Understanding Machine Learning Algorithms Through Examples

Overview

Explore common machine learning algorithms and practices through industry examples, learn to choose the right algorithm, and discover Azure ML solutions for automating tasks without coding.

Syllabus

• Introduction to Machine Learning
Overview of Machine Learning Concepts
Importance and Applications in Industry
Introduction to Azure ML Platform
• Supervised Learning Algorithms
Linear Regression
Case Study: Predicting Housing Prices
Implementation in Azure
Decision Trees
Case Study: Credit Risk Assessment
Implementation in Azure
Support Vector Machines
Case Study: Text Classification
Implementation in Azure
• Unsupervised Learning Algorithms
Clustering (k-Means)
Case Study: Customer Segmentation
Implementation in Azure
Principal Component Analysis (PCA)
Case Study: Dimensionality Reduction for Image Data
Implementation in Azure
• Reinforcement Learning
Introduction to Reinforcement Learning Concepts
Case Study: Dynamic Pricing Strategies
Implementation in Azure
• Model Evaluation and Selection
Accuracy, Precision, and Recall
ROC Curves and AUC
Cross-validation Techniques
• Choosing the Right Algorithm
Understanding Data Types and Characteristics
Trade-offs and Considerations (Bias-Variance Trade-off)
Practical Guidelines for Algorithm Selection
• Automation of Machine Learning Tasks
Introduction to Automated ML Solutions on Azure
Setting Up Automated ML Experiments
Best Practices for Automating ML Workflows
• Hands-On Labs and Projects
Exercise: Building a Simple Prediction Model
Project: End-to-End Machine Learning Pipeline with Azure
• Closing and Next Steps
Review of Learned Concepts
Resources for Continued Learning

Subjects

Conference Talks