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2019 AWS SageMaker and Machine Learning – With Python


Introduction and Housekeeping :
Introduction
Root Account Setup and Billing Dashboard Overview
Enable Access to Billing Data for IAM Users
Create Users Required For the Course
AWS Command Line Interface Tool Setup and Summary
Six Advantages of Cloud Computing
AWS Global Infrastructure Overview

2019 SageMaker Housekeeping :
Downloadable Resources
Demo - S3 Bucket Setup
Demo - Setup SageMaker Notebook Instance
2019 Demo - Source Code and Data Setup

2019 Machine Learning Concepts :
2019 Introduction to Machine Learning, Concepts, Terminologies
2019 Data Types - How to handle mixed data types
2019 Introduction to Python Notebook Environment
2019 Introduction to working with Missing Data
2019 Data Visualization - Linear, Log, Quadratic and More

2019 SageMaker Service Overview :
Downloadable Resources
SageMaker Overview
Compute Instance Families and Pricing
Algorithms and Data Formats Supported For Training and Inference

XGBoost - Gradient Boosted Trees :
Introduction to XGBoost
Source Code Overview
Demo - Create Files in SageMaker Data Formats and Save Files To S3
Demo - Working with XGBoost - Linear Regression Straight Line Fit
Demo - XGBoost Example with Quadratic Fit
Demo - Kaggle Bike Rental Data Setup, Exploration and Preparation
Demo - Kaggle Bike Rental Model Version 1
Demo - Kaggle Bike Rental Model Version 2
Demo - Kaggle Bike Rental Model Version 3
Demo - Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
Demo - Invoking SageMaker Model Endpoints For Real Time Predictions
Demo - Invoking SageMaker Model Endpoints From Client Outside of AWS
How to remove SageMaker endpoints and Shutdown Notebook Instance
Creating EndPoint From Existing Model Artifacts
XGBoost Hyper Parameter Tuning
Demo - XGBoost Multi-Class Classification Iris Data
Demo - XGBoost Binary Classifier For Diabetes Prediction
Demo - XGBoost Binary Classifier for Edible Mushroom Prediction
Summary - XGBoost

SageMaker - Principal Component Analysis (PCA) :
Downloadable Resources
Introduction to Principal Component Analysis (PCA)
PCA Demo Overview
Demo - PCA with Random Dataset
Demo - PCA with Correlated Dataset
Cleanup Resources on SageMaker
Demo - PCA with Kaggle Bike Sharing - Overview and Normalization
Demo - PCA Local Model with Kaggle Bike Train
Demo - PCA training with SageMaker
Demo - PCA Projection with SageMaker
Exercise : Kaggle Bike Train and PCA
Summary

SageMaker - Factorization Machines :
Downloadable Resources
Introduction to Factorization Machines
MovieLens Dataset
Demo - Movie Recommender Data Preparation
Demo - Movie Recommender Model Training
Demo - Movie Predictions By User

SageMaker - DeepAR Time Series Forecasting :
Downloadable Resources
Introduction to DeepAR Time Series Forecasting
DeepAR Training and Inference Formats
Working with Time Series Data, Handling Missing Values
Demo - Bike Rental as Time Series Forecasting Problem
Demo - Bike Rental Model Training
Demo - Bike Rental Prediction
Demo - DeepAR Categories
Demo - DeepAR Dynamic Features Data Preparation
Demo - DeepAR Dynamic Features Training and Prediction
Summary

2019 Integration Options - Model Endpoint :
Downloadable Resources
Integration Overview
Install Python and Boto3 - Local Machine
Install SageMaker SDK, GIT Client, Source Code, Security Permissions
Client to Endpoint using SageMaker SDK
Client to Endpoint using Boto3 SDK
Microservice - Lambda to Endpoint - Payload
Microservice - Lambda to Endpoint
Microservice - API Gateway, Lambda to Endpoint

2019 SageMaker HyperParameter Tuning :
Downloadable Resources
Introduction to Hyperparameter Tuning
Lab: Tuning Movie Rating Factorization Machine Recommender System
Lab: Step 2 Tuning Movie Rating Recommender System

AWS Machine Learning Service :
2019 MARCH - Important Update: AWS Machine Learning Service Deprecated
Python Development Environment and Boto3 Setup
Project Source Code and Data Setup
Lab: Intro to Python Jupyter Notebook Environment, Pandas, Matplotlib
Lab: AWS S3 Bucket Setup and Configure Security
Summary
Introduction and House Keeping Quiz
5 questions
Optional: Machine Learning Where To Start (Article)
Machine Learning Terminology
Data Types supported by AWS Machine Learning
Linear Regression Introduction
Binary Classification Introduction
Multiclass Classification Introduction
Data Visualization - Linear, Log, Quadratic and More
Algorithm and Terminology Quiz
10 questions

