2019 AWS SageMaker and Machine Learning - With Python
4.3 (681 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.
6,792 students enrolled

2019 AWS SageMaker and Machine Learning - With Python

Learn about cloud based machine learning algorithms and how to integrate with your applications
Bestseller
4.3 (681 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.
6,792 students enrolled
Created by Chandra Lingam
Last updated 4/2019
English
Current price: $11.99 Original price: $199.99 Discount: 94% off
2 days left at this price!
30-Day Money-Back Guarantee
This course includes
  • 19.5 hours on-demand video
  • 22 articles
  • 23 downloadable resources
  • 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

Requirements
  • All materials and software instructions are covered in housekeeping lecture
  • Familiarity with a programming language
  • AWS Account – if you want to try the hands-on activities. AWS charges a small amount for model creation and predictions
  • Some basic knowledge of Pandas, Numpy, Matplotlib would be helpful but not absolutely needed
Description

*** 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 ***

There are several courses on Machine Learning and AI.  What is special 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 most useful algorithms. Don’t waste your time sifting through mountains of techniques that are in the wild

  3. Cloud based service is very easy to integrate with your application and has support for wide variety of programming languages.

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

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

In this course, you will learn AI and Machine Learning in three different ways:

AWS Machine Learning

AWS Machine Learning Service is designed for complete beginners.   

You will learn three popular easy to understand linear algorithms from the ground-up  

You will gain hands-on knowledge on complete lifecycle – from model development, measuring quality, tuning, and integration with your application

AWS SageMaker

The next service is AWS SageMaker.  

If you are comfortable coding in Python, SageMaker service is for you.  

You will learn how to deploy your own Jupyter Notebook instance on the AWS Cloud.  

You will gain hands-on model development experience on very powerful and popular machine learning algorithms like  

  • XGBoost – a gradient boosted tree algorithm that has won several competitions,  

  • Recurrent Neural Networks for Time Series forecasting,  

  • Factorization Machines for high dimensional sparse datasets like Click Stream data  

  • Neural Network based Image Classifiers,  

  • Dimensionality reduction with Principal Component Analysis  

  • and much more

Application Services

In Application Services section of this course,  

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

You will gain hands-on experience in ready-to-use Vision service for image and video analysis, Conversation chatbots and Language Services for text translation, Speech recognition, and text to speech and more

I am looking forward to seeing you in the course.


Who this course is for:
  • This course is designed for anyone who is interested in machine learning and data science
  • If you are new to machine learning, this is a perfect course to upskill yourself and fastest way to learn machine learning
  • If you are an experienced practitioner, you will gain insight into AWS Machine Learning capability and learn how you can convert your ideas into highly scalable solution in a matter of days
  • AWS Certification - If you are preparing for certification, you will learn best practices and gain hands-on experience on securely deploying products using AWS Cloud
Course content
Expand all 229 lectures 19:42:16
+ Introduction and Housekeeping
7 lectures 44:03

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

Preview 02:38
Root Account Setup and Billing Dashboard Overview
03:41
Enable Access to Billing Data for IAM Users
05:14
Create Users Required For the Course
14:51
AWS Command Line Interface Tool Setup and Summary
05:08
Six Advantages of Cloud Computing
06:08
AWS Global Infrastructure Overview
06:23
+ 2019 SageMaker Housekeeping
4 lectures 13:54

Following Downloadable Resources are available in this lecture:

1. Source Code and Data Setup Document

2. Introduction to Machine Learning and Concepts Document

Downloadable Resources
00:05
Demo - S3 Bucket Setup
02:52
Demo - Setup SageMaker Notebook Instance
06:47
2019 Demo - Source Code and Data Setup
04:10
+ 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 SageMaker Service Overview
4 lectures 07:20
Downloadable Resources
00:02
SageMaker Overview
02:20
Compute Instance Families and Pricing
03:03
Algorithms and Data Formats Supported For Training and Inference
01:55
+ XGBoost - Gradient Boosted Trees
19 lectures 01:56:43

