AWS Certified Machine Learning - Specialty (MLS-C01) Cert Prep

Course details

Getting the AWS Certified Machine Learning - Specialty (MLS-C01) certification highlights your versatility as an ML engineer. Usually, ML engineers focus on handling data and building models, so if you know how to use cloud tools, you can provide even more value in your role. In this course, author Milecia McGregor shares a mix of slides and demonstrations in AWS, along with some examples drawn from Visual Studio with Python. Milecia helps you prepare for the exam by showing you how to ingest your own data, get through the feature engineering process, train and evaluate models, and deploy them to where they will be consumed.

Learning objectives
Identify and implement data ingestion solutions with Kinesis.
Train and evaluate ML models.
Deploy ML models with AWS tools.

Concepts

0. Introduction

01 - AWS Certified Machine Learning Specialty - Introduction

1. Data Engineering

02 - Learning objectives
03 - Create data repositories for machine learning
04 - Identify and implement a data ingestion solution
05 - Decide between ingestion tools
06 - Identify and implement a data transformation solution
07 - Get some practice - Questions and exercises

2. Exploratory Data Analysis

08 - Learning objectives
09 - Sanitize and prepare data for modeling
10 - Perform feature engineering
11 - Analyze data for machine learning
12 - Visualize data for machine learning
13 - Get some practice - Questions and exercises

3. Training Models

14 - Learning objectives
15 - Frame business problems as machine learning problems
16 - Select the appropriate model for a machine learning problem
17 - Understand the intuition behind the model
18 - Train machine learning models
19 - Choose a compute option
20 - Get some practice - Questions and exercises

4. Evaluating Models

21 - Learning objectives
22 - Perform hyperparameter optimization
23 - Use other methods for hyperparameter optimization
24 - Evaluate machine learning models
25 - Compare models with different metrics
26 - Implement machine learning best practices
27 - Get some practice - Questions and exercises

5. Machine Learning Implementation and Operations

28 - Learning objectives
29 - Build machine learning solutions for production
30 - Address scaling concerns
31 - Recommend and implement the appropriate machine learning services
32 - Apply basic AWS security practices to machine learning solutions
33 - Deploy and operationalize machine learning solutions
34 - Get some practice - Questions and exercises

Conclusion

35 - AWS Certified Machine Learning Specialty - Summary