Linear Regression :
Lab: Linear Model, Squared Error Loss Function, Stochastic Gradient Descent
Lab: Linear Regression for complex shapes
Summary
Linear Regression Quiz
5 questions

AWS - Linear Regression Models :
Lab: Simple Training Data
Lab: Datasource
Lab: Train Model with default recipe
AWS Models Quiz
7 questions
Concept - How to evaluate regression model accuracy?
Lab: Evaluate predictive quality of the trained model
Lab: Review Default Recipe Settings Used To Train model
Lab: Train Model With Custom Recipe and Review Performance
Model Performance Summary and Conclusion
AWS Regression Metrics Quiz
7 questions

Adding Features To Improve Model :
Lab: Quadratic Fit Training Data
Lab: Underfitting With Linear Features
Lab: Normal Fit With Quadratic Features
Summary

Normalization :
Lab: Impact of Features With Different Magnitude
Concept: Normalization to smoothen magnitude differences
Lab: Train Model With Feature Normalizaton
Summary
Underfitting and Normalization Quiz
4 questions

Adding Complex Features :
Lab: Prepare Training Data
Lab: Adding Complex Features
Lab: Train Model With Higher Order Features
Lab: Performance Of Model With Degree 1 Features
Lab: Performance of Model with Degree 4 Features
Lab: Performance of Model With Degree 15 Features
Summary

Kaggle Bike Hourly Rental Prediction :
Review Kaggle Bike Train Problem And Dataset
Lab: Train Model To Predict Hourly Rental
Lab: Evaluate Prediction Quality
Linear Regression Wrapup and Summary

Logistic Regression :
Binary Classification - Logistic Regression, Loss Function, Optimization
Lab: Binary Classification Approach
True Positive, True Negative, False Positive and False Negative
Lab: Logistic Optimization Objectives
Lab: Logistic Cost Function
Lab: Cost Example
Optimizing Weights
Summary
Logistic Regression Quiz
5 questions

Onset of Diabetes Prediction :
Problem Objective, Input Data and Strategy
Lab: Prepare For Training
Lab: Training a Classification Model
Concept: Classification Metrics
Concept: Classification Insights with AWS Histograms
Concept: AUC Metric
Lab: Review Diabetes Model Performance
Lab: Cutoff Threshold Interactive Testing
Lab: Evaluating Prediction Quality With Additional Dataset
Lab: Batch Prediction and Compute Metrics
Summary
Logistic Regression Metrics Quiz
4 questions

Multiclass Classifiers using Multinomial Logistic Regression :
Lab: Iris Classifcation
Lab: Train Classifier with Default and Custom Recipe
Concept: Evaluating Predictive Quality of Multiclass Classifiers
Concept: Confusion Matrix To Evaluating Predictive Quality
Lab: Evaluate Performance of Iris Classifiers using Default Recipe
Lab: Evaluate Performance of Iris Classifiers using Custom Recipe
Lab: Batch Prediction and Computing Metrics using Python Code
Summary

Text Based Classification with AWS Twitter Dataset :
AWS Twitter Feed Classification for Customer Service
Lab: Train, Evaluate Model and Assess Predictive Quality
Lab: Interactive Prediction with AWS
Logistic Regression Summary

Data Transformation using Recipes :
Recipe Overview
Recipe Example
Text Transformation
Numeric Transformation - Quantile Binning
Numeric Transformation - Normalization
Cartesian Product Transformation - Categorical and Text
Summary

Hyper Parameters, Model Optimization and Lifecycle :
Introduction
Data Rearrangement, Maximum Model Size, Passes, Shuffle Type
Regularization, Learning Rate
Regularization Effect
Improving Model Quality
Model Maintenance
AWS Machine Learning System Limits
AWS Machine Learning Pricing

Integration of AWS Machine Learning With Your Application :
Introduction
Integration Scenarios
Security using IAM
Hands-on lab - List of Demos and Objective
Lab: Enable Real Time End Point and Configure IAM Prediction User
Lab: Invoking Prediction From AWS Command Line Interface
Lab: Invoking Prediction From Python Client
Lab: Python Client to Train, Evaluate Models and Integrate with AWS
Lab: Invoking Prediction From Web Page AngularJS Client
Demo Allowing Prediction Only For Registered Users
Cognito Overview
Lab: Cognito User Pool Configuration
Lab: AngularJS Web Client - Invoke Prediction for authorized users
Lab: Invoke Machine Learning Service From AWS EC2 Instance
Summary

Homework - Additional Problems :
Mushroom Classification

Conclusion :
BONUS: Learn Advanced Data Processing Techniques, Cloud Computing and More
Conclusion