English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 36 Lessons (4h 27m) | 622 MB
Learn the techniques and approaches to successfully pass the AWS Certified Machine Learning – Specialty Exam with hands-on exercises.
Getting the AWS Certified Machine Learning – certification highlights your versatility as an ML engineer. Usually, ML engineers focus on handling data and building models, so if you know can use cloud tools, it makes you an even more valuable as an MLOps engineer. Youll be able to ingest your own data, get through the feature engineering process, train and evaluate models, and deploy them to where they will be consumed. This certification shows that you know how to do full-stack ML development.
In this series of videos, author Milecia McGregor shares a mix of slides and demonstrations in AWS, along with some examples in Visual Studio with Python. Its just what you need to learn to pass the exam. It includes an overview of concepts with hands-on work using AWS tools like Kinesis and EMR.
Learn How To:
- Learn effective tips and techniques for passing the AWS Certified Machine Learning – Specialty exam
- Identify and implement data ingestion solutions with Kinesis
- Evaluate ML models
- Deploy ML models with AWS tools
Table of Contents
Introduction
1 AWS Certified Machine Learning Specialty Introduction
2 AWS Certified Machine Learning Specialty Introduction
Lesson 1 Data Engineering
3 Learning objectives
4 Create data repositories for machine learning
5 Identify and implement a data ingestion solution
6 Decide between ingestion tools
7 Identify and implement a data transformation solution
8 Get some practice questions and exercises
Lesson 2 Exploratory Data Analysis
9 Learning objectives
10 Sanitize and prepare data for modeling
11 Perform feature engineering
12 Analyze data for machine learning
13 Visualize data for machine learning
14 Get some practice questions and exercises
Lesson 3 Training Models
15 Learning objectives
16 Frame business problems as machine learning problems
17 Select the appropriate model for a machine learning problem
18 Understand the intuition behind the model
19 Train machine learning models
20 Choose compute option
21 Get some practice questions and exercises
Lesson 4 Evaluating Models
22 Learning objectives
23 Perform hyperparameter optimization
24 Use other methods for hyperparameter optimization
25 Evaluate machine learning models
26 Compare models with different metrics
27 Implement machine learning best practices
28 Get some practice questions and exercises
Lesson 5 Machine Learning Implementation and Operations
29 Learning objectives
30 Build machine learning solutions for production
31 Address scaling concerns
32 Recommend and implement the appropriate machine learning services
33 Apply basic AWS security practices to machine learning solutions
34 Deploy and operationalize machine learning solutions
35 Get some practice questions and exercises
Summary
36 AWS Certified Machine Learning Specialty Summary
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