AWS Certified Machine Learning Specialty MLS-C01 [2024]

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Chandra Lingam

16:55:55

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01 - Introduction and Housekeeping

001 2020-Benefits-of-Cloud-Computing.pdf

001 AWS-Certified-Machine-Learning-Specialty-Preparation.pdf

001 AWS-Housekeeping.pdf

001 AWS-SageMaker-Course-Introduction.pdf

001 Downloadable Resources.html

001 Gap-Analysis.xlsx

002 Introduction.mp4

02:49

003 Increase the speed of learning.html

004 Overview - AWS Machine Learning Specialty Exam.mp4

09:05

005 Exam - Gap Analysis.html

006 Preparation - AWS Machine Learning Specialty Exam.mp4

04:21

007 Lab - AWS Account Setup, Free Tier Offers, Billing, Support.mp4

07:00

008 Lab - Billing Alerts, Delegate Access.mp4

08:10

009 Lab - Configure IAM Users, Setup Command Line Interface (CLI).mp4

11:30

010 [Optional] Total Cost of Ownership between On-Premises and Cloud.html

011 Benefits of Cloud Computing.mp4

08:39

012 AWS Global Infrastructure Overview.mp4

10:31

013 Security is Job Zero AWS Public Sector Summit 2016 by Steve Schmidt.html

014 Weekly AWS Study Group Session.html

02 - SageMaker Housekeeping

001 AWS-Introduction-ML-Concepts.pdf

001 Downloadable Resources.html

001 SourceCodeSetup.pdf

001 usa-airpassengers-numeric.xlsx

002 Lab - S3 Bucket Setup.mp4

02:52

003 Lab - Setup SageMaker Notebook Instance.mp4

02:49

004 Lab - Source Code Setup.mp4

02:25

005 Kaggle Data Setup.html

006 SageMaker Console looks different from the course videos - Why.html

007 How to download Kaggle data with code.html

03 - Machine Learning Concepts

001 Introduction to Machine Learning, Concepts, Terminologies.mp4

10:23

002 Data Types - How to handle mixed data types.mp4

12:42

003 Lab - Python Notebook Environment.mp4

10:33

004 Lab - Working with Missing Data.mp4

09:35

005 Lab - Data Visualization - Linear, Log, Quadratic and More.mp4

04:38

04 - Model Performance Evaluation

001 Model Performance.html

002 Downloadable Resources.html

002 Model-Performance-Evaluation.pdf

003 Introduction.mp4

03:26

004 Lab - Regression Model Performance.mp4

09:58

005 Lab - Binary Classifier Performance.mp4

08:00

006 Lab - Binary Classifier - Confusion Matrix.mp4

06:56

007 Lab - Binary Classifier - SKLearn Confusion Matrix.mp4

03:18

008 Binary Classifier - Metrics Definition.mp4

03:52

009 Binary Classifier - Metrics Calculation.mp4

04:26

010 Question - Why not Model 1.html

011 Binary Classifier - Area Under Curve Metrics.mp4

09:39

012 Lab - Multiclass Classifier.mp4

12:35

013 Model Performance.html

05 - SageMaker Service Overview

001 2020.01-AWS-SageMaker-WM.pdf

001 AWS-SageMaker-Algorithms-and-Frameworks.pdf

001 Downloadable Resources.html

002 How is AWS SageMaker different from other ML frameworks.html

003 Introduction to SageMaker.mp4

04:54

004 Instance Type and Pricing.mp4

10:21

005 CloudPractitionerReview-InfraPricingSupport.pdf

005 Save Money on SageMaker Usage.html

006 DataFormat.mp4

11:12

007 SageMaker Built-in Algorithms.mp4

09:36

008 Popular Frameworks and Bring Your Own Algorithm.mp4

05:24

009 CloudPractitionerReview-InfraPricingSupport.pdf

009 Infrastructure, Pricing, Support - Review.html

06 - SageMaker Service and SDK Changes

001 Overview of recent changes.html

001 SageMakerServiceAndSDK.pdf

002 Model Training using Console.mp4

08:02

003 Model Training using Python SDK.mp4

08:19

004 Incremental Training.html

005 Lab - Review the SageMaker console for Training Job.html