"XGBoost (eXtreme Gradient Boosting) is a popular and efficient open-source implementation of the gradient boosted trees algorithm. Gradient boosting is a supervised learning algorithm that attempts to accurately predict a target variable by combining the estimates of a set of simpler, weaker models. XGBoost has done remarkably well in machine learning competitions because it robustly handles a variety of data types, relationships, and distributions, and the large number of hyperparameters that can be tweaked and tuned for improved fits. This flexibility makes XGBoost a solid choice for problems in regression, classification (binary and multiclass), and ranking"

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

Introduction to XGBoost
07:14
Source Code Overview
03:16
Demo - Create Files in SageMaker Data Formats and Save Files To S3
07:57
Demo - Working with XGBoost - Linear Regression Straight Line Fit
12:36
Demo - XGBoost Example with Quadratic Fit
04:08
Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation
11:53
Demo - Kaggle Bike Rental Model Version 1
10:45
Demo - Kaggle Bike Rental Model Version 2
04:43
Demo - Kaggle Bike Rental Model Version 3
04:02
Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
13:55
Demo - Invoking SageMaker Model Endpoints For Real Time Predictions
05:10
Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS
03:33
How to remove SageMaker endpoints and Shutdown Notebook Instance
00:28
Creating EndPoint From Existing Model Artifacts
00:13
XGBoost Hyper Parameter Tuning
05:54
Demo - XGBoost Multi-Class Classification Iris Data
09:25
Demo - XGBoost Binary Classifier For Diabetes Prediction
04:57
Demo - XGBoost Binary Classifier for Edible Mushroom Prediction
04:22
Summary - XGBoost
02:12
+ SageMaker - Principal Component Analysis (PCA)
12 lectures 32:41
Downloadable Resources
00:02

"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 Model 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
00:24
Summary
01:22
+ SageMaker - Factorization Machines
6 lectures 29:31
Downloadable Resources
00:02

"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
00:11
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
11 lectures 01:10:33
Downloadable Resources
00:02

"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
+ AWS Machine Learning Service
13 lectures 01:02:11
2019 MARCH - Important Update: AWS Machine Learning Service Deprecated
00:32
  1. Setup Anaconda Python Development Environment
  2. Install Boto3 Module needed for AWS
Python Development Environment and Boto3 Setup
03:28

1. Setup Course Folder in local machine

2. Download Project Source Code

3. Download Data files

Project Source Code and Data Setup
04:09

Introduction to Python Development Environment, Pandas, NumPy, Matplotlib

Lab: Intro to Python Jupyter Notebook Environment, Pandas, Matplotlib
13:24
  1. Setup Simple Storage Service (S3) Bucket and Security Policies to allow access to machine learning
  2. S3 is the storage location where training, evaluation and test file will be kept
Lab: AWS S3 Bucket Setup and Configure Security
08:02

Summary of Introduction, Development Environment Setup and AWS Configuration

Summary
01:18
Introduction and House Keeping Quiz
5 questions
Optional: Machine Learning Where To Start (Article)
04:02

Learn about standard machine learning terminologies that we will be using in this course

Machine Learning Terminology
03:38

Introduces data types supported by AWS Machine Learning with examples

Data Types supported by AWS Machine Learning
02:55

Introduces linear regression models with examples; hands-on python data analysis for assessing the input features.

Linear Regression Introduction
05:41

Introduces binary classification models with examples; hands-on python data analysis for assessing the input features.

Binary Classification Introduction
04:09

Introduces multiclass classification models with examples; hands-on python data analysis for assessing the input features.

Multiclass Classification Introduction
03:02

Overview of plotting capability in python using Matplotlib and how various functions appear in a graph.  Useful for understanding relation between input and output attributes.

Data Visualization - Linear, Log, Quadratic and More
07:50
Algorithm and Terminology Quiz
10 questions
+ Linear Regression
3 lectures 22:54

Understand concepts behind linear regression.  How does the algorithm predict a numeric value, what optimization steps does it go through, how does it evaluate cost/loss and more

Lab: Linear Model, Squared Error Loss Function, Stochastic Gradient Descent
15:46

Understand how linear regression is able to fit non-linear shapes

Lab: Linear Regression for complex shapes
05:09
Summary
01:59
Linear Regression Quiz
5 questions