2020 AWS SageMaker, AI and Machine Learning Specialty Exam
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2020 AWS SageMaker, AI and Machine Learning Specialty Exam

Complete Guide to AWS Certified Machine Learning (MLS-C01) - Specialty and Practice Test
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4.5 (1,874 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
15,053 students enrolled
Created by Chandra Lingam
Last updated 6/2020
English
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Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 14 hours on-demand video
  • 54 articles
  • 26 downloadable resources
  • 1 Practice Test
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Learn AWS Machine Learning algorithms, Predictive Quality assessment, Model Optimization
  • Integrate predictive models with your application using simple and secure APIs
  • Convert your ideas into highly scalable products in days
  • Practice test and resources to gain AWS Certified Machine Learning - Specialty Certification (MLS-C01)
Course content
Expand all 181 lectures 14:18:04
+ Introduction and Housekeeping
12 lectures 56:32

The following downloadable resources are available as part of this lecture:

1. AWS SageMaker Course Introduction.pdf

2. AWS Certified Machine Learning Specialty-Preparation.pdf

3. Gap-Analysis.xlsx

4. AWS Housekeeping.pdf

5. 2020 Benefits of Cloud Computing.pdf

Preview 00:07

Introduction to AWS Machine Learning Course, Topics Covered, Course Structure

Preview 03:03
Increase the speed of learning
00:37

How  to set up an AWS account

Different free tier offers from AWS

How to view the charges accrued in your account, and

How to contact AWS support if you need help

2020 AWS Account Setup, Free Tier Offers, Billing, Support
07:00

How to delegate billing access to other authorized users in our account

Configure free tier usage alerts

Set up billing alerts using Cloud Watch and AWS Budget

2020 Billing Alerts, Delegate Access
08:10

Configure IAM users required for this course

Set up the AWS command-line tool in your laptop and set the access key credentials.

2020 Configure IAM Users, Setup Command Line Interface (CLI)
11:30
2020 Benefits of Cloud Computing
06:12
2020 AWS Global Infrastructure Overview
05:58
Security is Job Zero | AWS Public Sector Summit 2016 by Steve Schmidt
00:04
+ SageMaker Housekeeping
7 lectures 09:21

Following Downloadable Resources are available in this lecture:

1. Source Code Setup Document

2. Introduction to Machine Learning and Concepts Document

3. usa_airpassengers_numeric.xlsx

Downloadable Resources
00:05
Lab - S3 Bucket Setup
02:52
2020 Lab - Setup SageMaker Notebook Instance
02:49
2020 Lab - Source Code Setup
02:25
Kaggle Data Setup
00:18
2020 SageMaker Console looks different from the course videos - Why?
00:38
2020 How to download Kaggle data with code?
00:13
+ 2019 Machine Learning Concepts
5 lectures 47:50
2019 Introduction to Machine Learning, Concepts, Terminologies
10:23
2019 Data Types - How to handle mixed data types
12:41
2019 Introduction to Python Notebook Environment
10:33
2019 Introduction to working with Missing Data
09:35
2019 Data Visualization - Linear, Log, Quadratic and More
04:38
+ 2019.7 Model Performance Evaluation
11 lectures 01:03:00

Following Downloadable Resources are available in this lecture:

Model Performance Evaluation Presentation

For exercises in this section, get the latest code from GitHub

https://github.com/ChandraLingam/AmazonSageMakerCourse

If you need help, please refer to SageMaker House Keeping section on how to get the latest code

Downloadable Resources
00:10
Introduction
03:26
Regression Model Performance
09:58
Binary Classifier Performance
08:00
Binary Classifier - Confusion Matrix
06:55
Binary Classifier - SKLearn Confusion Matrix
03:18
Binary Classifier - Metrics Definition
03:52
Binary Classifier - Metrics Calculation
04:26
Question - Why not Model 1?
00:41
Binary Classifier - Area Under Curve Metrics
09:39
Multiclass Classifier
12:35
+ 2020 SageMaker Service Overview
6 lectures 41:35
2020 Downloadable Resources
00:10
Introduction to SageMaker
04:54
Instance Type and Pricing
10:20
DataFormat
11:12
2020 SageMaker Built-in Algorithms
09:35
2020 Popular Frameworks and Bring Your Own Algorithm
05:24
+ 2019 XGBoost - Gradient Boosted Trees
23 lectures 01:35:47
2019 Downloadable Resources
00:02