07 - XGBoost - Gradient Boosted Trees

001 Downloadable Resources.html

001 XGBoost-WM.pdf

002 Introduction to XGBoost.mp4

08:52

003 Lab - Data Preparation Simple Regression.mp4

05:06

004 Lab - Training Simple Regression.mp4

12:25

005 Lab - Data Preparation Non-linear Data set.mp4

02:39

006 Lab - Training Non-linear Data set.mp4

04:47

007 Exercise - Improving quality of predictions.html

008 Lab - Data Preparation Bike Rental Regression.mp4

08:24

009 Lab - Train Bike Rental Regression Model.mp4

06:09

010 Lab - Train using Log of Count.mp4

04:14

011 ResourceLimitExceeded Error - How to Increase Resource Limit.html

012 Lab - How to train using SageMakers built-in XGBoost Algorithm.mp4

07:36

013 Q&A How does SageMaker built-in know the target variable.html

014 Lab - How to run predictions against an existing SageMaker Endpoint.mp4

04:29

015 SageMaker Endpoint Features.mp4

05:41

016 SageMaker Spot Instances - Save up to 90% for training jobs.html

017 Lab - Multi-class Classification.mp4

05:41

018 Lab - Binary Classification.mp4

06:21

019 Exercise - Improve Data Quality in Diabetes dataset.html

020 Question on Diabetes Data Quality Improvement.html

021 Question on Diabetes model - is group mean on target the right approach.html

022 HyperParameter Tuning, Bias-Variance, Regularization (L1, L2).mp4

11:08

023 Exercise - Mushroom Classification.html

08 - Invoke Model Endpoint From External Clients

001 AWS-SageMaker-Integration.pdf

001 Install SageMaker SDK, GIT Client, Source Code, Security Permissions.html

001 Local-Machine-Housekeeping.pdf

002 IAM users for the lab.html

003 Integration Overview.mp4

02:32

004 Lab - Client to Endpoint using SageMaker SDK.mp4

09:26

005 Lab - Client to Endpoint using Boto3 SDK.mp4

03:50

006 Microservice - Lambda to Endpoint - Payload.mp4

03:24

007 Lambda UI Changes.html

008 Lab - Microservice - Lambda to Endpoint.mp4

09:09

009 API Gateway - UI Changes.html

010 Lab - API Gateway, Lambda, Endpoint.mp4

10:34

09 - Endpoint Changes with Zero Downtime

001 Downloadable Resources.html

001 SageMakeEndpoints.pdf

002 [Repeat] Endpoint Features, Monitoring and AutoScaling.mp4

05:41

003 How to handle changes to production system.mp4

07:54

004 Lab - AB Testing Multiple Production Variants.mp4

11:53

005 Lab Multi-model Endpoint.mp4

08:15

006 Run Models at the Edge.html

10 - Emerging AI Trends and Social Issues

001 Downloadable Resources.html

001 FairnessAndExplainability.pdf

002 Is AI Biased.mp4

07:40

003 Tools to Detect Bias - Clarify, Experiments, Model Monitor, Augmented AI.mp4

07:41

004 3.SageMaker-Tools.pdf

004 And Some More Tools.html

11 - Cloud Security and Access Management

001 2020-IAM.pdf

001 Introduction.html

002 Shared Responsibility Model, Compliance, Delegation, Federation.mp4

08:33

003 Credentials, MFA, Identity-based, Resources-based Policy.mp4

07:24

004 Inline and Managed Policy, Amazon Resource Naming (ARN) Convention.mp4

08:30

005 Principal, Effect, Action, Resource, Not Clause.mp4

06:55

006 Conditional Access, Implicit Deny, Explicit Allow and Deny, Permission Boundary.mp4

07:42

007 IAM Roles, Cross-account access options.mp4

06:17

008 Federation, SSO, SAML, Active Directory, AWS Organizations, Cognito.mp4

06:16

009 Lab - Identity-based policy, Implicit Deny, Explicit Allow.mp4

05:01

010 Lab - Policy Generator, Managed Policy, Versions, Groups.mp4

05:40

011 Lab - Resource-based policy, Policy Generator, Principals.mp4

05:14

12 - Principal Component Analysis (PCA)

