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IBM AI Engineering

A comprehensive guide to building Machine learning, Generative AI models

IBM AI Engineering Series

Welcome to the IBM AI Engineering Series! This series covers everything you need to know about building LLM, its architectures, data preparation, PyTorch, TensorFlow, and Machine learning.

What's Covered

  1. Machine Learning - Understanding traditional ML algorithms from scratch to develop mental model for AI learning.
  2. Introduction to Deep Learning & Neural Networks with Keras - Hands-on guide to develop deep learning models using Keras.
  3. Deep Learning with Keras and TensorFlow - Hands-on guide to develop advance deep learning models such as CNN, RNN using Keras and TensorFlow.
  4. Intoduction to Neural Networks and PyTorch - Hands-on guide to develop basic deep learning models using PyTorch.
  5. Deep learning with PyTorch - Hands-on guide to develop advance deep learning models such as CNN, RNN using PyTorch.
  6. AI Capstone Project with Deep Learning - Hands-on guide to an AI project with Deep Learning to provide industry standard practice experience.
  7. Generative AI and LLMs: Architecture and Data Preparation - Understanding LLM architecture and data preparation concept and techniques for LLM.
  8. Gen AI Foundational Models for NLP and Language understanding - Understanding foundational models and anatomy of the models for NLP and Language understanding use cases.
  9. Generative AI Language Modeling with Transformers - Understanding how transformer shaped language models.
  10. Generative AI Engineering and Fine-tuning transformers - A guide to develop transformer based language models and fine-tune them.
  11. Generative AI Advance Fine-tuning for LLMs - A guide to fine-tune foundational models with advance techniques to adapt for a domain specific use case.
  12. Fundamentals of AI Agents using RAG and LangChain - A guide to develop RAG and Agentic AI systems and hands-on LangChain.
  13. Project: Generative AI Applications with RAG and LangChain - A hands-on project following industry standard practices and procedures.

Prerequisites

This series assumes you have:

  • Basic understanding Linear algebra, Calculus and Statistics
  • Python programming experience

Start your journey into AI Engineering!

Posts in this series