Machine Learning Courses

Analyze, clean, and visualize data using R and SQL to perform end-to-end statistical analysis and communicate insights effectively.

Design, build, and automate reliable data pipelines with Python, SQL, and cloud-ready tooling for production workloads.

Analyze complex datasets and build predictive models by applying statistics and machine learning to deliver end-to-end data science solutions.

Train predictive models in Python, evaluate performance, and apply machine learning to real datasets for insights.

Learn to build Retrieval-Augmented Generation (RAG) systems in Python, covering pipeline architecture, prompt design, query expansion, reranking, and debugging common failure modes.

Learn how embeddings capture semantic meaning beyond keywords and power modern AI systems including search, RAG, and agent memory.

Learn how vector databases enable fast semantic search at scale. Build production-ready systems with ChromaDB, implement hybrid search strategies, and explore caching patterns for LLM applications.

Learn to build reliable LLM systems with structured outputs, function calling, and tool integration. Move beyond basic prompting to create maintainable workflows using validation, agentic loops, and the Model Context Protocol.

Explore how PyTorch is used across major deep learning application areas including sequence models, natural language processing, and computer vision.

Design and refine convolutional neural network models for computer vision by training, regularizing, and fine-tuning CNN architectures on image data.

Model sequential data by building and evaluating RNN, GRU, and LSTM architectures for time-series forecasting and sequence prediction tasks.

Process and model text data by applying NLP techniques such as tokenization, embeddings, sequence models, and transformers to build deep learning solutions.

Apply linear regression in R to build, interpret, and evaluate predictive models, understanding when linear assumptions hold and fail.

Implement core machine learning workflows in R using k-nearest neighbors, error metrics, and cross-validation to build reliable models.

Work with Apache Spark to process massive datasets using RDDs, DataFrames, and Spark SQL across distributed environments.

Explore deep learning with PyTorch by training, regularizing, and evaluating neural networks designed to generalize well on real data.

Optimize machine learning models by implementing and applying gradient descent techniques to efficiently train and improve predictive performance.

Classify and interpret categorical outcomes by constructing, evaluating, and applying logistic regression models for inference and prediction.