Understanding Machine Learning Algorithms Through Examples

Overview

Join our session on Understanding Machine Learning Algorithms Through Examples and enhance your skills in AI. This course provides an in-depth exploration of widely-used machine learning algorithms, illustrated through real-world industry examples.

Discover expert techniques on selecting the most suitable algorithm for various tasks, and experience the power of Azure Machine Learning solutions to automate processes effortlessly, all without the need for coding.

Hosted by renowned provider YouTube, this event is ideal for those looking to deepen their knowledge in artificial intelligence and for professionals eager to leverage AI tools for increased productivity. Don't miss this opportunity to stay at the forefront of AI innovation, with insights from leading industry figures and cutting-edge technologies.

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

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Subjects

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