Master Machine Learning, Deep Learning, LLMs & AI Systems with hands-on, real-world projects
Sub Category
- Data Science
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Objectives
- Build and evaluate Machine Learning models using regression, classification, clustering, and ensemble techniques with proper validation and optimization.
- Design, train, and debug Deep Learning models including fully connected networks, CNNs, and sequence models (RNNs, LSTMs, GRUs) using PyTorch or TensorFlow.
- Understand and implement Transformer-based Large Language Models (LLMs), including attention, embeddings, tokenization, and fine-tuning concepts.
- Create production-ready Generative AI applications using prompt engineering, embeddings, semantic search, and Retrieval-Augmented Generation (RAG).
- Develop agentic AI systems that perform multi-step reasoning, tool calling, and task execution with memory and control mechanisms.
- Apply AI engineering best practices such as feature engineering, model optimization, reproducibility, cost control, evaluation, and performance tuning.
- Integrate AI models into real applications by designing full-stack architectures that connect backends, APIs, and user interfaces with AI systems
Pre Requisites
- Basic Python programming knowledge
- Curiosity to understand how AI works under the hood
- No prior experience in Machine Learning, Deep Learning, or Generative AI is required — everything is explained from first principles to production.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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Coupon Code(s)