Machine Learning, Data Science, and Generative AI with Python

Complete hands-on machine learning tutorial with data science, Tensorflow, OpenAI, LLM Agents, RAG, artificial intelligence, and neural networks. Includes 20 hours of on-demand video and a certificate of completion.

New! Expanded with video and hands-on learning activities for GPT, ChatGPT, Advanced RAG, LLM Agents, Transformers, and more!

Also available at Udemy

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Lifetime access to 20 hours of videos and materials for this course with a one-time payment. Learn at your own pace!

Course Information


New! Updated for 2024 with extra content on generative AI- learn how ChatGPT works, and how to use it through OpenAI’s API, Advanced Retrieval-Augmented Generation techniques, and LLM Agents!

Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science and AI course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $150,000 according to Glassdoor and Indeed. That’s just the average! And it’s not just about money – it’s interesting work too!

If you’ve got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry – and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 145 lectures spanning 20 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t.

Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won’t find academic, deeply mathematical coverage of these algorithms in this course – the focus is on practical understanding and application of them. At the end, you’ll be given a final project to apply what you’ve learned!

The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We’ll cover the machine learning, AI, and data mining techniques real employers are looking for, including:

  • Deep Learning / Neural Networks (MLP’s, CNN’s, RNN’s) with TensorFlow and Keras
  • The Transformer architecture and multi-headed self-attention based neural networks
  • Using GPT with HuggingFace and Google CoLab
  • Using the OpenAI API to embed GPT and ChatGPT into your own applications
  • Fine-tuning GPT, with fun examples of creating Data from Star Trek and using IMDb data
  • Advanced Retrieval-Augmented Generation techniques (Advanced RAG)
  • LLM Agents and tools
  • Generative Models with Variational Auto-Encoders (VAE’s) and Generative Adversarial Networks (GAN’s)
  • Data Visualization in Python with MatPlotLib and Seaborn
  • Transfer Learning
  • Sentiment analysis
  • Image recognition and classification
  • Regression analysis
  • K-Means Clustering
  • Principal Component Analysis
  • Train/Test and cross validation
  • Bayesian Methods
  • Decision Trees and Random Forests
  • Multiple Regression
  • Multi-Level Models
  • Support Vector Machines
  • Reinforcement Learning
  • Collaborative Filtering
  • K-Nearest Neighbor
  • Bias/Variance Tradeoff
  • Ensemble Learning
  • Term Frequency / Inverse Document Frequency
  • Experimental Design and A/B Tests
  • Feature Engineering
  • Hyperparameter Tuning

…and much more! There’s also an entire section on machine learning with Apache Spark, which lets you scale up these techniques to “big data” analyzed on a computing cluster.

If you’re new to Python, don’t worry – the course starts with a crash course. If you’ve done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC’s, Linux desktops, and Macs.

If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by real-world industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now!

  • “I started doing your course… Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing.” – Kanad Basu, PhD

Course Instructor

Frank Kane Frank Kane Author

Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.

Buy This Course


Lifetime access to 20 hours of videos and materials for this course with a one-time payment. Learn at your own pace!

Getting Started

Statistics and Probability Refresher, and Python Practice

Predictive Models

Machine Learning with Python

Recommender Systems

More Data Mining and Machine Learning Techniques

Dealing with Real-World Data

Apache Spark: Machine Learning on Big Data

Experimental Design / ML in the Real World

Deep Learning and Neural Networks

Generative Models

Generative AI: GPT, ChatGPT, Transformers, Self-Attention

The OpenAI API (Developing with GPT and ChatGPT)

Retrieval Augmented Generation (RAG) and LLM Agents

Final Project

You Made It!

What Others Have Said

4 thoughts on “Machine Learning, Data Science, and Generative AI with Python”

  1. raj says:

    Hi Frank !
    I’m a great Fan of yours – tokk onsie-twosie courses from you and decided to enroll on the annual package plan- you are GOOD Sir !

    I am currently what people call a “Big Data Architect & Analytics Lead” and trying to break into DS, DL & ML…and need your guidance please ? i.e. IF I may trouble you ?
    c# 917 952 5597

    1. Frank Kane says:

      Thanks for your membership, Raj! With over 500,000 students I can’t really offer extensive 1:1 consultations, but “how do I break into the field” is a pretty common question. I prepared a couple of videos you may find helpful:

      I’m also going to be doing a free, live webinar for Udemy sometime in mid-December on “what tech hiring managers are really looking for” that you may find helpful; keep an eye out for that in your email.

      If you have any specific questions feel free to reply to this comment and I’ll do my best to answer them. In your case, my best advice would be to find a manager within your company who hires people in the role you want. In a normal year I’d suggest taking him or her to lunch or coffee just to better understand what they are looking for, but an “informational interview” online is probably the best you can do right now. Transferring internally is always easier than moving to a new company, as long as your current manager has been giving you good reviews.

  2. voxpopus says:

    Didn’t seet anyplace to ask this question so I put it in two places:

    Have a big problem.. Anaconda3 won’t install. I get the “Failed to create menus” error.
    Evidently this has been a Anaconda problem for a long time, many of the help sites go back to 2016.
    Hoping you have a suggestion so I can get started on the course. Got 60% finished on one of your Udemy courses.
    Made a dozen different attempts to get past the error.
    * I had already deleted my old Anaconda.
    * From CMD zapped out the $Path and reran
    * Tried running as administrator and toggling the Add Path and Install All variables.
    * Downloaded an earlier version and the 32bit version, both failed with the same.
    I can’t post my screen captures but it seems like Anaconda can’t find its own directories it just created in the install.

    Hoping you can give some guidance or provide some links so I can get going.

    1. Frank Kane says:

      Comments on lessons are the right place for questions! I replied to your first post on the help page.

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