Overview
Master the essential machine learning foundations needed for AI engineering in this comprehensive 35-minute guide covering ML techniques, deep learning, neural networks, and reinforcement learning.
Syllabus
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- Introduction to Machine Learning
-- Overview of Machine Learning and its Importance in AI
-- Key Concepts: Supervised, Unsupervised, and Reinforcement Learning
- Supervised Learning
-- Fundamentals of Regression and Classification
-- Common Algorithms: Linear Regression, Logistic Regression, Decision Trees
-- Evaluation Metrics: Accuracy, Precision, Recall, F1 Score
- Unsupervised Learning
-- Clustering Techniques
-- Dimensionality Reduction
-- Applications and Use Cases
- Introduction to Deep Learning
-- Understanding Neural Networks
-- Activation Functions and Layers
-- Architecture: Feedforward Neural Networks
- Advanced Deep Learning Concepts
-- Convolutional Neural Networks (CNNs) for Image Processing
-- Recurrent Neural Networks (RNNs) for Sequential Data
- Reinforcement Learning Basics
-- Concepts of Agents, Actions, Rewards, and Environments
-- Q-Learning and Policy Gradients
- Conclusion
-- Integration of Machine Learning Techniques in AI Engineering
-- Future Trends in ML and AI
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