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2021-02-17 09:19:30
30-Day Money-Back Guarantee
IT & Software IT Certification AWS Certification

AWS Certified Machine Learning Specialty 2021 - Hands On!

AWS machine learning certification preparation - learn SageMaker, feature engineering, data engineering, modeling & more
Bestseller
Rating: 4.5 out of 54.5 (3,324 ratings)
23,464 students
Created by Sundog Education by Frank Kane, Stephane Maarek | AWS Certified Solutions Architect & Developer Associate, Frank Kane
Last updated 3/2021
English
English, French [Auto]
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

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, in depth

  • 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
Ville Mäkinen
75 courses
6 reviews
Rating: 5.0 out of 5a year ago
This was a good course to prepare for the AWS ML Specialty Exam. This covers about anything in high-level detail you need to know in the exam. Labs were also really nice, pretty short and all the relevant content was still managed to get in. Overall, I recommend this course for everyone who are planning to take the exam!

Course content

7 sections • 114 lectures • 9h 38m total length

  • Preview02:10
  • Preview06:09
  • Get the Course Materials
    01:42

  • Section Intro: Data Engineering
    01:04
  • Amazon S3 - Overview
    05:04
  • Amazon S3 - Storage Tiers & Lifecycle Rules
    04:29
  • Amazon S3 Security
    08:05
  • Kinesis Data Streams & Kinesis Data Firehose
    08:38
  • Preview06:04
  • Kinesis Data Analytics
    04:25
  • Lab 1.2 - Kinesis Data Analytics
    07:22
  • Kinesis Video Streams
    02:55
  • Kinesis ML Summary
    01:12
  • Glue Data Catalog & Crawlers
    02:32
  • Lab 1.3 - Glue Data Catalog
    04:23
  • Glue ETL
    02:10
  • Lab 1.4 - Glue ETL
    06:20
  • Lab 1.5 - Athena
    01:26
  • Lab 1 - Cleanup
    01:32
  • AWS Data Stores in Machine Learning
    03:09
  • AWS Data Pipelines
    02:39
  • AWS Batch
    01:51
  • AWS DMS - Database Migration Services
    01:58
  • AWS Step Functions
    02:44
  • Full Data Engineering Pipelines
    05:09
  • Data Engineering Summary
    00:48
  • Quiz: Data Engineering
    5 questions

  • Section Intro: Data Analysis
    01:12
  • Python in Data Science and Machine Learning
    12:08
  • Example: Preparing Data for Machine Learning in a Jupyter Notebook.
    10:21
  • Types of Data
    04:31
  • Preview06:05
  • Time Series: Trends and Seasonality
    03:57
  • Introduction to Amazon Athena
    05:06
  • Overview of Amazon Quicksight
    05:59
  • Types of Visualizations, and When to Use Them.
    04:46
  • Elastic MapReduce (EMR) and Hadoop Overview
    07:14
  • Apache Spark on EMR
    09:59
  • EMR Notebooks, Security, and Instance Types
    04:10
  • Feature Engineering and the Curse of Dimensionality
    06:34
  • Preview08:04
  • Dealing with Unbalanced Data
    05:35
  • Handling Outliers
    08:30
  • Binning, Transforming, Encoding, Scaling, and Shuffling
    07:59
  • Amazon SageMaker Ground Truth and Label Generation
    04:28
  • Preview06:18
  • Lab: Preparing Data for TF-IDF with Spark and EMR, Part 2
    09:46
  • Lab: Preparing Data for TF-IDF with Spark and EMR, Part 3
    13:29
  • Quiz: Exploratory Data Analysis
    5 questions

