Mastering Machine Learning on AWS
AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud.
As you go through the chapters, you’ll gain insights into how these algorithms can be trained, tuned, and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics and predictive modeling through to sentiment analysis.
By the end of this book, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS.
|Course Length||9 hours 10 minutes|
|Date Of Publication||20 May 2019|
|How AWS empowers data scientists|
|Identifying candidate problems that can be solved using machine learning|
|Machine learning project life cycle|
|Predicting the price of houses|
|Understanding linear regression|
|Evaluating regression models|
|Implementing linear regression through scikit-learn|
|Implementing linear regression through Apache Spark|
|Implementing linear regression through SageMaker's linear Learner|
|Understanding logistic regression|
|Pros and cons of linear models|
|TensorFlow as a general machine learning library|
|Training and serving the TensorFlow model through SageMaker|
|Creating a custom neural net with TensorFlow|