Machine Learning, Data Science & Generative AI with Python
A complete hands-on program covering machine learning, data science, deep learning, and cutting-edge generative AI with Python, TensorFlow, GPT, OpenAI tools, and modern neural networks.
What You’ll Learn
Build artificial neural networks using TensorFlow and Keras
Apply machine learning at scale with Apache Spark MLLib
Classify images, text, and sentiment using deep learning models
Build predictive models using linear, polynomial, and multivariate regression
Visualize data with MatPlotLib and Seaborn
Understand reinforcement learning and build a Pac-Man AI agent
Use ML algorithms such as K-Means, SVM, KNN, Decision Trees, Naive Bayes, and PCA
Apply training methods like train/test splits and K-Fold cross-validation
Create a movie recommender system with collaborative filtering
Clean and preprocess datasets and handle outliers effectively
Design and evaluate A/B tests using T-tests and p-values
Requirements
Desktop computer (Windows, Mac, or Linux) capable of running Anaconda 3+
Some prior coding/scripting experience
High school–level mathematics
Course Description
Machine Learning, Data Science & Generative AI with Python is your complete pathway into the world of modern AI. This immersive, hands-on course teaches you how today’s most advanced ML and AI systems work—and how to build them yourself.
From foundational statistics to cutting-edge AI architectures, you’ll learn how companies like Google, Amazon, and Meta leverage data and machine learning to drive insight and innovation. With over 145 lectures and 20+ hours of guided instruction, you’ll develop real, practical expertise in Python-based ML workflows, deep learning, and generative AI.
Why This Course Stands Out
Latest Generative AI content: Learn transformers, GPT, ChatGPT, OpenAI API, advanced RAG pipelines, LangChain, LLM agents, and self-attention networks.
Job-aligned curriculum: Designed from real hiring data and skill expectations across major tech companies.
Hands-on learning: Every concept is paired with practical Python examples and real-world exercises.
Wide technical coverage: Neural networks, TensorFlow, Keras, clustering, reinforcement learning, SVMs, Spark, image recognition, and more.
Beginner-friendly explanations: Complex ideas are broken down in plain English—no unnecessary mathematical overhead.
Course Highlights
Python crash course for those needing a quick ramp-up
Deep learning with MLPs, CNNs, RNNs, and modern architectures
Practical generative AI development using OpenAI APIs, GPT models, transformers, advanced RAG, LangChain, and agent frameworks
In-depth machine learning fundamentals beyond GenAI—regression, clustering, feature engineering, tree-based models, and parameter tuning
Real-world data science workflows: visualization, preprocessing, modeling, evaluation
Big data integration using Apache Spark, enabling scalable ML across compute clusters
Who This Course Is For
Software developers transitioning into data science, machine learning, or AI engineering
Technologists who want to understand how deep learning really works
Data analysts moving from spreadsheet tools to Python-based analysis
Anyone seeking a structured, practical introduction to ML, deep learning, and generative AI
If you have no coding experience, take an introductory Python course first.