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AI coding agents can now do much more than autocomplete code. Learn how to use them for planning, implementation, testing, debugging, and code review while keeping developers in control of quality and security.
Build a practical semantic search system with Python and Sentence Transformers. Learn how text becomes embeddings, compare queries with documents, rank results by similarity, and turn the prototype into a reusable search component.
Learn how real data science starts before machine learning. Work with a messy sales dataset in Python, clean missing and inconsistent values, explore patterns with pandas, visualize business trends, and turn raw CSV data into practical recommendations.
Build a real sentiment analysis application with Python and Hugging Face Transformers. Learn how pretrained NLP models work, run inference locally, interpret confidence scores, process multiple reviews, expose the model through an API, and understand when sentiment models can fail.
Build your first machine learning project with Python by turning a small dataset into a working prediction model, from data preparation and feature selection to training, evaluation, and saving the model.
Learn how to evaluate LLM applications using practical datasets, quality criteria, automated checks, human review, and regression testing
Understand the difference between RAG and fine-tuning, how each approach works, their advantages and limitations, and when developers should use one or combine both.
Follow a practical machine learning roadmap for 2026, covering Python, mathematics, data analysis, machine learning algorithms, deep learning, projects, deployment, and job-ready skills.
Learn what generative AI is, how it works, how models generate text, images, code, and other content, and how beginners can start learning and using generative AI.
