Machine Learning, Data Science & AI Engineering with Python
-
Getting StartedIntroduction0sImportant noteInstallation: Getting Started[Activity] WINDOWS: Installing and Using Anaconda & Course Materials0s[Activity] MAC: Installing and Using Anaconda & Course Materials0s[Activity] LINUX: Installing and Using Anaconda & Course Materials0sPython Basics, Part 1 [Optional]0s[Activity] Python Basics, Part 2 [Optional]0s[Activity] Python Basics, Part 3 [Optional]0s[Activity] Python Basics, Part 4 [Optional]0sIntroducing the Pandas Library [Optional]0s
-
Statistics and Probability Refresher, and Python PracticeTypes of Data (Numerical, Categorical, Ordinal)0sMean, Median, Mode0s[Activity] Using mean, median, and mode in Python0s[Activity] Variation and Standard Deviation0sProbability Density Function; Probability Mass Function0sCommon Data Distributions (Normal, Binomial, Poisson, etc)0s[Activity] Percentiles and Moments0s[Activity] A Crash Course in matplotlib0s[Activity] Advanced Visualization with Seaborn0s[Activity] Covariance and Correlation0s[Exercise] Conditional Probability0sExercise Solution: Conditional Probability of Purchase by Age0sBayes’ Theorem0s
-
Predictive Models
-
Machine Learning with PythonSupervised vs. Unsupervised Learning, and Train/Test0s[Activity] Using Train/Test to Prevent Overfitting a Polynomial Regression0sBayesian Methods: Concepts0s[Activity] Implementing a Spam Classifier with Naive Bayes0sK-Means Clustering0s[Activity] Clustering people based on income and age0sMeasuring Entropy0s[Activity] WINDOWS: Installing Graphviz0s[Activity] MAC: Installing Graphviz0s[Activity] LINUX: Installing Graphviz0sDecision Trees: Concepts0s[Activity] Decision Trees: Predicting Hiring Decisions0sEnsemble Learning0s[Activity] XGBoost0sSupport Vector Machines (SVM) Overview0s[Activity] Using SVM to cluster people using scikit-learn0s
-
Recommender SystemsUser-Based Collaborative Filtering0sItem-Based Collaborative Filtering0s[Activity] Finding Movie Similarities using Cosine Similarity0s[Activity] Improving the Results on Movie Similarities0s[Activity] Making Movie Recommendations with Item-Based Collaborative Filtering0s[Exercise] Improve the recommender’s results0s
-
More Data Mining and Machine Learning TechniquesK-Nearest-Neighbors: Concepts0s[Activity] Using KNN to predict a rating for a movie0sDimensionality Reduction; Principal Component Analysis (PCA)0s[Activity] PCA Example with the Iris data set0sData Warehousing Overview: ETL and ELT0sReinforcement Learning0s[Activity] Reinforcement Learning & Q-Learning with Gym0sUnderstanding a Confusion Matrix0sMeasuring Classifiers (Precision, Recall, F1, ROC, AUC)0s
-
Dealing with Real-World DataBias/Variance Tradeoff0s[Activity] K-Fold Cross-Validation to avoid overfitting0sData Cleaning and Normalization0s[Activity] Cleaning web log data0sNormalizing numerical data0s[Activity] Detecting outliers0sFeature Engineering and the Curse of Dimensionality0sImputation Techniques for Missing Data0sHandling Unbalanced Data: Oversampling, Undersampling, and SMOTE0sBinning, Transforming, Encoding, Scaling, and Shuffling0s
-
Apache Spark: Machine Learning on Big DataWarning about Java 21+ and Spark 3!Spark installation notes for MacOS and Linux users[Activity] Installing Spark0sSpark Introduction0sSpark and the Resilient Distributed Dataset (RDD)0sIntroducing MLLib0sIntroduction to Decision Trees in Spark0s[Activity] K-Means Clustering in Spark0sTF / IDF0s[Activity] Searching Wikipedia with Spark0s[Activity] Using the Spark DataFrame API for MLLib0s
-
Experimental Design / ML in the Real World
-
Deep Learning and Neural NetworksDeep Learning Pre-Requisites0sThe History of Artificial Neural Networks0s[Activity] Deep Learning in the Tensorflow Playground0sDeep Learning Details0sIntroducing Tensorflow0s[Activity] Using Tensorflow, Part 10s[Activity] Using Tensorflow, Part 20s[Activity] Introducing Keras0s[Activity] Using Keras to Predict Political Affiliations0sConvolutional Neural Networks (CNN’s)0s[Activity] Using CNN’s for handwriting recognition0sRecurrent Neural Networks (RNN’s)0s[Activity] Using a RNN for sentiment analysis0sTuning Neural Networks: Learning Rate and Batch Size Hyperparameters0sDeep Learning Regularization with Dropout and Early Stopping0sThe Ethics of Deep Learning0s
-
Generative ModelsVariational Auto-Encoders (VAE’s) – how they work0sVariational Auto-Encoders (VAE) – Hands-on with Fashion MNIST0sGenerative Adversarial Networks (GAN’s) – How they work0sGenerative Adversarial Networks (GAN’s) – Playing with some demos0sGenerative Adversarial Networks (GAN’s) – Hands-on with Fashion MNIST0sLearning More about Deep Learning0s
-
