Build foundational Python programming skills required for AI and machine learning development. Topics include Python syntax, variables, data types, operators, data structures, loops, functions, object-oriented programming, and file handling. This AI ML Course also introduces AI-assisted coding practices, including prompt-based coding and debugging.

Develop core data science capabilities using Python. Learners explore NumPy, linear algebra, statistics, probability distributions, and data analysis techniques. This machine learning and artificial intelligence course also covers working with Pandas, data wrangling, and visualization to analyze datasets and apply statistical concepts in real-world business scenarios.

Learn the fundamentals of machine learning and how models are used to solve real-world problems. This course covers supervised learning techniques such as regression and classification, ensemble learning methods, and unsupervised learning techniques including clustering, dimensionality reduction, anomaly detection, and recommendation systems.

Explore deep learning techniques used to build advanced AI systems. Topics include artificial neural networks, deep neural networks, TensorFlow, Keras and PyTorch frameworks, convolutional neural networks, transfer learning, object detection, recurrent neural networks, transformer models for natural language processing, and autoencoders.

Understand the fundamentals of generative AI and its real-world applications. This Artificial Intelligence course introduces generative AI tools, prompt engineering basics, responsible AI considerations such as security and bias. Learners also explore various Gen AI use cases.

Gain a deeper understanding of generative AI architectures that power modern generative AI systems. Learners explore transformers, attention mechanisms, generative adversarial networks (GANs), and variational autoencoders (VAEs). The course also focuses on developing generative AI applications, fine-tuning models, and exploring emerging techniques in generative AI.

Learn how machine learning models are deployed, managed, and maintained in production environments. This artificial intelligence and machine learning course introduces the ML lifecycle, experiment tracking, data and model versioning, building machine learning pipelines, model deployment, monitoring, and automation of ML workflows using CI/CD practices.

Apply the skills learned across Python, data science, machine learning, deep learning, and generative AI to real-world projects. Guided by industry experts, you will work on capstone projects such as vehicle detection or retail sales forecasting, helping you build practical experience and a portfolio-ready project that demonstrates job-ready AI skills.

A 3-part, hands-on AI workshop series where you will build with Claude Code for workflows and coding, design product-grade UI using Claude with Figma MCP, and automate end-to-end execution by deploying your own AI assistant with OpenClaw.