AWS Certified Machine Learning Specialty 2022 – Hands On! Free Download

Get AWS Certified Machine Learning Specialty 2022 – Hands On! Free Download

[ Updated for 2022’s latest SageMaker features 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:

  • 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 StudioSageMaker Model MonitorSageMaker Autopilot, and SageMaker Debugger.
  • Regularization techniques
  • Evaluating machine learning models (precision, recall, F1, confusion matrix, etc.)
  • High-level ML services: ComprehendTranslatePollyTranscribeLexRekognition, 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.

Who this course is for:

  • Individuals performing a development or data science role seeking certification in machine learning and AWS.

Course 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 Classes + Glacier
  • Amazon S3 Storage + Glacier – Hands On
  • Amazon S3 Lifecycle Rules
  • Amazon S3 Lifecycle Rules – Hands On
  • Amazon S3 Security
  • Kinesis Data Streams & 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
  • 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
  • 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 2
  • Lab: Preparing Data for TF-IDF with Spark and EMR, Part 3
  • Quiz: Exploratory Data Analysis

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

  • Introduction to Deep Learning
  • Activation Functions
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Deep Learning on EC2 and EMR
  • Regularization Techniques for Neural Networks (Dropout, Early Stopping)
  • L1 and L2 Regularization
  • Grief with Gradients: The Vanishing Gradient problem
  • The Confusion Matrix
  • Precision, Recall, F1, AUC, and more
  • Ensemble Methods: Bagging and Boosting
  • Quiz: Deep Learning and Machine Learning

Modeling, Part 2: 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 SageMaker Experiments
  • SageMaker Debugger
  • Preview
  • SageMaker Model Monitor
  • Other recent features (JumpStart, Data Wrangler, Features Store, Edge Manager)
  • SageMaker Canvas
  • SageMaker Training Compiler
  • Quiz: Modeling

Modeling, Part 3: High-Level ML Services:

  • Amazon Comprehend
  • Amazon Translate
  • Amazon Transcribe
  • Amazon Rekognition
  • Amazon Forecast
  • Amazon Lex
  • Amazon Personalize
  • Lightning round! TexTract, DeepLens, DeepRacher, Lookout, and Monitron
  • TorchServe, AWS Neuron, and AWS Panorama
  • Deep Composer, Fraud Detection, CodeGuru, and Contact Lens
  • Amazon Kendra and Amazon Augmented AI (A2I)
  • Quiz: High-Level ML Services

Modeling, Part 4: Wrapping up & Lab Activity:

  • 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

ML Implementation and Operations:

  • Section Intro: Machine Learning Implementation and Operations
  • 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 Serverless Inference and Inference Recommender
  • 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
  • You Made It!
  • Save 50% on your AWS Exam Cost!
  • Get an Extra 30 Minutes on your AWS Exam – Non Native English Speakers only

Practice Exams:

  • Warmup Test: Quick Assessment
  • THANK YOU!
  • Bonus Lecture

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This course is intended for audiences of all experiences who are interested in learning about new skills in a business context; there are no prerequisite courses.

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