001 Normalization and Standardization.html

002 AWS-SageMakerPCA-WM.pdf

002 Downloadable Resources.html

003 Introduction to Principal Component Analysis (PCA).mp4

05:49

004 PCA Demo Overview.mp4

01:16

005 Demo - PCA with Random Dataset.mp4

03:29

006 Demo - PCA with Correlated Dataset.mp4

05:26

007 Cleanup Resources on SageMaker.html

008 Demo - PCA with Kaggle Bike Sharing - Overview and Normalization.mp4

03:51

009 Demo - PCA Local Mode with Kaggle Bike Train.mp4

03:30

010 Demo - PCA training with SageMaker.mp4

04:22

011 Demo - PCA Projection with SageMaker.mp4

02:42

012 Exercise Kaggle Bike Train and PCA.html

013 Summary.mp4

01:22

13 - Recommender Systems - Factorization Machines

001 Recommender System.html

002 2.AWS-SageMakerFactorizationMachine-WM.pdf

002 Downloadable Resources.html

003 Introduction to Factorization Machines.mp4

05:59

004 MovieLens Dataset.html

005 Demo - Movie Recommender Data Preparation.mp4

10:35

006 Demo - Movie Recommender Model Training.mp4

05:34

007 Demo - Movie Predictions By User.mp4

07:10

14 - Model Optimization and HyperParameter Tuning

001 AWS-SageMaker-Hyperparameter-Tuning.pdf

001 Downloadable Resources.html

001 FM-Autotuning-Lab-Configuration.xlsx

002 Introduction to Hyperparameter Tuning.mp4

06:11

003 Lab Tuning Movie Rating Factorization Machine Recommender System.mp4

18:05

004 Lab Step 2 Tuning Movie Rating Recommender System.mp4

05:00

005 HyperParameter, Bias-Variance, Regularization (L1, L2) [Repeat from XGBoost].mp4

11:08

006 Nuts and Bolts of Optimization.html

007 Model Optimization - related question.html

15 - Time Series Forecasting - DeepAR

001 AWS-SageMakerDeepAR-WM.pdf

001 Downloadable Resources.html

002 Introduction to DeepAR Time Series Forecasting.mp4

09:47

003 DeepAR Training and Inference Formats.mp4

09:49

004 Working with Time Series Data, Handling Missing Values.mp4

09:58

005 Demo - Bike Rental as Time Series Forecasting Problem.mp4

11:43

006 Demo - Bike Rental Model Training.mp4

07:21

007 Demo - Bike Rental Prediction.mp4

04:50

008 Demo - DeepAR Categories.mp4

06:10

009 Demo - DeepAR Dynamic Features Data Preparation.mp4

06:34

010 Demo - DeepAR Dynamic Features Training and Prediction.mp4

03:05

011 Summary.mp4

01:16

012 Question How to train a model for different products using DeepAR.html

16 - Anomaly Detection - Random Cut Forest

001 2020-RandomCutForest.pdf

001 Downloadable Resources.html

002 Introduction to Random Cut Forest and Intuition Behind Anomaly Detection.mp4

10:10

003 Lab - Taxi Passenger Traffic Analysis (AWS Provided Example).mp4

08:53

004 Lab - Auto Sales Analysis.mp4

05:54

17 - Artificial Intelligence (AI) Services

001 AWS-AI-Services.pdf

001 Downloadable Resources.html

002 Lab Instructions.html

003 1. Introduction.mp4

03:15

004 2.1 Amazon Transcribe and Lab.mp4

05:32

005 2.2 Amazon Transcribe and Lab.mp4

06:34

006 3. Amazon Translate.mp4

04:29

007 Translate - Practical Scenario.html

008 4.1 Amazon Comprehend.mp4

05:42

009 Pricing Comprehend.html

010 4.2 Amazon Comprehend.mp4

05:00

011 4.3 Amazon Comprehend training.mp4

08:35

012 5. Amazon Polly.mp4

04:16

013 6. Amazon Lex.mp4

06:48

014 7. Amazon Rekognition.mp4

08:21

015 8. Amazon Textract & Summary.mp4

03:02

18 - S3 Data Lake Architecture - Data Consolidation

001 AWS-Data-Lake.pdf

001 Downloadable Resources.html

002 Lab Instructions.html

003 Introduction to Data Lake.mp4

10:28

004 Kinesis - Streaming and Batch Processing.mp4

05:23

005 Data Formats and Tools for Data Format Conversion.mp4

08:34

006 In-Place Analytics and Portfolio of Tools.mp4

04:46

007 Monitoring and Optimization.mp4

06:16

008 Security and Protection.mp4

06:36

009 Lab Instructions - Glue Data Catalog.html

010 Lab Glue Data Catalog.mp4

08:31

011 Lab Instructions Athena In-place Querying.html

012 Lab - Query with Athena.mp4

02:01