"The XGBoost (eXtreme Gradient Boosting) is a popular and efficient open-source implementation of the gradient boosted trees algorithm". 

https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html

Introduction to XGBoost and how it compares to the Linear Model, Decision Tree, and Ensemble Methods

2019 Introduction to XGBoost
08:52

Let's compare performance of XGBoost and Linear Model using a simple regression dataset

2019 Lab - Data Preparation Simple Regression
05:06
Q&A - XGBoost Install Taking Too Long - How to Fix?
00:24

Train model using XGBoost and Linear Regression. Evaluate performance using Plots, Residual Histograms and RMSE metrics

2019 Lab - Training Simple Regression
12:25

Let's compare performance of XGBoost and Linear Model using a non-linear data set

2019 Lab - Data Preparation Non-linear Data set
02:39
2019 Lab - Training Non-linear Data set
04:47
Exercise - Improving quality of predictions
00:12

In this lab, let's look at Bike Rental demand forecasting problem.  This is an old competition problem from Kaggle: https://www.kaggle.com/c/bike-sharing-demand/data.  To download data files, you need to register with Kaggle (it's free).

2019 Lab - Data Preparation Bike Rental Regression
08:24

In this lab, let's train our model for forecasting hourly bike rental counts.  This is a complex non-linear data set that has seasonality, trend and several factors that impact rentals.  Evaluate quality of predictions using Plots, Residual Histograms, RMSE and RMSLE metrics.  Finally, submit the results at Kaggle for test data.

2019 Lab - Train Bike Rental Regression Model
06:09

In this lab, let's transform the target using log operation.  Log of target can help when the target is a count/integer, it has seasonality and trend.  After model predicts the value, we need to apply inverse transform (exp) to get the count back.

2019 Lab - Train using Log of Count
04:14
ResourceLimitExceeded Error - How to Increase Resource Limit
00:38

In this lab, let's train bike rental model on SageMaker's built-in XGBoost Algorithm.  We will walk through the fours steps for using a SageMaker algorithm

2019 Lab - How to train using SageMaker's built-in XGBoost Algorithm
07:36

In this lab, let's look at the steps involved in connecting to an existing SageMaker endpoint, security of an endpoint, how to send multiple observations in each call.

2019 Lab - How to run predictions against an existing SageMaker Endpoint
04:29
Additional Integration Scenarios and Examples
00:05

Let's look at key benefits of a managed Endpoint.  SageMaker takes care of automatic replacement of unhealthy instances, AutoScaling infrastructure based on workload, hosting multiple versions of model behind an endpoint, and metrics published to CloudWatch.

2019 SageMaker Endpoint Features
05:41

In this lab, let's look at multi-class classification using XGBoost.

2019 Lab - Multi-class Classification
05:41

In this lab, let's look at a how to perform Binary Classification using XGBoost.  We will use the diabetes data set in this lab

2019 Lab - Binary Classification
06:21
2019 Exercise - Improve Data Quality in Diabetes dataset
00:19
2020 - Question on Diabetes Data Quality Improvement
00:15
2020 - Question on Diabetes model - is group mean on target the right approach?
00:04

In this lecture, let's look at important XBoost Hyperparameters.  We will also look at Bias, Variance, Regularization (L1, L2), and Automatic Tuning

2019 XGBoost HyperParameters and Tuning
11:07
2019 Exercise - Mushroom Classification
00:15
Quiz - XGBoost
8 questions
+ SageMaker - Principal Component Analysis (PCA)
13 lectures 33:08
Normalization and Standardization
00:23
Downloadable Resources
00:07

"PCA is an unsupervised machine learning algorithm that attempts to reduce the dimensionality (number of features) within a dataset while still retaining as much information as possible. This is done by finding a new set of features called components, which are composites of the original features that are uncorrelated with one another. They are also constrained so that the first component accounts for the largest possible variability in the data, the second component the second most variability, and so on."