  • Preview01:47
  • Introduction to Deep Learning
    09:23
  • Activation Functions
    10:50
  • Convolutional Neural Networks
    12:07
  • Recurrent Neural Networks
    10:48
  • Deep Learning on EC2 and EMR
    01:32
  • Preview04:48
  • Regularization Techniques for Neural Networks (Dropout, Early Stopping)
    06:41
  • Grief with Gradients: The Vanishing Gradient problem
    04:28
  • L1 and L2 Regularization
    03:04
  • The Confusion Matrix
    05:30
  • Precision, Recall, F1, AUC, and more
    06:59
  • Ensemble Methods: Bagging and Boosting
    03:43
  • Preview08:06
  • Linear Learner in SageMaker
    04:59
  • XGBoost in SageMaker
    03:00
  • Seq2Seq in SageMaker
    04:47
  • DeepAR in SageMaker
    04:06
  • BlazingText in SageMaker
    04:55
  • Object2Vec in SageMaker
    04:44
  • Object Detection in SageMaker
    04:02
  • Image Classification in SageMaker
    04:08
  • Semantic Segmentation in SageMaker
    03:48
  • Random Cut Forest in SageMaker
    03:01
  • Neural Topic Model in SageMaker
    03:25
  • Latent Dirichlet Allocation (LDA) in SageMaker
    03:09
  • K-Nearest-Neighbors (KNN) in SageMaker
    02:59
  • K-Means Clustering in SageMaker
    05:00
  • Principal Component Analysis (PCA) in SageMaker
    03:20
  • Factorization Machines in SageMaker
    04:11
  • IP Insights in SageMaker
    02:58
  • Reinforcement Learning in SageMaker
    12:23
  • Automatic Model Tuning
    05:55
  • Apache Spark with SageMaker
    03:17
  • SageMaker Studio, and new SageMaker features for 2020
    06:06
  • Amazon Comprehend
    05:49
  • Amazon Translate
    01:54
  • Amazon Transcribe
    04:16
  • Preview05:38
  • Amazon Rekognition
    07:45
  • Amazon Forecast
    01:45
  • Amazon Lex
    03:07
  • The Best of the Rest: Other High-Level AWS Machine Learning Services
    02:50
  • New ML Services for 2020
    06:18
  • Putting them All Together
    02:08
  • Lab: Tuning a Convolutional Neural Network on EC2, Part 1
    08:59
  • Lab: Tuning a Convolutional Neural Network on EC2, Part 2
    09:06
  • Lab: Tuning a Convolutional Neural Network on EC2, Part 3
    06:29
  • Quiz: Modeling
    5 questions

  • Section Intro: Machine Learning Implementation and Operations
    01:10
  • Preview10:55
  • SageMaker On the Edge: SageMaker Neo and IoT Greengrass
    04:18
  • SageMaker Security: Encryption at Rest and In Transit
    04:31
  • SageMaker Security: VPC's, IAM, Logging, and Monitoring
    04:02
  • SageMaker Resource Management: Instance Types and Spot Training
    03:35
  • SageMaker Resource Management: Elastic Inference, Automatic Scaling, AZ's
    04:34
  • SageMaker Inference Pipelines
    01:39
  • Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 1
    05:20
  • Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 2
    10:33
  • Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 3
    12:20
  • Quiz: ML Implementation and Operations
    5 questions

  • Section Intro: Wrapping Up
    00:24
  • More Preparation Resources
    05:52
  • Preview10:04
  • You Made It!
    00:46
  • Save 50% on your AWS Exam Cost!
    01:41
  • Get an Extra 30 Minutes on your AWS Exam - Non Native English Speakers only
    01:09

  • Warmup Test: Quick Assessment
    10 questions
  • THANK YOU!
    01:32
  • Bonus Lecture: Get the Full 3-Hour Practice Exam
    01:45

Instructors

Sundog Education by Frank Kane
Founder, Sundog Education. Machine Learning Pro
Sundog Education by Frank Kane
  • 4.5 Instructor Rating
  • 98,563 Reviews
  • 442,901 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.

Stephane Maarek | AWS Certified Solutions Architect & Developer Associate
Best Selling Instructor, Kafka Guru, 9x AWS Certified
Stephane Maarek | AWS Certified Solutions Architect & Developer Associate
  • 4.7 Instructor Rating
  • 225,229 Reviews
  • 731,446 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!

Frank Kane
Founder, Sundog Education
Frank Kane
  • 4.5 Instructor Rating
  • 95,092 Reviews
  • 398,525 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.

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