AWS Certified Machine Learning Specialty 2024 - Hands On!

AWS machine learning certification preparation - learn SageMaker, generative AI, data engineering, modeling & more

4.57 (12468 reviews)
Udemy
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English
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IT Certification
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AWS Certified Machine Learning Specialty 2024 - Hands On!
90,238
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14 hours
content
Apr 2024
last update
$109.99
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What you will 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

Why take this course?

Updated for the latest SageMaker features, Generative AI (GPT), 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 11-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:


  • How generative AI and large language models (LLM's) work, including the Transformer architecture (GPT) and attention-based neural networks (masked self-attention)

  • Amazon's newest generative AI services: Bedrock, SageMaker JumpStart for Generative AICodeWhisperer, and SageMaker Foundation Models

  • 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

  • Building recommender systems with Amazon Personalize

  • Monitoring industrial equipment with Lookout and Monitron

  • 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.


Instructor

My name is Stéphane Maarek, I am passionate about Cloud Computing, and I will be your instructor in this course. I teach about AWS certifications, focusing on helping my students improve their professional proficiencies in AWS.

I have already taught 1,500,000+ students and gotten 500,000+ reviews throughout my career in designing and delivering these certifications and courses!

With AWS becoming the centerpiece of today's modern IT architectures, I have decided it is time for students to learn how to be an AWS Machine Learning Professional. So, let’s kick start the course! You are in good hands!


Instructor

Hey, I'm Frank Kane, and I'm also instructing this course. I spent nine years working for Amazon from the inside as a senior engineer and senior manager, where my specialty was recommender systems and machine learning. As an instructor, I'm best known for my top-selling courses in "big data", data analytics, machine learning, Apache Spark, system design, technical management and career growth, and Elasticsearch.

I've been teaching on Udemy since 2015, where I've reached over 700,00 students all around the world!

I've worked hard to keep this course up to date with the latest developments in AWS machine learning, and to make sure you're prepared for the latest version of this exam. Let's dive in and get you ready!


This course also comes with:

  • Lifetime access to all future updates

  • A responsive instructor in the Q&A Section

  • Udemy Certificate of Completion Ready for Download

  • A 30 Day "No Questions Asked" Money Back Guarantee!

Join us in this course if you want to prepare for the AWS Machine Learning Certification and master the AWS platform!

Content

Introduction

Udemy 101
Course Introduction: What to Expect
Get the Course Materials

Data Engineering

Section Intro: Data Engineering
Amazon S3 - Overview
Amazon S3 - Storage Tiers & Lifecycle Rules
Amazon S3 Security
Kinesis Data Streams & Kinesis Data Firehose
Lab 1.1 - Kinesis Data Firehose
Kinesis Data Analytics
Lab 1.2 - Kinesis Data Analytics
Kinesis Video Streams
Kinesis ML Summary
Glue Data Catalog & Crawlers
Lab 1.3 - Glue Data Catalog
Glue ETL
Lab 1.4 - Glue ETL
Lab 1.5 - Athena
Lab 1 - Cleanup
AWS Data Stores in Machine Learning
AWS Data Pipelines
AWS Batch
AWS DMS - Database Migration Services
AWS Step Functions
Full Data Engineering Pipelines
Data Engineering Summary
Quiz: Data Engineering

Exploratory Data Analysis

Section Intro: Data Analysis
Python in Data Science and Machine Learning
Example: Preparing Data for Machine Learning in a Jupyter Notebook.
Types of Data
Data Distributions
Time Series: Trends and Seasonality
Introduction to Amazon Athena
Overview of Amazon Quicksight
Types of Visualizations, and When to Use Them.
Elastic MapReduce (EMR) and Hadoop Overview
Apache Spark on EMR
EMR Notebooks, Security, and Instance Types
Feature Engineering and the Curse of Dimensionality
Imputing Missing Data
Dealing with Unbalanced Data
Handling Outliers
Binning, Transforming, Encoding, Scaling, and Shuffling
Amazon SageMaker Ground Truth and Label Generation
Lab: Preparing Data for TF-IDF with Spark and EMR, Part 1
Lab: Preparing Data for TF-IDF with Spark and EMR, Part 2
Lab: Preparing Data for TF-IDF with Spark and EMR, Part 3
Quiz: Exploratory Data Analysis

Modeling

Section Intro: Modeling
Introduction to Deep Learning
Activation Functions
Convolutional Neural Networks
Recurrent Neural Networks
Deep Learning on EC2 and EMR
Tuning Neural Networks
Regularization Techniques for Neural Networks (Dropout, Early Stopping)
Grief with Gradients: The Vanishing Gradient problem
L1 and L2 Regularization
The Confusion Matrix
Precision, Recall, F1, AUC, and more
Ensemble Methods: Bagging and Boosting
Introducing Amazon SageMaker
Linear Learner in SageMaker
XGBoost in SageMaker
Seq2Seq in SageMaker
DeepAR in SageMaker
BlazingText in SageMaker
Object2Vec in SageMaker
Object Detection in SageMaker
Image Classification in SageMaker
Semantic Segmentation in SageMaker
Random Cut Forest in SageMaker
Neural Topic Model in SageMaker
Latent Dirichlet Allocation (LDA) in SageMaker
K-Nearest-Neighbors (KNN) in SageMaker
K-Means Clustering in SageMaker
Principal Component Analysis (PCA) in SageMaker
Factorization Machines in SageMaker
IP Insights in SageMaker
Reinforcement Learning in SageMaker
Automatic Model Tuning
Apache Spark with SageMaker
SageMaker Studio, and new SageMaker features for 2020
Amazon Comprehend
Amazon Translate
Amazon Transcribe
Amazon Polly
Amazon Rekognition
Amazon Forecast
Amazon Lex
The Best of the Rest: Other High-Level AWS Machine Learning Services
New ML Services for 2020
Putting them All Together
Lab: Tuning a Convolutional Neural Network on EC2, Part 1
Lab: Tuning a Convolutional Neural Network on EC2, Part 2
Lab: Tuning a Convolutional Neural Network on EC2, Part 3
Quiz: Modeling

