Overview
Test your understanding of machine learning fundamentals with questions on supervised/unsupervised learning, algorithms, evaluation metrics, and core concepts essential for AI applications.
Syllabus
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- Introduction to Machine Learning
-- Overview of Machine Learning concepts
-- Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
- Supervised Learning Techniques
-- Regression Algorithms
-- Classification Algorithms
-- Performance Metrics: Accuracy, Precision, Recall, F1 Score
- Unsupervised Learning Techniques
-- Clustering Algorithms
-- Dimensionality Reduction techniques
-- Applications in real-world scenarios
- AWS Machine Learning Services
-- Amazon SageMaker overview
-- Key features and workflows in SageMaker
- Model Evaluation and Validation
-- Understanding Overfitting and Underfitting
-- Cross-Validation techniques
- Practical Considerations in Machine Learning
-- Data Preprocessing and Feature Engineering
-- Hyperparameter Tuning
-- Model Deployment strategies
- Conclusion and Resources
-- Advanced topics in Machine Learning
-- Recommended resources for further learning in AWS and Machine Learning
Taught by
Tags
On-Demand
