AWS Certified Machine Learning Specialty 2021 - Hands On!
What you'll learn
- What to expect on the AWS Certified Machine Learning Specialty exam
- Amazon SageMaker's built-in machine learning algorithms (XGBoost, BlazingText, Object Detection, etc.)
- Feature engineering techniques, including imputation, outliers, binning, and normalization
- High-level ML services: Comprehend, Translate, Polly, Transcribe, Lex, Rekognition, and more
- Data engineering with S3, Glue, Kinesis, and DynamoDB
- Exploratory data analysis with scikit_learn, Athena, Apache Spark, and EMR
- Deep learning and hyperparameter tuning of deep neural networks
- Automatic model tuning and operations with SageMaker
- L1 and L2 regularization
- Applying security best practices to machine learning pipelines
Requirements
- Associate-level knowledge of AWS services such as EC2
- Some existing familiarity with machine learning
- An AWS account is needed to perform the hands-on lab exercises
Description
[ Updated for 2021's latest SageMaker features and new AWS ML Services. Happy learning! ]
Nervous about passing the AWS Certified Machine Learning - Specialty exam (MLS-C01)? You should be! There's no doubt it's one of the most difficult and coveted AWS certifications. A deep knowledge of AWS and SageMaker isn't enough to pass this one - you also need deep knowledge of machine learning, and the nuances of feature engineering and model tuning that generally aren't taught in books or classrooms. You just can't prepare enough for this one.
This certification prep course is taught by Frank Kane, who spent nine years working at Amazon itself in the field of machine learning. Frank took and passed this exam on the first try, and knows exactly what it takes for you to pass it yourself. Joining Frank in this course is Stephane Maarek, an AWS expert and popular AWS certification instructor on Udemy.
In addition to the 9-hour video course, a 30-minute quick assessment practice exam is included that consists of the same topics and style as the real exam. You'll also get four hands-on labs that allow you to practice what you've learned, and gain valuable experience in model tuning, feature engineering, and data engineering.
This course is structured into the four domains tested by this exam: data engineering, exploratory data analysis, modeling, and machine learning implementation and operations. Just some of the topics we'll cover include:
S3 data lakes
AWS Glue and Glue ETL
Kinesis data streams, firehose, and video streams
DynamoDB
Data Pipelines, AWS Batch, and Step Functions
Using scikit_learn
Data science basics
Athena and Quicksight
Elastic MapReduce (EMR)
Apache Spark and MLLib
Feature engineering (imputation, outliers, binning, transforms, encoding, and normalization)
Ground Truth
Deep Learning basics
Tuning neural networks and avoiding overfitting
Amazon SageMaker, including SageMaker Studio, SageMaker Model Monitor, SageMaker Autopilot, and SageMaker Debugger.
Regularization techniques
Evaluating machine learning models (precision, recall, F1, confusion matrix, etc.)
High-level ML services: Comprehend, Translate, Polly, Transcribe, Lex, Rekognition, and more
Security best practices with machine learning on AWS
Machine learning is an advanced certification, and it's best tackled by students who have already obtained associate-level certification in AWS and have some real-world industry experience. This exam is not intended for AWS beginners.
If there's a more comprehensive prep course for the AWS Certified Machine Learning - Specialty exam, we haven't seen it. Enroll now, and gain confidence as you walk into that testing center.
Who this course is for:
- Individuals performing a development or data science role seeking certification in machine learning and AWS.
Featured review
![Ville Mäkinen](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
Course content
- Preview02:10
- Preview06:09
- 01:42Get the Course Materials
Instructors
- 4.5 Instructor Rating
- 102,752 Reviews
- 461,277 Students
- 22 Courses
Sundog Education's mission is to make highly valuable career skills in big data, data science, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford.
Sundog Education is led by Frank Kane and owned by Frank's company, Sundog Software LLC. Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.
Due to our volume of students we are unable to respond to private messages; please post your questions within the Q&A of your course. Thanks for understanding.
- 4.7 Instructor Rating
- 258,913 Reviews
- 822,925 Students
- 38 Courses
Stephane is a solutions architect, consultant and software developer that has a particular interest in all things related to Big Data, Cloud & API. He's also a many-times best seller instructor on Udemy for his courses in Apache Kafka and AWS.
[See FAQ below to see in which order you can take my courses]
Stéphane is recognized as an AWS Hero and is an AWS Certified Solutions Architect Professional & AWS Certified DevOps Professional. He loves to teach people how to use the AWS properly, to get them ready for their AWS certifications, and most importantly for the real world.
He also loves Apache Kafka. He sits on the 2019 Program Committee organizing the Kafka Summit in New York, London and San Francisco. He is also an active member of the Apache Kafka community, authoring blogs on Medium and a guest blog for Confluent.
During his spare time he enjoys cooking, practicing yoga, surfing, watching TV shows, and traveling to awesome destinations!
FAQ: In which order should you learn?...
AWS Cloud: Start with AWS Certified Solutions Architect Associate, then move on to AWS Certified Developer Associate and then AWS Certified SysOps Administrator. Afterwards you can either do AWS Certified Solutions Architect Professional or AWS Certified DevOps Professional, or a specialty certification of your choosing. Once ready, you can learn AWS Lambda and AWS CloudFormation in depth, or do the AWS Big Data certification.
Apache Kafka: Start with Apache Kafka for Beginners, then you can learn Connect, Streams and Schema Registry if you're a developer, and Setup and Monitoring courses if you're an admin. Both tracks are needed to pass the Confluent Kafka certification.
gRPC: First do the protocol buffers course, then move on to gRPC Java or gRPC Golang course.
In order to share knowledge I am not able to respond to private messages on Udemy. If you're a student, please ask questions inside the course. Thanks for understanding!
- 4.5 Instructor Rating
- 98,926 Reviews
- 416,168 Students
- 14 Courses
Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.
Due to our volume of students, I am unable to respond to private messages; please post your questions within the Q&A of your course. Thanks for understanding.