Professional Level
8 Hands-on labs

AWS Certified Machine Learning - Specialty (LA)

By Tia Williams

Thank you for your interest in this course. Unfortunately, this content is no longer being supported, and some of it may be out of date. Search for "AWS Certified Machine Learning - Specialty 2020" to find the most up-to-date content for your learning journey.

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30 hours
  • 106 Lessons
  • 8 Hands-On Labs
  • 1 Practice Exam

About the course

Thank you for your interest in this course. Unfortunately, this content is no longer being supported, and some of it may be out of date. Search for "AWS Certified Machine Learning – Specialty 2020" to find the most up-to-date content for your learning journey.

Welcome to A Cloud Guru’s AWS Certified Machine Learning – Specialty prep course. This course prepares you to take the AWS Certified Machine Learning – Specialty (MLS-C01) certification exam. It also gives you the hands-on experience required to use machine learning and deep learning in a real-world environment.

This course starts off with coming to grips with Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence (AI) terminology. After the theory comes the practice. You’ll get hands-on with a number of ML frameworks and AWS services specific to the certification.

Course code and other resources can be found here: https://github.com/linuxacademy/content-aws-mls-c01

Lab Highlights

Categorizing Uploaded Data Using AWS Step Functions Coordinating AI Services with Step Functions Automatically Processing Data in S3 Using Lambda Performing Real-Time Data Analysis with Kinesis Creating a scikit-learn Random Forest Classifier in AWS SageMaker Creating an MXNet Image Classifier in AWS SageMaker Creating a TensorFlow Image Classifier in AWS SageMaker Introducing Jupyter Notebooks (AWS SageMaker)
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  • Chapter 1 4 Lessons Getting Started 24:46

    An Important Note About A Cloud Guru and Linux Academy Courses

    1:19

    Course Introduction

    3:49

    About the Training Architect

    1:57

    About the Exam

    17:41
  • Chapter 2 3 Lessons Machine Learning Fundamentals 23:02

    Artificial Intelligence

    4:34

    What Is Machine Learning?

    11:40

    What Is Deep Learning?

    6:48
  • Chapter 3 7 Lessons Machine Learning Concepts 47:34

    Section Introduction

    0:58

    Machine Learning Lifecycle

    13:52

    Supervised, Unsupervised, and Reinforcement

    9:52

    Optimization

    9:38

    Regularization

    3:42

    Hyperparameters

    5:22

    Validation

    4:10
  • Chapter 4 7 Lessons Data 45:06

    Section Introduction

    0:50

    Feature Selection and Engineering

    10:04

    Principal Component Analysis (PCA)

    10:24

    Missing and Unbalanced Data

    11:23

    Label and One Hot Encoding

    4:36

    Splitting and Randomization

    3:44

    RecordIO Format

    4:05
  • Chapter 5 9 Lessons Machine Learning Algorithms 57:28

    Section Introduction

    0:41

    Logistical Regression

    7:54

    Linear Regression

    5:41

    Support Vector Machines

    4:36

    Decision Trees

    9:47

    Random Forests

    5:47

    K-Means

    10:32

    K-Nearest Neighbour

    3:37

    Latent Dirichlet Allocation (LDA) Algorithm

    8:53
  • Chapter 6 4 Lessons Deep Learning Algorithms 36:18

    Section Introduction

    0:41

    Neural Networks

    15:13

    Convolutional Neural Networks (CNN)

    10:28

    Recurrent Neural Networks (RNN)

    9:56
  • Chapter 7 7 Lessons Model Performance and Optimization 1:04:39

    Section Introduction

    1:29

    Confusion Matrix

    11:59

    Sensitivity and Specificity

    15:00

    Accuracy and Precision

    5:41

    ROC/AUC

    18:02

    Gini Impurity

    7:31

    F1 Score

    4:57
  • Chapter 8 11 Lessons Machine Learning Tools and Frameworks 5:18:07

    Section Introduction

    1:49

    Introduction to Jupyter Notebooks

    16:06

    ML and DL Frameworks

    11:54

    TensorFlow

    16:43

    PyTorch

    9:23

    MXNet

    7:35

    Scikit-learn

    14:37

    Introduction to Jupyter Notebooks (AWS SageMaker)