Generative AI: GPT, ChatGPT, Transformers, Self Attention Based Neural NetworksThe Transformer Architecture (encoders, decoders, and self-attention.)0sSelf-Attention, Masked Self-Attention, and Multi-Headed Self Attention in depth0sApplications of Transformers (GPT)0sHow GPT Works, Part 1: The GPT Transformer Architecture0sHow GPT Works, Part 2: Tokenization, Positional Encoding, Embedding0sFine Tuning / Transfer Learning with Transformers0s[Activity] Tokenization with Google CoLab and HuggingFace0s[Activity] Positional Encoding0s[Activity] Masked, Multi-Headed Self Attention with BERT, BERTViz, and exBERT0s[Activity] Using small and large GPT models within Google CoLab and HuggingFace0s[Activity] Fine Tuning GPT with the IMDb dataset0sFrom GPT to ChatGPT: Deep Reinforcement Learning, Proximal Policy Gradients0sFrom GPT to ChatGPT: Reinforcement Learning from Human Feedback and Moderation0s
-
The OpenAI API (Developing with GPT and ChatGPT)[Activity] The OpenAI Chat Completions API0s[Activity] Using Tools and Functions in the OpenAI Chat Completion API0s[Activity] The Images (DALL-E) API in OpenAI0s[Activity] The Embeddings API in OpenAI: Finding similarities between words0sThe Legacy Fine-Tuning API for GPT Models in OpenAI0s[Demo] Fine-Tuning OpenAI’s Davinci Model to simulate Data from Star Trek0sThe New OpenAI Fine-Tuning API; Fine-Tuning GPT-3.5 to simulate Commander Data!0s[Activity] The OpenAI Moderation API0s[Activity] The OpenAI Audio API (speech to text)0s
-
Retrieval Augmented Generation (RAG,) Advanced RAG, and LLM Agents
-
Final Project
-
You made it!
Master Machine Learning & AI Engineering — From Data Analytics to Agentic AI Solutions
Launch your career in AI with a comprehensive, hands-on course that takes you from beginner to advanced. Learn Python, data science, classical machine learning, and the latest in AI engineering—including generative AI, transformers, and LLM agents / agentic AI.
Why This Course?
Learn by Doing
With over 145 lectures and 21+ hours of video content, this course is built around practical Python projects and real-world use cases—not just theory.
Built for the Real World
Learn how companies like Google, Amazon, and OpenAI use AI to drive innovation. Our curriculum is based on skills in demand from leading tech employers.
No Experience? No Problem
Start from scratch with beginner-friendly lessons in Python and statistics. By the end, you’ll be building intelligent systems with cutting-edge AI tools.
A Structured Path from Beginner to AI Engineer
1. Programming Foundations
Start with a crash course in Python, designed for beginners. You’ll learn the language fundamentals needed for data science and AI work.
2. Data Science and Statistics
Build a solid foundation in data analysis, visualization, descriptive and inferential statistics, and feature engineering—essential skills for working with real-world datasets.
3. Classical Machine Learning
Explore supervised and unsupervised learning, including linear regression, decision trees, SVMs, clustering, ensemble models, and reinforcement learning.
4. Deep Learning with TensorFlow and Keras
Understand neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), using real code examples and exercises.
5. Advanced AI Engineering and Generative AI
Go beyond traditional ML to learn the latest AI tools and techniques:
Transformers and self-attention mechanisms
GPT, ChatGPT, and the OpenAI API
Fine-tuning foundation models
Advanced Retrieval-Augmented Generation (RAG)
LangChain and LLM agents
Designing and building multi-agent systems with the OpenAI Agents SDK
Real-world GenAI projects and deployment strategies
6. Big Data and Apache Spark
Learn how to scale machine learning to large datasets using Spark, and apply ML techniques on distributed computing clusters.
Designed for Career Growth
Whether you’re a programmer looking to pivot into AI or a tech professional seeking to expand your skills, this course delivers a complete, industry-relevant education. Concepts are explained clearly, in plain English, with a focus on applying what you learn.
What Students Are Saying
“I started doing your course… and it was pivotal in helping me transition into a role where I now solve corporate problems using AI. Your course demystified how to succeed in corporate AI research, making you the most impressive instructor in ML I’ve encountered.”
— Kanad Basu, PhD
Enroll Today and Build Your Future in AI
Join thousands of learners who have used this course to land jobs, lead projects, and build real AI applications. Stay ahead in one of the fastest-growing fields in tech.
Start your journey today—from Python beginner to AI engineer.
What's included
- 21 hours on-demand video
- 6 articles
- 16 downloadable resources
- Access on mobile and TV
- Certificate of completion