013 Lab - Glue ETL - Convert format to Parquet.mp4

04:43

014 Lab - Query Amazon Customer Reviews with Athena.mp4

05:06

015 Lab Sentiment of the Customer Review.mp4

06:06

016 Lab - Query Sentiment of Customer Reviews using Athena.mp4

04:17

017 Lambda UI Changes.html

018 Lab Serverless Customer Review Solution Part 1.mp4

09:45

019 Lab Serverless Customer Review Solution Part 2.mp4

07:51

19 - Deep Learning and Neural Networks

001 AWS-Deep-Learning-01.pdf

001 ReadMe and Downloadable Resources.html

002 Lab Instructions.html

003 Concepts - Gradient Descent, Loss Function for Regression.mp4

14:11

004 Concepts - Gradient Descent, Loss Function for Classification.mp4

10:02

005 Neural Networks and Deep Learning.mp4

07:35

006 Introduction to Deep Learning.html

007 Convolutional Neural Network (CNN).html

008 Recurrent Neural Networks (RNN), LSTM.html

009 Generative Adversarial Networks (GANs).html

010 Real World Blind Face Restoration.html

011 Nuts and Bolts of Optimization [Repeat].html

012 Lab - Regression with SKLearn Neural Network.mp4

06:38

013 Lab - Regression with Keras and TensorFlow.mp4

07:23

014 Customer Churn Data.html

014 churn.txt

015 Lab - Binary Classification - Part 1- Customer Churn Prediction.mp4

05:59

016 Lab - Binary Classification - Part 2 - Customer Churn Prediction.mp4

07:32

017 Lab - Multiclass Classification - Iris.mp4

04:48

018 Transfer Learning.html

019 Optimizing for GPUs.html

020 Multi-Class Multi-Label Classification.html

20 - Bring Your Own Algorithm

001 How to use TensorFlow, Pytorch, SKLearn in SageMaker.html

002 AWS-SageMaker-Custom-Algorithms-and-Frameworks.pdf

002 Downloadable Resources.html

003 Introduction and How built-in algorithms work.mp4

05:06

004 Custom Image and Popular Framework.mp4

03:55

005 Folder Structure and Environment Variables.mp4

07:19

006 Lab - SKLearn Estimator Bring Your Own Part 1.mp4

09:22

007 Lab - SKLearn Estimator Bring Your Own Part 2.mp4

08:15

008 Lab - TensorFlow Estimator Bring Your Own.mp4

03:51

21 - Storage for Servers

001 AWS-Storage.pdf

001 Downloadable Resources.html

002 Introduction to Storage.mp4

08:40

003 Elastic Block Store (EBS).mp4

13:09

004 Elastic File System, FSx for Windows, FSx for Lustre.mp4

04:52

005 Elastic Block Store (EBS) Encryption.html

22 - AWS - Support Plans and Feedback

001 AWS Product Improvement Feedback.html

002 How to contact AWS for Production Support.mp4

07:14

23 - Databases on AWS

001 AWS-Database-Distribution-ML.pdf

001 Downloadable Resources.html

002 AWS Databases - Introduction, Benefits, and Types.mp4

08:09

003 Relational Database Service (RDS) - Features and Benefits.mp4

12:41

004 Aurora and Aurora Serverless Relational Database.mp4

04:47

005 DynamoDB - Primary Key, Partitions, and Features.mp4

08:03

006 Cassandra and DocumentDB.mp4

02:30

007 Amazon ElastiCache - Usage Example, Features.mp4

05:29

008 Amazon Redshift.mp4

02:26

24 - On-Premises usage and other technologies

001 SageMaker Canvas Service No Code ML.html

002 On-Premises Usage and other technologies.html

25 - Practice Exam - AWS Certified Machine Learning Specialty

001 Sections to Review.html

002 AWS Exam Readiness.html

26 - Other Resources

001 How to Access Discount Vouchers.html

002 Congratulations!.html

Description

Experience AWS SageMaker: A Practical Course with Hands-On Learning, Practice Tests and Certification Preparation

What You'll Learn?

You will gain first-hand experience on how to train, optimize, deploy, and integrate ML in AWS cloud
AWS Built-in algorithms, Bring Your Own, Ready-to-use AI capabilities
Complete Guide to AWS Certified Machine Learning – Specialty (MLS-C01)
Includes a high-quality Timed practice test (a lot of courses charge a separate fee for practice test)
Zero Downtime Model Deployment
How to Integrate and Invoke ML from your Application
Automated Hyperparameter Tuning

Who is this for?

