AWSDD-MLBAWAmazon Web ServicesAW-AWSDD-MLB1.0<p>During this event, you will learn:</p> <ul> <li>What is Machine Learning?</li><li>What is the machine learning pipeline, and what are its phases?</li><li>What is the difference between supervised and unsupervised learning?</li><li>What is reinforcement learning?</li><li>What is deep learning?</li></ul><p>This event is intended for:</p> <ul> <li>Developers</li><li>Solution architects</li><li>Data engineers</li><li>Individuals interested in building solutions with machine learning - no machine learning experience required!</li></ul><h5>Section 1: Machine learning basics </h5><ul> <li>Classical programming vs. machine learning approach</li><li>What is a model?</li><li>Algorithm features, weights, and outputs</li><li>Machine learning algorithm categories</li><li>Supervised algorithms</li><li>Unsupervised algorithms</li><li>Reinforcement learning</li></ul><h5>Section 2: What is deep learning?</h5><ul> <li>How does deep learning work?</li><li>How deep learning is different</li></ul><h5>Section 3: The Machine Learning Pipeline</h5><ul> <li>Overview</li><li>Business problem</li><li>Data collection and integration</li><li>Data processing and visualization</li><li>Feature engineering</li><li>Model training and tuning</li><li>Model evaluation</li><li>Model deployment</li></ul><h5>Section 4: What are my next steps?</h5><ul> <li>Resources to continue learning</li></ul>During this event, you will learn: - What is Machine Learning? - What is the machine learning pipeline, and what are its phases? - What is the difference between supervised and unsupervised learning? - What is reinforcement learning? - What is deep learning?This event is intended for: - Developers - Solution architects - Data engineers - Individuals interested in building solutions with machine learning - no machine learning experience required!Section 1: Machine learning basics - Classical programming vs. machine learning approach - What is a model? - Algorithm features, weights, and outputs - Machine learning algorithm categories - Supervised algorithms - Unsupervised algorithms - Reinforcement learning Section 2: What is deep learning? - How does deep learning work? - How deep learning is different Section 3: The Machine Learning Pipeline - Overview - Business problem - Data collection and integration - Data processing and visualization - Feature engineering - Model training and tuning - Model evaluation - Model deployment Section 4: What are my next steps? - Resources to continue learning2 hours