https://docs.aws.amazon.com/sagemaker/latest/dg/pca.html

Introduction to Principal Component Analysis (PCA)
05:49
PCA Demo Overview
01:16
Demo - PCA with Random Dataset
03:29
Demo - PCA with Correlated Dataset
05:26
Cleanup Resources on SageMaker
00:28
Demo - PCA with Kaggle Bike Sharing - Overview and Normalization
03:51
Demo - PCA Local Mode with Kaggle Bike Train
03:30
Demo - PCA training with SageMaker
04:22
Demo - PCA Projection with SageMaker
02:42
Exercise : Kaggle Bike Train and PCA
00:23
Summary
01:22
+ SageMaker - Factorization Machines
6 lectures 29:33
Downloadable Resources
00:07

"A factorization machine is a general-purpose supervised learning algorithm that you can use for both classification and regression tasks. It is an extension of a linear model that is designed to capture interactions between features within high dimensional sparse datasets economically. For example, in a click prediction system, the factorization machine model can capture click rate patterns observed when ads from a certain ad-category are placed on pages from a certain page-category. Factorization machines are a good choice for tasks dealing with high dimensional sparse datasets, such as click prediction and item recommendation."

https://docs.aws.amazon.com/sagemaker/latest/dg/fact-machines.html

Introduction to Factorization Machines
05:59
MovieLens Dataset
00:08
Demo - Movie Recommender Data Preparation
10:35
Demo - Movie Recommender Model Training
05:34
Demo - Movie Predictions By User
07:10
+ SageMaker - DeepAR Time Series Forecasting
12 lectures 01:11:30
Downloadable Resources
00:06

"The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN)"

https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html

Introduction to DeepAR Time Series Forecasting
09:47
DeepAR Training and Inference Formats
09:48
Working with Time Series Data, Handling Missing Values
09:58
Demo - Bike Rental as Time Series Forecasting Problem
11:43
Demo - Bike Rental Model Training
07:21
Demo - Bike Rental Prediction
04:50
Demo - DeepAR Categories
06:10
Demo - DeepAR Dynamic Features Data Preparation
06:34
Demo - DeepAR Dynamic Features Training and Prediction
03:05
Summary
01:15
Question: How to train a model for different products using DeepAR?
00:53
+ 2019 Integration Options for Model Endpoint
9 lectures 41:41
Downloadable Resources
00:03
Integration Overview
02:32
Install Python and Boto3 - Local Machine
02:39
Install SageMaker SDK, GIT Client, Source Code, Security Permissions
00:04
Client to Endpoint using SageMaker SDK
09:26
Client to Endpoint using Boto3 SDK
03:50
Microservice - Lambda to Endpoint - Payload
03:24
Microservice - Lambda to Endpoint
09:09
Microservice - API Gateway, Lambda to Endpoint
10:34
Requirements
  • 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
Description

***Start learning now for a chance to win the AWS Machine Learning Specialty Exam Voucher (USD 300)

The process is simple: You need to complete my 2020 AWS SageMaker, AI, and Machine Learning Specialty Exam course by July-30-2020.

One winner will be chosen at random from all active students who have completed the course.

I will announce and contact the winner by August-7-2020***

Learn about cloud based machine learning algorithms, how to integrate with your applications and Certification Prep

*** UPDATE APR-2020 Bring Your Own Algorithm - We take a behind the scene look at the SageMaker Training and Hosting Infrastructure for your own algorithms. With Labs ***

*** UPDATE FEB-2020 Subtitles and Closed Caption Available - I spent several hours cleaning and editing manually for an accurate subtitle ***

*** UPDATE JAN-2020 Timed Practice Test and additional lectures for Exam Preparation added

For  Practice Test, look for the section: 2020 Practice Exam - AWS Certified Machine Learning Specialty

For exam overview, gap analysis and preparation strategy, look for 2020 - Overview - AWS Machine Learning Specialty Exam

***

*** UPDATE DEC-2019  Third update for this month!!! AWS Certified Machine Learning Specialty Exam Overview and Preparation Strategies lectures added to the course!  Timed Practice Exam is coming soon!