This course is designed for anyone who is interested in AWS cloud based machine learning and data science

AWS Certified Machine Learning - Specialty Preparation

What You Need to Know?

Familiarity with Python

AWS Account - I will walk through steps to setup one

Basic knowledge of Pandas, Numpy, Matplotlib

Be an active learner and use course discussion forum if you need help - Please don't put help needed items in course review

More details

Description
Learn about cloud based machine learning algorithms, how to integrate with your applications and Certification Prep
Welcome to AWS Machine Learning Specialty Course!
In this course, you will gain practical experience with AWS SageMaker through hands-on labs that demonstrate specific concepts. We will begin by setting up your SageMaker environment. If you are new to machine learning, you will learn how to handle mixed data types, missing data, and how to verify the quality of the model. These topics are essential for machine learning practitioners and the certification exam.
SageMaker uses containers to package algorithms and frameworks, such as Pytorch and TensorFlow. The container-based approach provides a standard interface for building and deploying your models, and it is easy to convert your model into a production application. Through a series of concise labs, you will train, deploy, and invoke your first SageMaker model.
Like any other software project, a machine-learning solution also requires continuous improvement. We will look at how to safely incorporate new changes in a production system, perform A/B testing, and even roll back changes when necessary, all with zero downtime to your application.
We will also discuss emerging social trends in the fairness of machine learning and AI systems. What will you do if your users accuse your model of being racially or gender-biased? How will you handle it? In this section, we will cover the concept of fairness, how to explain a decision made by the model, different types of bias, and how to measure them.
We will also cover cloud security and how to protect your data and model from unauthorized use. You will learn about recommender systems and how to incorporate features such as movie and product recommendations. The algorithms you learn in the course are state-of-the-art, and tuning them for your dataset can be challenging. We will look at how to tune your model with automated tools, and you will gain experience in time series forecasting, anomaly detection, and building custom deep-learning models.
With the knowledge you gain in this course, and the included high-quality practice exam, you will be well-prepared to achieve the AWS Certified Machine Learning - Specialty certification. I am looking forward to meeting you and helping you succeed in this course. Thank you!
Who this course is for:
This course is designed for anyone who is interested in AWS cloud based machine learning and data science
AWS Certified Machine Learning - Specialty Preparation

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Chandra Lingam is an experienced professional in AWS, with a strong background in mission-critical systems and machine learning. He has a wealth of knowledge and expertise in systems development for both traditional IT data centers and cloud computing.Chandra's courses on the AWS certification have been highly praised and were even mentioned in a recent Udemy earnings call by the CEO. Prior to his work as a course developer and instructor, he spent 15 years as a software engineer at Intel.Chandra holds a Master's in Computer Science from Arizona State University and a Bachelor's in Computer Science from Thiagarajar College of Engineering in Madurai.His courses offer valuable and up-to-date content for both beginners and experienced IT professionals looking to advance their skills in AWS and related technologies.

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