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AWS Certified Machine Learning Specialty 2025 - Hands On!

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AWS Certified Machine Learning - Specialty

AWS Certified Machine Learning Specialty 2025 - Hands On!

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

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Last updated 11/2021

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₹ 5499.0

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Lessons

Quizzes

Duration

13 Hours

Skill level

Beginner

Language

English

Certificate

Yes

Full lifetime access

Yes

Description

Updated for the latest SageMaker features, and Generative AI (LLM's / Bedrock). 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
15-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 AI
,
CodeWhisperer
, 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 2,500,000+ students and gotten 800,000+ 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 850,00 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!
Who this course is for:
Individuals performing a development or data science role seeking certification in machine learning and AWS.

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

You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
No previous design experience is needed.
No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures

Expand All Sections

1-Introduction

1.1-Udemy 101

1.2-One quick note

1.3-Course Introduction: What to Expect

1.4-Get the Course Materials

2-Data Engineering

2.1-Section Intro: Data Engineering

2.2-Set up an AWS Billing Alarm

2.3-Amazon S3 - Overview

2.4-Amazon S3 Storage Classes + Glacier

2.5-Amazon S3 Storage + Glacier - Hands On

2.6-Amazon S3 Lifecycle Rules (with S3 Analytics)

2.7-Amazon S3 Lifecycle Rules - Hands On

2.8-Amazon S3 - Bucket Policy

2.9-Amazon S3 - Bucket Policy Hands On

2.10-Amazon S3 - Encryption

2.11-Amazon S3 - Encryption Hands On

2.12-Amazon S3 - Default Encryption

2.13-Amazon S3 - VPC Endpoints

2.14-Kinesis Data Streams & Kinesis Data Firehose

2.15-Lab 1.1 - Kinesis Data Firehose

2.16-Kinesis Data Analytics

2.17-Lab 1.2 - Managed Apache Flink (Formely Kinesis Data Analytics)

2.18-Kinesis Data Analytics and Lambda / Managed Service for Apache Flink

2.19-Kinesis Video Streams

2.20-Kinesis ML Summary

2.21-Glue Data Catalog & Crawlers

2.22-Lab 1.3 - Glue Data Catalog

2.23-Glue ETL

2.24-Lab 1.4 - Glue ETL

2.25-Glue Data Brew

2.26-Lab 1.5 - Glue Data Brew

2.27-Lab 1.6 - Athena

2.28-Lab 1 - Cleanup

2.29-AWS Data Stores in Machine Learning

2.30-AWS Data Pipelines

2.31-AWS Batch

2.32-AWS DMS - Database Migration Services

2.33-AWS Step Functions

2.34-Full Data Engineering Pipelines

2.35-Random things you need to know: AWS DataSync and MQTT

2.36-Data Engineering Summary

2.37-Quiz: Data Engineering

3-Exploratory Data Analysis

3.1-Section Intro: Data Analysis

3.2-Python in Data Science and Machine Learning

3.3-Example: Preparing Data for Machine Learning in a Jupyter Notebook.

3.4-Types of Data

3.5-Data Distributions

3.6-Time Series: Trends and Seasonality

3.7-Introduction to Amazon Athena

3.8-Overview of Amazon Quicksight

3.9-Types of Visualizations, and When to Use Them.

3.10-Elastic MapReduce (EMR) and Hadoop Overview

3.11-Apache Spark on EMR

3.12-EMR Notebooks, Security, and Instance Types

3.13-Feature Engineering and the Curse of Dimensionality

3.14-Imputing Missing Data

3.15-Dealing with Unbalanced Data

3.16-Handling Outliers

3.17-Binning, Transforming, Encoding, Scaling, and Shuffling

3.18-Amazon SageMaker Ground Truth and Label Generation

3.19-Lab: Preparing Data for TF-IDF with Spark and EMR Studio, Part 1

3.20-Lab: Preparing Data for TF-IDF with Spark and EMR Studio, Part 2

3.21-Quiz: Exploratory Data Analysis

4-Modeling, Part 1: General Deep Learning and Machine Learning

4.1-Section Intro: Modeling

4.2-Introduction to Deep Learning

4.3-Activation Functions

4.4-Convolutional Neural Networks

4.5-Recurrent Neural Networks

4.6-Modern NLP with BERT and GPT, and Transfer Learning

4.7-Deep Learning on EC2 and EMR

4.8-Tuning Neural Networks

4.9-Regularization Techniques for Neural Networks (Dropout, Early Stopping)

