extremely ambiguous projects on real data. He has also been interviewing candidates for Applied Scientist positions, hence providing interview insights during the course.

Ultimate Machine Learning Course will teach you foundational concepts like computational probability and statistics, Bayesian data analysis, relevant linear algebra, optimization, and calculus. We will then switch gears and learn about machine learning, deep learning, recommendation systems, and more.

You will also get exposure to relevant tools via code walkthrough examples which will consolidate the concepts learned. A few of the tools you will get exposed to are Numpy, Pandas, Pytorch, Pyro, PySpark, scikit-learn, xgboost.

After the course, you will be able to use the acquired knowledge in real-world settings.

Walk the learning path with me and improve your potential

Commit 12 months of your time and let’s walk together on this exciting learning path.

Course Structure Details Here

Features of our offerings

Learn From Industry Expert

Group AMA Sessions

We will have monthly group AMA sessions where you can have everything related to your or my career answered.

Applied Scientist Interview Perspectives

The instructor has been an Applied Scientist at top-tier companies like Amazon and Microsoft and has been at both sides of the interview table. He will share his interview perspectives.

Significant Focus on Computational Probability & Statistics

We will spend a significant amount of time with Bayesian thinking, computational probability, and statistics.

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Classical ML & Deep Learning

We will look at many methods in classical ML and deep learning.

Advanced Topics

We will look at a few advanced topics like variational inference, low variance estimators, diffusion models, counterfactual estimation, and learning.

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Large Industry Cases, ML Design & Select Research Papers

We will discuss a few large industry cases like advertising, and streaming platforms. We will also look at ML design thinking and select research papers.

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Code Walkthrough to reinforce Concepts

Code walk-through to reinforce concepts and get clarity on implementation.

Probabilistic Programming

We will get some exposure to pyro, a probabilistic programming framework with PyTorch backend.

Meet The Instructor

You will get to know Devendra and learn foundational concepts underlying hot topics in the news like LLM and Generative AI from industry practitioners who have worked at Amazon and Microsoft with a total industry experience of 7+ years.

Course Lessons

Module 0: Setting up Python Virtual Environment (13 mins)

Lessons

Session 1: Setting up a python virtual environment

Module 1: Python Crash Course (6 hours)

Python is the dominant programming language in Data Science and Machine Learning. This Module covers the basics of Python language.

Lessons

[W1] Session 1: Visual Studio Code, REPL, Primitives, Print function [W1] Session 2: String, List & Mutability, Tuple & Immutability, need for deepcopy [W1] Session 3: function, conditionals, if-else, loops [W1] Session 4: Set, Dictionary, comprehensions (list dictionary set generator) [W1] Session 5: args, kwargs, higher order functions, filter, map, reduce [W2] Session 6: Object Oriented Programming, class, data-validation, getter, setter, classmethod, staticmethod [W2] Session 7: Inheritance, Operator Overloading for custom datatypes & Polymorphism [W2] session 7b: classmethod & staticmethod clarification [W2] Session 8: Iterators & Generators [W2] Session 8b – Generator Pipelines, Operator Overloading for Pipelining [W2] Session 9: fileIO, try-except, PyTest, Type Hinting [W2] Session 10: concurrency, parallelism, multithreading & multiprocessing

Module 2: Background For Moving To Probability (3 hours 58 mins)

Before we start learning probability, we some basic grounding to jog our mathematics muscles. This module does that.

Lessons

[W3] Session 1: Data Science, Infinite Series, bar-plot, geometric series, derivatives Preview [W3] Session 2: Standard Derivatives, Rules of Derivatives, Permutation & Combination Preview [W3] Session 3: Binomial Series, Taylor Approximation Preview [W3 Optional] Session 4: Useful Result for Probability Module [W3] Session 5: Integration as Rieman Sum, Rules of Integration, Odd and Even functions & Examples Preview [W3] Session 6: Introduction to Probability & Set Theory Preview

Module 3: Numpy & pytorch Primitives (1 Hour)

This is just a starter module on numpy and pytorch primitives. This lets us get started with numerical libraries. We believe slow exposure over time is better for retention than bombarding everything in a single module.

Lessons

[W4] Session 1: Numpy Primitives [W4] Session 2: Pytorch Primitives

Module 4: Linear Algebra Part 1 ( 2 Hours 37 Mins )

We cover introductory parts of linear algebra (along with a bit of matrix calculus). Again with the philosophy that slow exposure over time is better for retention. We will have one more module in the future on linear algebra which will also have advanced concepts.