ML Implementation and Operations

Section Intro: Machine Learning Implementation and Operations
SageMaker's Inner Details and Production Variants
SageMaker On the Edge: SageMaker Neo and IoT Greengrass
SageMaker Security: Encryption at Rest and In Transit
SageMaker Security: VPC's, IAM, Logging, and Monitoring
SageMaker Resource Management: Instance Types and Spot Training
SageMaker Resource Management: Elastic Inference, Automatic Scaling, AZ's
SageMaker Inference Pipelines
Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 1
Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 2
Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 3
Quiz: ML Implementation and Operations

Wrapping Up

Section Intro: Wrapping Up
More Preparation Resources
Test-Taking Strategies, and What to Expect
You Made It!
Save 50% on your AWS Exam Cost!
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Practice Exams

Warmup Test: Quick Assessment
THANK YOU!
Bonus Lecture: Get the Full 3-Hour Practice Exam

Screenshots

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Reviews

Robin
November 14, 2023
Just the explanation of possibilities with something as "simple" as S3 has me excited to learn more about what's possible with the AWS services
Moataz
November 11, 2023
The course appears to cover most topics needed for the exam. The delivery is a bit monotonic throughout the lessons, I sometimes found it hard to follow in longer learning sessions.
Sam
November 9, 2023
Videos are short, but get the point across very well. I feel like I am learning a lot without wasting too much time
Saurabh
October 24, 2023
Just passed my AWS Machine learning specialty exam, The course covered most of the topics, This course lags in MLOPS part, So read MLOPS part from somewhere else and That's it, Also watchout for those data format questions.
Suhaib
October 24, 2023
I have completed the course and passed the exam, the course was enough to pass the exam, but most importantly it is prepared to give you a deep dive into using AWS for your ML Projects
Keith
October 23, 2023
The content helped me pass the exam on Friday 10/20. Overall, most of the topics are covered. I will concur with what other students said, and Frank needs to keep his repeated opinions to himself, as they come off judgmental and add no value in the context of the exam.
Neeraj
October 22, 2023
Hi, This course nicely cover all the exams topics, just go through these multiple times until you grasp the concepts. Taking one exam like cloud practitioner before this would be nice to cover maximum topics. Today 22-Oct-2023, I passed real exam with 910 score in first attempt. Thanks a lot instructor for this high rated collection.
Vaidhin
October 16, 2023
Machine Learning content explanation is awesome. Since I am from ML background, I expected more AWS demo labs... lil disappointed. Also it would be great if there is atleast one full end to end ML project is included.
Adeola
October 6, 2023
It is a great match for me. i have been involved with Data Science an machine learning for a while and this course is helping me to know more.
Jagadesh
October 3, 2023
Hi All, This is Amazing. I just got my Certification with a score of 807. Thank you Frank and Stephane. I want to share few things. Am new to ML world. The exam was super challenging and I didn't get any single direct question. But am able to understand all the questions. Don't be surprised with the exam questions. Go through the course and mainly UNDERSTAND the process try to CONNECT THE DOTS which is what helped me. Am newbie to ML but this give me a good confident to start with. All the Best !!
Surjeet
September 27, 2023
Absolutely loved this course! Stephane Maarek is fantastic, and Frank Kane is a wizard! Frank effortlessly explained complex concepts and even highlighted areas where we might encounter exam questions. Huge fan of Frank now! ?
Binnie
July 26, 2023
I followed the course from top to end. What appeared in the exam was all basically covered in this course. The important thing to note is, besides the lecture materials, it is best to read AWS official SageMaker documentation as well. They get into the nitty-gritty details as small as input file formats for the training of a specific algorithm. Overall this course provides a perfect guideline and overview for further study.
Oleg
July 14, 2023
It is to the point, concise, giving enough details. At the same time, the course lacks practical tasks or labs to deep dive into the hands-on actual usage of the knowledge given. Another thing is, the lecturer speaks in a rush and sometimes swallows some words. IMHO no need to rush, if someone needs it faster, they could increase the play speed. The last thing, I don't really value the approach to care only about the exam. I need the knowledge, not the certificate just for the certificate. Other than that, it's a good course, thank you!
Igor
July 13, 2023
It's probably the best course I've done until now. Good depth, very dense and relevant. Instructors have solid knowledge in their domains. This course is worth at least 10x its price.
A
July 12, 2023
Decent material, but the instructor would do well to remove politically-charged topics from his slides. Removing the controversial content will broaden his audience base, so that more people can benefit from his ML knowledge, which is what they're here to learn - not the his political views. If an example is required then here is one: in my opinion the graph regarding rising sea levels hurt his credibility because he didn't apply his own advice around understanding the data but just said something naive like "it goes up, that's not good everyone!". Another example is the slide labeled "Income Inequality".

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