4.10-L1 and L2 Regularization

4.11-Grief with Gradients: The Vanishing Gradient problem

4.12-The Confusion Matrix

4.13-Precision, Recall, F1, AUC, and more

4.14-Ensemble Methods: Bagging and Boosting

4.15-Quiz: Deep Learning and Machine Learning

5-Modeling, Part 2: Amazon SageMaker

5.1-Introducing Amazon SageMaker

5.2-Linear Learner in SageMaker

5.3-XGBoost in SageMaker

5.4-Seq2Seq in SageMaker

5.5-DeepAR in SageMaker

5.6-BlazingText in SageMaker

5.7-Object2Vec in SageMaker

5.8-Object Detection in SageMaker

5.9-Image Classification in SageMaker

5.10-Semantic Segmentation in SageMaker

5.11-Random Cut Forest in SageMaker

5.12-Neural Topic Model in SageMaker

5.13-Latent Dirichlet Allocation (LDA) in SageMaker

5.14-K-Nearest-Neighbors (KNN) in SageMaker

5.15-K-Means Clustering in SageMaker

5.16-Principal Component Analysis (PCA) in SageMaker

5.17-Factorization Machines in SageMaker

5.18-IP Insights in SageMaker

5.19-Reinforcement Learning in SageMaker

5.20-Automatic Model Tuning

5.21-Apache Spark with SageMaker

5.22-SageMaker Studio, and SageMaker Experiments

5.23-SageMaker Debugger

5.24-SageMaker Autopilot / AutoML

5.25-SageMaker Model Monitor

5.26-Deployment Guardrails and Shadow Tests

5.27-WARNING about SageMaker billing

5.28-SageMaker Canvas

5.29-Bias Measures in SageMaker Clarify

5.30-SageMaker Training Compiler

5.31-SageMaker Feature Store

5.32-SageMaker ML Lineage Tracking

5.33-SageMaker Data Wrangler

5.34-Demo: SageMaker Studio, Canvas, and Data Wrangler

5.35-Quiz: Modeling

6-Modeling, Part 3: High-Level ML Services

6.1-Amazon Comprehend

6.2-Amazon Translate

6.3-Amazon Transcribe

6.4-Amazon Polly

6.5-Amazon Rekognition

6.6-Amazon Forecast

6.7-Amazon Forecast Algorithms

6.8-Amazon Lex

6.9-Amazon Personalize

6.10-Lightning round! TexTract, DeepRacer, Lookout, and Monitron

6.11-TorchServe, AWS Neuron, and AWS Panorama

6.12-Deep Composer, Fraud Detection, CodeGuru, and Contact Lens

6.13-Amazon Kendra and Amazon Augmented AI (A2I)

6.14-Quiz: High-Level ML Services

7-Modeling, Part 4: Wrapping up & Lab Activity

7.1-Putting them All Together

7.2-Lab: Tuning a Convolutional Neural Network on EC2, Part 1

7.3-Lab: Tuning a Convolutional Neural Network on EC2, Part 2

7.4-Lab: Tuning a Convolutional Neural Network on EC2, Part 3

8-ML Implementation and Operations

8.1-Section Intro: Machine Learning Implementation and Operations

8.2-SageMaker's Inner Details and Production Variants

8.3-SageMaker On the Edge: SageMaker Neo and IoT Greengrass

8.4-SageMaker Security: Encryption at Rest and In Transit

8.5-SageMaker Security: VPC's, IAM, Logging, and Monitoring

8.6-SageMaker Resource Management: Instance Types and Spot Training

8.7-SageMaker Resource Management: Automatic Scaling, AZ's

8.8-SageMaker Serverless Inference and Inference Recommender

8.9-SageMaker Inference Pipelines

8.10-MLOps with SageMaker, Kubernetes, SageMaker Projects, and SageMaker Pipelines

8.11-Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 1

8.12-Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 2

8.13-Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 3

8.14-Quiz: ML Implementation and Operations

9-Practice Exam Questions

9.1-Warmup Test: Quick Assessment

10-Generative AI: Transformers, GPT, Self-Attention, and Foundation Models

10.1-This section is optional

should I take it?

10.2-The Transformer Architecture

10.3-Self-Attention and Attention-based Neural Networks in Depth

10.4-Applications of Transformers (such as GPT-4 and ChatGPT)

10.5-Generative Pre-Trained Transformers (GPT) in Depth: How They Work, Part 1

10.6-Generative Pre-Trained Transformers (GPT) in Depth: How They Work, Part 2

10.7-Fine-Tuning / Transfer Learning with GPT and Transformers

10.8-Lab: Tokenization and Positional Encoding with SageMaker Notebooks + Huggingface

10.9-Lab: Multi-Headed, Masked Self-Attention in Sagemaker and Huggingface

10.10-Lab: Using GPT within a SageMaker Notebook

10.11-AWS Foundation Models and Amazon SageMaker JumpStart with Generative AI

10.12-Lab: Using Amazon SageMaker JumpStart to load and use GPT from HuggingFace

10.13-Building Generative AI with Amazon Bedrock and Foundation Models

10.14-Lab: Chat, Text, and Image Foundation Models in the Bedrock Playground

10.15-Amazon Q Developer (formerly CodeWhisperer)

10.16-AWS HealthScribe - AI-generated clinical notes from consultation transcriptions

11-Wrapping Up

11.1-Section Intro: Wrapping Up

11.2-More Preparation Resources

11.3-Test-Taking Strategies, and What to Expect

11.4-You Made It!

11.5-Exam Walkthrough and Signup

11.6-Save 50% on your AWS Exam Cost!

11.7-Get an Extra 30 Minutes on your AWS Exam - Non Native English Speakers only

11.8-AWS Certification Paths

11.9-Congratulations

11.10-Bonus Lecture

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