Published on: 2024-08-31 21:44:36
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Associate AI Engineer for Data Scientists, Begin building AI solutions and gain the career-building skills you need to succeed as an AI Engineer, from model development to deployment into production! In this track, you’ll train and evaluate robust predictive models on real-world datasets across a variety of domains. Starting with the fundamentals of machine learning, you’ll get hands-on with some of the most popular Python libraries for machine learning and deep learning, including scikit-learn, PyTorch, and many more. As you progress, you’ll get hands-on with Large Language Models (LLMs) for a variety of natural language tasks. You’ll learn to fine-tune Llama 3 on custom data and integrate this into a LangChain application to begin surfacing predictions to end-users. Finally, you’ll discover what it takes to move an AI model from a notebook into production. You’ll build foundational skills in MLOps, including testing, version control, and monitoring performance in production.
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Subtitle : English
Quality: 720p
Intermediate Deep Learning with PyTorch
Introduction to Deep Learning with PyTorch
Introduction to LLMs in Python
Introduction to Testing in Python
MLOps Concepts
MLOps Deployment and Life Cycling
Monitoring Machine Learning Concepts
Responsible AI Data Management
Software Engineering Principles in Python
Supervised Learning with scikit-learn
Unsupervised Learning in Python
Working with Llama 3
introduction-to-git
1.14 GB
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