Machine Learning Techniques & Applications

Overview

Learn to implement both supervised and unsupervised machine learning algorithms on benchmark datasets through this comprehensive 40-hour course. Master the analysis of model performance metrics and develop skills to select the most appropriate ML techniques for specific problem domains. Gain hands-on experience implementing deep learning and generative models for computer vision and natural language processing applications. Practice evaluating algorithm effectiveness across different datasets and understand when to apply various machine learning approaches based on problem requirements and data characteristics.

Syllabus

Implement supervised and unsupervised ML algorithms on benchmark datasets.
Analyze model performance and select suitable ML techniques for specific problems.
Demonstrate use of deep learning or generative models in vision or NLP applications.
Implement supervised and unsupervised ML algorithms on benchmark datasets.
Analyze model performance and select suitable ML techniques for specific problems.
Demonstrate use of deep learning or generative models in vision or NLP applications.