ML Foundations for AI Engineers

Overview

Unlock the essentials of machine learning, tailored for AI engineers, in a concise 35-minute session available on YouTube. This comprehensive guide walks you through critical ML techniques, including deep learning, neural networks, and the basics of reinforcement learning.

Ideal for both novices and professionals looking to strengthen their AI skillset, this resource will equip you with the foundational knowledge necessary for success in AI engineering. Whether you're part of a computer science audience or specifically interested in artificial intelligence courses, this guide serves as a valuable asset for your educational journey.

Syllabus

• Introduction to Machine Learning
Overview of ML in AI Engineering
Key Concepts and Terminology
• Machine Learning Techniques
Supervised Learning
Unsupervised Learning
Semi-supervised Learning
Model Evaluation and Selection
• Deep Learning Foundations
Introduction to Neural Networks
Common Architectures (CNNs, RNNs)
Training Deep Learning Models
Overfitting and Regularization
• Neural Networks
Structure of Neural Networks
Activation Functions
Backpropagation and Optimization
• Reinforcement Learning Basics
Key Concepts of Reinforcement Learning
Action, State, and Reward Framework
Exploration vs. Exploitation Trade-off
• Wrap-Up and Further Resources
Summary of Key Takeaways
Suggested Readings and Tools for Further Learning

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Subjects

Computer Science