What this book covers
Chapter 1, Machine Learning Fundamentals, covers some machine learning definitions, different types of modeling approaches, and all the steps necessary to build a machine learning product, known as the modeling pipeline.
Chapter 2, AWS Application Services for AI/ML, covers details of the various AI/ML applications offered by AWS, which you should know to pass the exam.
Chapter 3, Data Preparation and Transformation, deals with categorical and numerical features, applying different techniques to transform your data, such as one-hot encoding, binary encoding, ordinal encoding, binning, and text transformations. You will also learn how to handle missing values and outliers on your data, two important topics to build good machine learning models.
Chapter 4, Understanding and Visualizing Data, teaches you how to select the most appropriate data visualization technique according to different variable types and business needs. You will also learn about available AWS services for visualizing data.
Chapter 5, AWS Services for Data Storing, teaches you about AWS services used to store data for machine learning. You will learn about the many different S3 storage classes and when to use each of them. You will also learn how to handle data encryption and how to secure your data at rest and in transit. Finally, we will present other types of data store services, still worth knowing for the exam.
Chapter 6, AWS Services for Processing, teaches you about AWS services used to process data for machine learning. You will learn how to deal with batch and real-time processing, how to directly query data on Amazon S3, and how to create big data applications on EMR.
Chapter 7, Applying Machine Learning Algorithms, covers different types of machine learning tasks, such as classification, regression, clustering, forecasting, anomaly detection, text mining, and image processing. Each of these tasks has specific algorithms that you should know about to pass the exam. You will also learn how ensemble models work and how to deal with the curse of dimensionality.
Chapter 8, Evaluating and Optimizing Models, teaches you how to select model metrics to evaluate model results. You will also learn how to optimize your model by tuning its hyperparameters.
Chapter 9, Amazon SageMaker Modeling, teaches you how to spin up notebooks to work with exploratory data analysis and how to train your models on Amazon SageMaker. You will learn where and how your training data should be stored in order to be accessible through SageMaker and the different data formats that you can use.