Book Image

AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide

By : Somanath Nanda, Weslley Moura
Book Image

AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide

By: Somanath Nanda, Weslley Moura

Overview of this book

The AWS Certified Machine Learning Specialty exam tests your competency to perform machine learning (ML) on AWS infrastructure. This book covers the entire exam syllabus using practical examples to help you with your real-world machine learning projects on AWS. Starting with an introduction to machine learning on AWS, you'll learn the fundamentals of machine learning and explore important AWS services for artificial intelligence (AI). You'll then see how to prepare data for machine learning and discover a wide variety of techniques for data manipulation and transformation for different types of variables. The book also shows you how to handle missing data and outliers and takes you through various machine learning tasks such as classification, regression, clustering, forecasting, anomaly detection, text mining, and image processing, along with the specific ML algorithms you need to know to pass the exam. Finally, you'll explore model evaluation, optimization, and deployment and get to grips with deploying models in a production environment and monitoring them. By the end of this book, you'll have gained knowledge of the key challenges in machine learning and the solutions that AWS has released for each of them, along with the tools, methods, and techniques commonly used in each domain of AWS ML.
Table of Contents (14 chapters)
1
Section 1: Introduction to Machine Learning
4
Section 2: Data Engineering and Exploratory Data Analysis
9
Section 3: Data Modeling

Questions

  1. You are working as a lead data scientist for a retail company. Your team is building a regression model and using the linear learner built-in algorithm to predict the optimal price of a particular product. The model is clearly overfitting to the training data and you suspect that this is due to the excessive number of variables being used. Which of the following approaches would best suit a solution that addresses your suspicion?

    a) Implementing a cross-validation process to reduce overfitting during the training process.

    b) Applying L1 regularization and changing the wd hyperparameter of the linear learner algorithm.

    c) Applying L2 regularization and changing the wd hyperparameter of the linear learner algorithm.

    d) Applying L1 and L2 regularization.

    Answers

    C, This question prompts about to the problem of overfitting due an excessive number of features being used. L2 regularization, which is available in linear learner through the wd hyperparameter, will work as a feature...