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Learn Machine Learning with Python taught by Googler
Rating: 5.0 out of 5(3 ratings)
1,025 students

Learn Machine Learning with Python taught by Googler

Your Hands-On Guide to Building Intelligent Systems & Real-World AI Solutions
Created byRajiv Pujala
Last updated 8/2025
English

What you'll learn

  • Master the core Python concepts required for Data Science and Artificial Intelligence.
  • Implement key machine learning algorithms on real-world data for practical insights.
  • Build, train, and evaluate custom deep neural networks from scratch using PyTorch.
  • Develop an end-to-end AI application by integrating Python, ML, and PyTorch skills.
  • 50+ advanced LEETCODES on Python to practice

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

See a demo
Image of coding exercise example

Course content

4 sections • 47 lectures • 9h 57m total length

  • Part 152:24

    Learn how to create meaningful variable names, manipulate strings with escape sequences and formatted strings, and explore string methods. Understand numbers including integers, floats, and complex types. Master numerical functions like round, abs, ceil, floor, perform data type conversion, use comparison operators, and write effective conditional IF statements.

  • Part 240:08

    Explore the ternary operator for concise conditionals and master logical operators with short circuit evaluation. Learn chaining comparisons for readable code. Practice with a quiz, and dive into for loops, nested loops, and working with iterables to efficiently process collections and control program flow.

  • Part 318:29

    Master while loops to execute code repeatedly based on conditions. Test your knowledge with a quiz. Learn how to define functions with various argument types, including keyword and default arguments, and understand different function types to write flexible, reusable, and efficient Python code.

  • Part 436:08

    Understand how to use *args and **kwargs for flexible function arguments. Explore core data structures like lists, tuples, sets, and dictionaries, learn to access items, perform list unpacking, and master sorting lists to organize and manipulate data efficiently in Python.

  • Part 517:01

    Learn how to sort tuples efficiently and harness the power of lambda functions for concise, anonymous operations. Explore map, zip, and filter functions to process collections, and master list comprehensions for clean, readable, and Pythonic ways to create and transform lists quickly.

  • Part 618:39

    Discover tuple comprehensions (using generator expressions), and master generator expressions for memory-efficient data processing. Learn effective exception handling to write robust code, and dive into classes to implement object-oriented programming, enabling you to create reusable, modular, and organized Python programs with custom data structures and behaviors.

Requirements

  • A computer with a stable internet connection. All lectures and materials are delivered online, and you will need to download software and datasets.
  • Basic computer skills. You should be comfortable with tasks like downloading and installing software, creating folders, and navigating your operating system (Windows, macOS, or Linux).
  • High school level mathematics. A basic understanding of algebra and concepts like variables and functions will be very helpful, but we will review key mathematical concepts as needed.
  • No prior programming experience is required! The course is designed to take you from the fundamentals of Python all the way to advanced AI topics. A strong desire to learn is the most important prerequisite.

Description

This course is meticulously designed for students and working professionals who are passionate about applied machine learning with Scikit-learn. Leveraging my 10+ years of industry experience, I've customized this program to provide a truly practical and comprehensive learning journey.

This course meticulously covers:

  1. Python fundamentals: Build a strong foundation with essential data structures like lists, tuples, sets, and dictionaries, alongside classes and functions. You'll gain practical coding experience through dedicated exercises.

  2. Applied machine learning with Python: Dive deep into real-world scenarios, exploring multiple classification and regression models to solve diverse problems. You'll learn the art of data preprocessing, model selection, and performance evaluation.

This course is overall structured with muliple projects and recap sessions which helps you to get how ML is being used.

By the end of this course, you'll be profoundly comfortable with Machine Learning and Deep Learning concepts, equipped with the confidence and practical skills to start applying for jobs in the rapidly expanding AI field. You'll not only understand the theory but also how to implement and optimize models, making you a valuable asset in any data-driven team. This journey will transform your understanding of AI into tangible, employable skills.


Thank you and all the best !

Who this course is for:

  • Aspiring Data Scientists & AI Specialists: If you're looking to start a career in the exciting field of AI but don't know where to begin, this course provides the complete roadmap.
  • Absolute Beginners in Programming: We start with the absolute basics of Python. No prior coding experience is necessary to follow along and succeed.
  • Programmers & Developers: If you already know another programming language, this course will get you up to speed with Python and show you how to apply your skills to the domains of Machine Learning and Deep Learning.
  • Students & Academics: This course will help you bridge the gap between theoretical knowledge and practical, hands-on application by working on real-world projects.

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