Build Your Own AI Systems & Models

Complete this path and you'll:

• Build a strong foundation in python, the primary programming language used for AI, machine learning, and data science
• Understand how machine learning, deep learning, generative AI, and large language models work under the hood
• Prepare, clean, analyze, and transform real-world data so it can be used to train reliable AI models
• Train, evaluate, tune, and compare machine learning models using the right techniques and performance metrics
• Build neural networks and deep learning models using industry-standard frameworks like PyTorch
• Create computer vision, natural language processing, and transformer-based systems that solve real-world problems
• Use the Hugging Face ecosystem to work with pretrained models, customize them for specific tasks, and deploy them into applications
• Prepare custom datasets and fine-tune foundation models to produce better results for specialized use cases
• Build production-ready AI applications using LLM APIs, LangChain, LangGraph, LangSmith, and other modern AI engineering tools
• Make AI systems more accurate and useful by connecting LLMs to private, current, and domain-specific data with Retrieval-Augmented Generation (RAG)
• Design and deploy AI agents and multi-agent systems that can use tools, complete tasks, and automate complex workflows
• Build the data pipelines and infrastructure needed to move, process, and prepare data for production AI systems
• Deploy and scale AI models and applications using AWS Bedrock, SageMaker, Microsoft Foundry, and other cloud platforms
• Run and deploy open-source AI models using local hardware, cloud GPUs, and self-hosted infrastructure
• Use AI throughout the development process to write better code, debug problems, review quality, and work more efficiently
• Build a portfolio of production-level AI projects that proves you can take an AI system from raw data and initial idea to a deployed product