AWS Certified Machine Learning - Specialty 2020

By Scott Pletcher
By Brock Tubre

Ace the AWS Machine Learning Specialty Certification exam with ACG - no PhD in Mathematics needed!

13 hours
  • 87 Lessons
  • 7 Hands-On Labs
  • 8 Course Quizzes
  • 1 Practice Exam

About the course

Hello Cloud Gurus,

Your executive board members are asking you to do something with it. Your grandmother is asking you if it will put you out of a job. Your deadbeat college roommate is asking if you can help him find a date with it. Everyone seems to fuss over Machine Learning, but how many of us truly understand it? Too few.

Fortunately, ACG has your back yet again with a fresh course focused on helping you outsmart the new AWS Certified Machine Learning Specialty. In typical ACG manner, we have created a course that confronts the potentially dull and boring topic of machine learning head-on with quirky and engaging lectures, interactive labs and plenty of real-world, plain-speak examples.

In this course, you’ll learn:

  • The domains of knowledge for the AWS Certified Machine Learning Speciality exam.
  • Best practices for using the tools and platforms of AWS for data engineering, data analysis, machine learning modeling, model evaluation and deployment.
  • Hands-on labs designed to challenge your intuition, creativity and knowledge of the AWS platform.

With this course you’ll get a solid understanding of the services and platforms available on AWS for Machine Learning projects, build a foundation to pass the certification exam and feel equipped to use the AWS ML portfolio in your own real-world applications.

Don’t just sit idly by, watching as robotic overlords take over the world. Create your own army of sentient machines and beat them at their own game! And keep being awesome, cloud gurus!

  • Chapter 1 5 Lessons Introduction 17:36

    An Important Note About A Cloud Guru and Linux Academy Courses

    1:18

    Course Introduction

    2:31

    About The Exam

    4:54

    About This Course

    7:59

    A Note About Demo Lessons

    0:54
  • Chapter 2 9 Lessons Data Collection 1:16:58

    Introduction

    1:36

    Concepts

    9:13

    General Data Terminology

    8:06

    Machine Learning Data Terminology

    16:08

    AWS Data Stores

    9:11

    AWS Migration Tools

    9:50

    AWS Helper Tools

    3:52

    Exam Tips

    4:02

    Chapter 2 Quiz

    15:00 Quiz
  • Chapter 3 10 Lessons Streaming Data Collection 1:26:47

    Introduction

    0:22

    Concepts

    7:03

    Kinesis Data Streams

    13:11

    Kinesis Firehose

    5:26

    Kinesis Video Streams

    4:11

    Kinesis Data Analytics

    6:25

    Exam Tips

    1:54

    Demo: Streaming Data Collection

    33:15

    Chapter 3 Quiz

    15:00 Quiz

    Perform Real-Time Data Analysis with Kinesis

    0:00 Hands-On Lab
  • Chapter 4 12 Lessons Data Preparation 2:05:49

    Introduction

    0:25

    Concepts

    5:34

    Categorical Encoding

    11:05

    Text Feature Engineering

    17:50

    Numeric Feature Engineering

    8:26

    Other Feature Engineering

    7:01

    Handling Missing Values

    4:45

    Feature Selection

    3:46

    Helper Tools

    16:51

    Exam Tips

    7:30

    Demo: Data Preparation

    27:36

    Chapter 4 Quiz

    15:00 Quiz
  • Chapter 5 10 Lessons Data Analysis and Visualization 1:31:36

    Introduction

    0:46

    Concepts

    4:29

    Relationships

    6:21

    Comparisons

    3:58

    Distributions

    7:12

    Compositions

    3:04

    Choosing A Visualization

    5:22

    Exam Tips

    2:15

    Demo: Data Analysis and Visualization

    43:09

    Chapter 5 Quiz

    15:00 Quiz
  • Chapter 6 9 Lessons Modeling 1:37:32

    Introduction

    0:48

    Concepts

    11:30

    Data Preparation

    8:11

    SageMaker Modeling

    11:01

    SageMaker Training

    12:05

    Exam Tips

    2:49

    Demo: Modeling

    36:08

    Chapter 6 Quiz

    15:00 Quiz

    Introduction to Jupyter Notebooks (AWS SageMaker)

    0:00 Hands-On Lab
  • Chapter 7 18 Lessons Algorithms 2:35:49

    Introduction

    0:42

    OPTIONAL - Why do we call them "Algorithms"?

    7:54

    Concepts

    12:22

    Regression

    13:09

    Clustering

    6:52

    Classification

    6:28

    Image Analysis

    6:41

    Anomaly Detection

    5:54

    Text Analytics

    9:40

    Reinforcement Learning

    8:22

    Forecasting

    5:12

    Ensemble Learning

    6:35

    Exam Tips

    5:39

    Demo: Algorithms

    45:19

    Creating a TensorFlow Image Classifier in AWS SageMaker

    0:00 Hands-On Lab

    Creating an MXNet Image Classifier in AWS SageMaker

    0:00 Hands-On Lab

    Creating a scikit-learn Random Forest Classifier in AWS SageMaker

    0:00 Hands-On Lab

    Chapter 7 quiz

    15:00 Quiz
  • Chapter 8 8 Lessons Evaluation and Optimization 1:30:55

    Introduction

    0:27

    Concepts

    4:36

    Monitoring and Analyzing Training Jobs

    3:16

    Evaluating Model Accuracy

    16:56

    Model Tuning

    7:13

    Exam Tips

    2:52

    Demo: Evaluation and Optimization

    40:35

    Chapter 8 Quiz

    15:00 Quiz
  • Chapter 9 13 Lessons Implementation and Operations 1:53:15

    Introduction

    0:37

    Concepts

    11:01

    AI Developer Services

    5:45

    Categorizing Uploaded Data Using AWS Step Functions

    0:00 Hands-On Lab

    Coordinating AI Services with Step Functions

    0:00 Hands-On Lab

    Amazon SageMaker Deployments

    10:46

    Other ML Deployment Options

    2:48

    Security

    10:36

    Monitor and Evaluate

    8:01

    Exam Tips

    3:50

    Demo: Implementation and Operations

    40:43

    Demo: Deconstruction

    4:08

    Chapter 9 Quiz

    15:00 Quiz
  • Chapter 10 2 Lessons Wrap-Up 3:00:47

    Good Luck!

    0:47

    AWS Certified Machine Learning - Specialty

    3:00:00 Quiz

What you will need

  • You should have a solid understanding of introductory Machine Learning concepts.

  • You should already be familiar with AWS concepts like the AWS Console, creating an AWS account and common AWS services such as S3 and IAM.

  • You should have a basic understanding of using Jupyter Notebook.

What you can expect

  • You will become familiar with the blueprint coverage and expectations of the AWS Certified Machine Learning - Specialty exam

  • You will gain a solid understanding of the services and platforms available on AWS for machine learning projects.

  • You will build some background on how and why machine learning activities and concepts work in a practical sense.

  • You will be equipped with a level of comfort using the AWS ML portfolio in real-world use cases and applications.

What are Hands-on Labs

What's the difference between theoretical knowledge and real skills? Practical real-world experience. That's where Hands-on Labs come in! Hands-on Labs are guided, interactive experiences that help you learn and practice real-world scenarios in real cloud environments. Hands-on Labs are seamlessly integrated in courses, so you can learn by doing.

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