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More Than 7 Hours of Video Instruction
This course covers the essentials of Machine Learning on AWS and prepares a candidate to sit for the AWS Machine Learning-Specialty (ML-S) Certification exam. Four main categories are covered: Data Engineering, EDA (Exploratory Data Analysis), Modeling, and Operations. The course offers the following tools to help users pass the exam:* Quizzes: Each lesson module includes a five-question multiple-choice quiz in the style of the AWS ML exam. There are 39 quizzes, with a total of 195 questions.
* Practice Exam: A multiple-choice practice exam is included in the style of the AWS ML exam. This exam totals 50 questions and takes one hour to complete.
This 7+ hour Complete Video Course is fully geared toward the AWS Machine Learning-Specialty (ML-S) Certification exam. The course offers a modular lesson and sublesson approach, with a mix of screencasting and headhsot treatment.
* Data Engineering instruction covers the ingestion, cleaning, and maintenance of data on AWS.* Exploratory Data Analysis covers topics including data visualization, descriptive statistics, and dimension reduction and includes information on relevant AWS services.
* Machine Learning Modeling covers topics including feature engineering, performance metrics, overfitting, and algorithm selection.
* Operations covers deploying models, A/B testing, using AI services versus training your own model, and proper cost utilization.
Each module offers module quizzes and a practice exam in multiple-choice format.
What You Will Learn
* How to perform data engineering tasks on AWS* How to use Exploratory Data Analysis (EDA) to solve machine learning problems on AWS
* How to perform machine learning modeling tasks on the AWS platform
* How to operationalize machine learning models and deploy them to production on the AWS platform
* How to think about the AWS Machine Learning-Specialty (ML-S) Certification exam to optimize for the best outcome
Who Should Take This Course
* DevOps engineers who want to understand how to operationalize ML workloads* Software engineers who want to ensure they have a mastery of machine learning terminology and practice on AWS
* Machine learning engineers who want to solidify their knowledge about AWS machine learning practices
* Product managers who need to understand the AWS machine learning lifecycle
* Data scientists who run machine learning workloads on AWS
One to two years of experience with AWS and six months using ML tools. Ideally, candidates will have already passed the AWS Cloud Practitioner certification.
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Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.
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Lesson 1: AWS Machine Learning-Specialty (ML-S) Certification
1.1 Get an overview of the certification
1.2 Use exam study resources
1.3 Review the exam guide
1.4 Learn the exam strategy
1.5 Learn the best practices of ML on AWS
1.6 Learn the techniques to accelerate hands-on practice
1.7 Understand important ML related services
Lesson 2: Data Engineering for ML on AWS
2.1 Learn data ingestion concepts
2.2 Using data cleaning and preparation
2.3 Learn data storage concepts
2.4 Learn ETL solutions (Extract-Transform-Load)
2.5 Understand data batch vs data streaming
2.6 Understand data security
2.7 Learn data backup and recovery concepts
Lesson 3: Exploratory Data Analysis on AWS
3.1 Understand data visualization: Overview
3.2 Learn Clustering
3.3 Use Summary Statistics
3.4 Implement Heatmap
3.5 Understand Principal Component Analysis (PCA)
3.6 Understand data distributions
3.7 Use data normalization techniques
Lesson 4: Machine Learning Modeling on AWS
4.1 Understand AWS ML Systems: Overview (Sagemaker, AWS ML, EMR, MXNet)
4.2 Use Feature Engineering
4.3 Train a Model
4.4 Evaluate a Model
4.5 Tune a Model
4.6 Understand ML Inference
4.7 Understand Deep Learning on AWS
Lesson 5: Operationalize Machine Learning on AWS
5.1 Understand ML operations: Overview
5.2 Use Containerization with Machine Learning and Deep Learning
5.3 Implement continuous deployment and delivery for Machine Learning
5.4 Understand A/B Testing production deployment
5.5 Troubleshoot production deployment
5.6 Understand production security
5.7 Understand cost and efficiency of ML systems
Lesson 6: Create a Production Machine Learning Application
6.1 Create Machine Learning Data Pipeline
6.2 Perform Exploratory Data Analysis using AWS Sagemaker
6.3 Create Machine Learning Model using AWS Sagemaker
6.4 Deploy Machine Learning Model using AWS Sagemaker
Lesson 7: Case Studies
7.1 Sagemaker Features
7.2 DeepLense Features
7.3 Kinesis Features
7.4 AWS Flavored Python