Also added, two new lectures that gives an overview of all SageMaker Built-in Algorithms, Frameworks and Bring-Your-Own Algorithm Supports

Look for lectures starting with 2020

***

*** UPDATE DEC-2019.  In the  Neural Network and Deep Learning section, we will look at  the core concepts behind neural networks, why deep learning is popular these days, different network architectures and hands-on labs to build models using Keras, TensorFlow, Apache MxNet: 2020 Deep Learning and Neural Networks

***

*** UPDATE DEC-2019.  New reference architecture section with hands-on lab that demonstrates how to build a data lake solution using AWS Services and the best practices: 2020 AWS S3 Data Lake Architecture. This topic covers essential services and how they work together for a cohesive solution.  Covers critical topics like S3, Athena, Glue, Kinesis, Security, Optimization, Monitoring and more.

***

*** UPDATE NOV-2019. AWS Artificial Intelligence material is now live!

Within a few minutes, you will learn about algorithms for sophisticated facial recognition systems, sentiment analysis, conversational interfaces with speech and text and much more.

***

*** UPDATE OCT-2019. New XGBoost Lectures, Labs, do-it-yourself exercises, quizzes, Autoscaling, high availability,  Monitoring, security, and lots of good stuff

*** UPDATE MAY-2019.  1. Model endpoint integration with hands-on-labs for (Direct Client, Microservice, API Gateway).  2. Hyperparameter Tuning - Learn how to automatically tune hyperparameters ***

*** UPDATE MARCH-12-2019.  I came to know that new accounts are not able to use AWSML Service.  AWS is asking new users to use SageMaker Service. 

I have restructured the course to start with SageMaker Lectures First.  Machine Learning Service Lectures are still available in the later parts of the course.  Newly updated sections start with 2019 prefix.

All source code for SageMaker Course is now available on Github

The new house keeping lectures cover all the steps for setting up code from GitHub.

***


*** SageMaker Lectures -  DeepAR - Time Series Forecasting, XGBoost - Gradient Boosted Tree algorithm in-depth with hands-on.  XGBoost has won several competitions and is a very popular Regression and Classification Algorithm, Factorization Machine based Recommender Systems and PCA for dimensionality reduction ***

Benefits

There are several courses on Machine Learning and AI. What is unique about this course?

Here are the top reasons:

1. Cloud-based machine learning keeps you focused on the current best practices.

2. In this course, you will learn the most useful algorithms.  Don’t waste your time sifting through mountains of techniques that are in the wild

4. Cloud-based service is straightforward to integrate with your application and has support for a wide variety of programming languages.

5. Whether you have small data or big data, the elastic nature of the AWS cloud allows you to handle them all.

6. There is also No upfront cost or commitment – Pay only for what you need and use

Hands-on Labs

In this course, you will learn with hands-on labs and work on exciting and challenging problems

What exactly will you learn in this course?

Here are the things that you will learn in this course:

AWS SageMaker

* You will learn how to deploy a Notebook instance on the AWS Cloud.

* You will gain insight into algorithms provided by SageMaker service

* Learn how to train, optimize and deploy your models

AI Services

In the AI Services section of this course,

* You will learn about a set of pre-trained services that you can directly integrate with your application.

* Within a few minutes, you can build image and video analysis applications – like face recognition

* You can develop solutions for natural language processing, like finding sentiment, text translation, and conversational chatbots.

Integration

* Learning algorithms is one part of the story - You need to know how to integrate the trained models in your application.

* You will learn how to host your models, scale on-demand, handle failures

* Provide a clean interface for the applications using Lambda and API Gateway

Data Lake

* Data management is one of the most complex and time-consuming activities when working on machine learning projects.

* With AWS, you have a variety of powerful tools for ingesting, cataloging, transforming, securing, visualization of your data assets.

* We will build a data lake solution in this course.

Machine Learning Certification

* If you are planning to get AWS Machine Learning Specialty Certification, you will find all the resources that you need to pass the exam in this course.

Timed Practice Exam and Quizzes

Source Code

* The source code for this course available on Git and that ensures you always get the latest code

Ideal Student

* The ideal student for this course is willing to learn, participate in the course Q&A forum when you need help, and you need to be comfortable coding in Python.

Author

My name is Chandra Lingam, and I am the instructor for this course.

I have over 50,000 thousand students

I spend a considerable amount of time keeping myself up-to-date and teach cloud technologies from the basics.

I have the following AWS Certifications: Solutions Architect, Developer, SysOps, Solutions Architect Professional, Machine Learning Specialty.

I am looking forward to meeting you.

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