Pearson IT Certification – AWS Certified Machine Learning – Specialty (Video Course) 2024-4

AWS Certified Machine Learning Course – Specialty (Video Course). This course will help you succeed in the AWS Certified Machine Learning – Specialty exam by learning techniques and doing practical exercises. Earning an AWS Machine Learning Specialist certification will highlight your skills as a machine learning engineer. Usually, machine learning engineers focus on data management and model building, but if you can also use cloud tools, you will be more valuable as an MLOps engineer. In this course, you’ll learn how to get your data, perform the feature engineering process, train and evaluate models, and make them available to consumers for use. This course shows that you have full-stack machine learning development skills.

The instructor of this course, Mylesia McGregor, will provide a combination of slides and hands-on demonstrations in AWS, along with examples in Visual Studio with Python. This course is designed to help you pass the exam. This course includes an overview of concepts along with hands-on practice using AWS tools such as Kinesis and EMR.

What you will learn:

• Effective tips and methods for passing the AWS machine learning exam
• Familiarization and implementation of data receiving solutions with Kinesis
• Evaluation of machine learning models
• Deploying machine learning models with AWS tools

This course is suitable for people who:

• They are machine learning engineers.
• Development Operations Engineer (DevOps).
• Looking to become a machine learning engineer.

Course specifications AWS Certified Machine Learning – Specialty (Video Course)

• Publisher: Pearsonitcertification
• Instructor: Milecia McGregor
• Education level: Intermediate
• Training duration: 4 hours and 27 minutes

Course headings

Introduction

Lesson 1: Data Engineering

1.1 Create data repositories for machine learning

1.2 Identify and implement a data ingestion solution

1.3 Decide between ingestion tools

1.4 Identify and implement a data transformation solution

1.5 Get some practice: questions and exercises

Lesson 2: Exploratory Data Analysis

2.1 Sanitize and prepare data for modeling

2.2 Perform feature engineering

2.3 Analyze data for machine learning

2.4 Visualize data for machine learning

2.5 Get some practice: questions and exercises

Lesson 3: Training Models

3.1 Frame business problems as machine learning problems

3.2 Select the appropriate model for a machine learning problem

3.3 Understand the intuition behind the model

3.4 Train machine learning models

3.5 Choose compute option

3.6 Get some practice: questions and exercises

Lesson 4: Evaluating Models

4.1 Perform hyperparameter optimization

4.2 Use other methods for hyperparameter optimization

4.3 Evaluate machine learning models

4.4 Compare models with different metrics

4.5 Implement machine learning best practices

4.6 Get some practice: questions and exercises

Lesson 5: Machine Learning Implementation and Operations

5.1 Build machine learning solutions for production

5.2 Address scaling concerns

5.3 Recommend and implement the appropriate machine learning services

5.4 Apply basic AWS security practices to machine learning solutions

5.5 Deploy and operationalize machine learning solutions

5.6 Get some practice: questions and exercises

Summary

Prerequisites of AWS Certified Machine Learning – Specialty (Video Course)

• Prerequisites: Knowledge of how to use various AWS tools to deploy ML models into different environments
• Knowledge of data engineering principles and model training and evaluation

Course images

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