AWS Certified Machine Learning Specialty 2022 – Hands On!

AWS Certified Machine Learning Specialty 2022 – Hands On!

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 127 lectures (11h 9m) | 2.73 GB

AWS machine learning certification preparation – learn SageMaker, feature engineering, data engineering, modeling & more

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:

  • 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.

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
Table of Contents

1 Udemy 101
2 Course Introduction What to Expect
3 Get the Course Materials

Data Engineering
4 Section Intro Data Engineering
5 Amazon S3 – Overview
6 Amazon S3 Storage Classes + Glacier
7 Amazon S3 Storage + Glacier – Hands On
8 Amazon S3 Lifecycle Rules
9 Amazon S3 Lifecycle Rules – Hands On
10 Amazon S3 Security
11 Kinesis Data Streams & Kinesis Data Firehose
12 Lab 1.1 – Kinesis Data Firehose
13 Kinesis Data Analytics
14 Lab 1.2 – Kinesis Data Analytics
15 Kinesis Video Streams
16 Kinesis ML Summary
17 Glue Data Catalog & Crawlers
18 Lab 1.3 – Glue Data Catalog
19 Glue ETL
20 Lab 1.4 – Glue ETL
21 Lab 1.5 – Athena
22 Lab 1 – Cleanup
23 AWS Data Stores in Machine Learning
24 AWS Data Pipelines
25 AWS Batch
26 AWS DMS – Database Migration Services
27 AWS Step Functions
28 Full Data Engineering Pipelines
29 Data Engineering Summary

Exploratory Data Analysis
30 Section Intro Data Analysis
31 Python in Data Science and Machine Learning
32 Example Preparing Data for Machine Learning in a Jupyter Notebook
33 Types of Data
34 Data Distributions
35 Time Series Trends and Seasonality
36 Introduction to Amazon Athena
37 Overview of Amazon Quicksight
38 Types of Visualizations, and When to Use Them
39 Elastic MapReduce (EMR) and Hadoop Overview
40 Apache Spark on EMR
41 EMR Notebooks, Security, and Instance Types
42 Feature Engineering and the Curse of Dimensionality
43 Imputing Missing Data
44 Dealing with Unbalanced Data
45 Handling Outliers
46 Binning, Transforming, Encoding, Scaling, and Shuffling
47 Amazon SageMaker Ground Truth and Label Generation
48 Lab Preparing Data for TF-IDF with Spark and EMR, Part 1
49 Lab Preparing Data for TF-IDF with Spark and EMR, Part 2
50 Lab Preparing Data for TF-IDF with Spark and EMR, Part 3

Modeling, Part 1 General Deep Learning and Machine Learning
51 Section Intro Modeling
52 Introduction to Deep Learning
53 Activation Functions
54 Convolutional Neural Networks
55 Recurrent Neural Networks
56 Deep Learning on EC2 and EMR
57 Tuning Neural Networks
58 Regularization Techniques for Neural Networks (Dropout, Early Stopping)
59 L1 and L2 Regularization
60 Grief with Gradients The Vanishing Gradient problem
61 The Confusion Matrix
62 Precision, Recall, F1, AUC, and more
63 Ensemble Methods Bagging and Boosting

Modeling, Part 2 Amazon SageMaker
64 Introducing Amazon SageMaker
65 Linear Learner in SageMaker
66 XGBoost in SageMaker
67 Seq2Seq in SageMaker
68 DeepAR in SageMaker
69 BlazingText in SageMaker
70 Object2Vec in SageMaker
71 Object Detection in SageMaker
72 Image Classification in SageMaker
73 Semantic Segmentation in SageMaker
74 Random Cut Forest in SageMaker
75 Neural Topic Model in SageMaker
76 Latent Dirichlet Allocation (LDA) in SageMaker
77 K-Nearest-Neighbors (KNN) in SageMaker
78 K-Means Clustering in SageMaker
79 Principal Component Analysis (PCA) in SageMaker
80 Factorization Machines in SageMaker
81 IP Insights in SageMaker
82 Reinforcement Learning in SageMaker
83 Automatic Model Tuning
84 Apache Spark with SageMaker
85 SageMaker Studio, and SageMaker Experiments
86 SageMaker Debugger
87 SageMaker Autopilot AutoML
88 SageMaker Model Monitor
89 Other recent features (JumpStart, Data Wrangler, Features Store, Edge Manager)
90 SageMaker Canvas
91 SageMaker Training Compiler

Modeling, Part 3 High-Level ML Services
92 Amazon Comprehend
93 Amazon Translate
94 Amazon Transcribe
95 Amazon Polly
96 Amazon Rekognition
97 Amazon Forecast
98 Amazon Lex
99 Amazon Personalize
100 Lightning round! TexTract, DeepLens, DeepRacher, Lookout, and Monitron
101 TorchServe, AWS Neuron, and AWS Panorama
102 Deep Composer, Fraud Detection, CodeGuru, and Contact Lens
103 Amazon Kendra and Amazon Augmented AI (A2I)

Modeling, Part 4 Wrapping up & Lab Activity
104 Putting them All Together
105 Lab Tuning a Convolutional Neural Network on EC2, Part 1
106 Lab Tuning a Convolutional Neural Network on EC2, Part 2
107 Lab Tuning a Convolutional Neural Network on EC2, Part 3

ML Implementation and Operations
108 Section Intro Machine Learning Implementation and Operations
109 SageMaker’s Inner Details and Production Variants
110 SageMaker On the Edge SageMaker Neo and IoT Greengrass
111 SageMaker Security Encryption at Rest and In Transit
112 SageMaker Security VPC’s, IAM, Logging, and Monitoring
113 SageMaker Resource Management Instance Types and Spot Training
114 SageMaker Resource Management Elastic Inference, Automatic Scaling, AZ’s
115 SageMaker Serverless Inference and Inference Recommender
116 SageMaker Inference Pipelines
117 Lab Tuning, Deploying, and Predicting with Tensorflow on SageMaker – Part 1
118 Lab Tuning, Deploying, and Predicting with Tensorflow on SageMaker – Part 2
119 Lab Tuning, Deploying, and Predicting with Tensorflow on SageMaker – Part 3

Wrapping Up
120 Section Intro Wrapping Up
121 More Preparation Resources
122 Test-Taking Strategies, and What to Expect
123 You Made It!
124 Save 50% on your AWS Exam Cost!
125 Get an Extra 30 Minutes on your AWS Exam – Non Native English Speakers only

Practice Exams
127 Bonus Lecture