    1:00:00 Hands-On Lab

    Creating a TensorFlow Image Classifier in AWS SageMaker

    1:00:00 Hands-On Lab

    Creating an MXNet Image Classifier in AWS SageMaker

    1:00:00 Hands-On Lab

    Creating a scikit-learn Random Forest Classifier in AWS SageMaker

    1:00:00 Hands-On Lab
  • Chapter 9 10 Lessons AWS Services 2:35:45

    Section Introduction

    1:26

    S3

    21:00

    Glue

    16:34

    Athena

    13:50

    QuickSight

    8:29

    Kinesis, Streams, Firehose, Video, and Analytics

    15:07

    EMR with Spark

    6:21

    EC2 for ML

    10:54

    Amazon ML

    2:04

    Perform Real-Time Data Analysis with Kinesis

    1:00:00 Hands-On Lab
  • Chapter 10 13 Lessons AWS Application Services AI/ML 5:25:56

    Section Introduction

    3:16

    Amazon Rekognition (Images) Part 1

    13:36

    Amazon Rekognition (Images) Part 2 - the API

    23:19

    Amazon Rekognition (Video)

    12:33

    Amazon Polly

    9:23

    Amazon Transcribe

    10:28

    Automatically Process Data in S3 Using Lambda

    45:00 Hands-On Lab

    Amazon Translate

    13:53

    Amazon Comprehend

    14:29

    Amazon Lex

    13:33

    Amazon Service Chaining with AWS Step Functions

    16:26

    Categorizing Uploaded Data Using AWS Step Functions

    1:30:00 Hands-On Lab

    Coordinating AI Services with Step Functions

    1:00:00 Hands-On Lab
  • Chapter 11 5 Lessons Introduction 39:28

    Section Introduction

    1:39

    What is Amazon SageMaker?

    7:19

    The Three Stages

    3:16

    Control (Console/SDK/Notebooks)

    12:07

    SageMaker Notebooks

    15:07
  • Chapter 12 4 Lessons Build 1:04:49

    Data Preprocessing

    13:57

    Ground Truth

    9:42

    Preprocessing Image Data (Pinehead, NotPinehead)

    26:39

    Algorithms

    14:31
  • Chapter 13 6 Lessons Train 57:34

    SageMaker Algorithms - Architecture 1

    10:19

    SageMaker Algorithms - Architecture 2

    7:02

    SageMaker Algorithms - Architecture 3

    5:59

    Training an Image Classifier - Part 1 (Pinehead, NotPinehead)

    19:03

    Training an Image Classifier - Part 2 (Pinehead, NotPinehead)

    4:49

    Hyperparameter Tuning

    10:22
  • Chapter 14 6 Lessons Deploy 51:09

    Inference Pipelines

    3:42

    Real-Time and Batch Inference

    6:01

    Deploy an Image Classifier (Pinehead, NotPinehead)

    16:34

    Accessing Inference from Apps

    3:56

    Create a Custom API for Inference - Part 1 (Pinehead, NotPinehead)

    9:05

    Create a Custom API for Inference - Part 2 (Pinehead, NotPinehead)

    11:51
  • Chapter 15 2 Lessons Security 24:00

    Securing SageMaker Notebooks

    19:14

    SageMaker and the VPC

    4:46
  • Chapter 16 5 Lessons Other AWS Services 59:03

    Section Introduction

    0:53

    DeepLens - Part 1

    24:08

    DeepLens - Part 2

    5:30

    DeepRacer - Part 1

    23:07

    DeepRacer - Part 2

    5:25
  • Chapter 17 3 Lessons The Exam 2:53:25

    How to Answer Questions

    16:17

    How to Prepare

    7:08

    AWS Certified Machine Learning–Specialty (MLS-C01) Final Practice Exam

    2:30:00 Quiz
  • Chapter 18 1 Lesson Thank You 4:37

    Goodbye!

    4:37

What you will need

  • While there are no formal prerequisites, its recommended to hold a current associate or even professional level AWS certification.

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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