| Topic | Details |
| Topic 1 | - Exploratory Data Analysis: This topic covers sanitizing and preparing data for modeling and performing feature engineering. Additionally, it discusses analyzing and visualizing data for ML.
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| Topic 2 | - Machine Learning Implementation and Operations: Building ML solutions for performance, availability, scalability, resiliency, and fault tolerance is discussed in this topic. It also focuses on suitable ML services and features for a given problem. Lastly, the topic delves into applying basic AWS security practices to ML solutions and deploying and operationalizing ML solutions.
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| Topic 3 | - Data Engineering: It discusses creating data repositories for ML, identifying and implementing a data ingestion solution. Lastly, the topic delves into identifying and implementing a data transformation solution.
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| Topic 4 | - Modeling: The topic of modeling deals with framing business problems as ML problems, choosing the suitable model(s) for a given ML problem, training ML models. It also discusses hyperparameter optimization and evaluation of ML models.
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