Complete AWS Certified Machine Learning Specialty Exam- 2020
With full Practice Certification test, hand-on lab, featured interview guest, practical use cases
What you will learn
Data Engineering with S3, Kinesis, etc
Basics of Feature engineering
Feature engineering techniques such as imputation, normalisation, etc
Amazon SageMaker basics
SakeMaker GroundTruth
SageMaker Built-in algorithms such as XGBoost, Object Detection etc
High Level machine learning services such as Rekognition, Translate, Transcribe, Lex, Polly
Exploratory data analysis basics, and how to use scikit_learn, Athena, Spark, EMR
AWS Kinesis suite of tools including Kinesis firehose, Kinesis video streams, Kinesis data streams, Kinesis analytics
Basics of Artificial neural networks, Machine and Deep Learning
Machine learning model implementation and operations in AWS
Why take this course?
*Best value for money, with practice test included*
*Updated for 2020's latest AWS Machine Learning and SageMaker features*
*FEATURED INTERVIEW WITH AWS ML EXPERT* Topics discussed are:
Example of real project of AWS ML services
Some of the challenges faced on the project
What do companies look for when hiring for AWS specialists?
How can someone showcase their skills?
At the time of publication, this is the only complete AWS Machine Learning Specialty Certification course on udemy to include a full length practice test.
The course will follow the exam structure, and is divided into the following sections: Data Engineering, Exploratory Data Analysis, Modeling, Machine Learning Implementation and Operations.
Topics we will cover include:
Featured interview with AWS Expert that covers practical and job related aspects, important AWS tools etc.
Basics of Data Science, Artificial Neural Networks, and Deep Learning
Practical examples and use cases
S3
Glue and Glue ETL
Kinesis data streams, firehose, and video streams
Data Pipelines, AWS Batch, and Step Functions
scikit_learn, numpy, panda
Athena and Quicksight
Elastic MapReduce (EMR)
Apache Spark
Feature engineering
SageMaker Ground Truth, Built-in Algorithms
Deep Learning basics
How to evaluate machine learning models (confusion matrix)
Regularisation techniques
Comparison of various AWS services to help you understand when to use which service
High Level Machine Learning Services: Polly, Transcribe, Lex, Rekognition, and more
Security on AWS