AWS Machine Learning, AI, SageMaker - With Python
  • ondemand_video
       Video Length : 27h00m0s
  • format_list_bulleted
       Tasks Number : 180
  • group
       Students Enrolled : 867
  • equalizer
       Medium Level
  • Curriculum
  • 1. Introduction and Housekeeping
    • videocam
      Introduction
      10m0s
    • videocam
      Root Account Setup and Billing Dashboard Overview
      10m0s
    • videocam
      Enable Access to Billing Data for IAM Users
      10m0s
    • videocam
      Create Users Required For the Course
      10m0s
    • videocam
      AWS Command Line Interface Tool Setup and Summary
      10m0s
    • videocam
      Six Advantages of Cloud Computing
      10m0s
    • videocam
      AWS Global Infrastructure Overview
      10m0s
    • videocam
      Course Structure
      10m0s
  • 2. AWS Machine Learning Service
    • videocam
      Python Development Environment and Boto3 Setup
      10m0s
    • videocam
      Project Source Code and Data Setup
      10m0s
    • videocam
      Lab: Intro to Python Jupyter Notebook Environment, Pandas, Matplotlib
      10m0s
    • videocam
      Lab: AWS S3 Bucket Setup and Configure Security
      10m0s
    • videocam
      Summary
      10m0s
    • videocam
      Introduction and House Keeping Quiz
      10m0s
    • videocam
      Optional: Machine Learning Where To Start (Article)
      10m0s
    • videocam
      Machine Learning Terminology
      10m0s
    • videocam
      Data Types supported by AWS Machine Learning
      10m0s
    • videocam
      Linear Regression Introduction
      10m0s
    • videocam
      Binary Classification Introduction
      10m0s
    • videocam
      Multiclass Classification Introduction
      10m0s
    • videocam
      Data Visualization - Linear, Log, Quadratic and More
      10m0s
    • videocam
      Algorithm and Terminology Quiz
      10m0s
  • 3. Linear Regression
    • videocam
      Lab: Linear Model, Squared Error Loss Function, Stochastic Gradient Descent
      10m0s
    • videocam
      Lab: Linear Regression for complex shapes
      10m0s
    • videocam
      Summary
      10m0s
    • videocam
      Linear Regression Quiz
      10m0s
  • 4. AWS - Linear Regression Models
    • videocam
      Lab: Simple Training Data
      10m0s
    • videocam
      Lab: Datasource
      10m0s
    • videocam
      Lab: Train Model with default recipe
      10m0s
    • videocam
      AWS Models Quiz
      10m0s
    • videocam
      Concept - How to evaluate regression model accuracy?
      10m0s
    • videocam
      Lab: Evaluate predictive quality of the trained model
      10m0s
    • videocam
      Lab: Review Default Recipe Settings Used To Train model
      10m0s
    • videocam
      Lab: Train Model With Custom Recipe and Review Performance
      10m0s
    • videocam
      Model Performance Summary and Conclusion
      10m0s
    • videocam
      AWS Regression Metrics Quiz
      10m0s
  • 5. Adding Features To Improve Model
    • videocam
      Lab: Quadratic Fit Training Data
      10m0s
    • videocam
      Lab: Underfitting With Linear Features
      10m0s
    • videocam
      Lab: Normal Fit With Quadratic Features
      10m0s
    • videocam
      Summary
      10m0s
  • 6. Normalization
    • videocam
      Lab: Impact of Features With Different Magnitude
      10m0s
    • videocam
      Concept: Normalization to smoothen magnitude differences
      10m0s
    • videocam
      Lab: Train Model With Feature Normalizaton
      10m0s
    • videocam
      Summary
      10m0s
    • videocam
      Underfitting and Normalization Quiz
      10m0s
  • 7. Adding Complex Features
    • videocam
      Lab: Prepare Training Data
      10m0s
    • videocam
      Lab: Adding Complex Features
      10m0s
    • videocam
      Lab: Train Model With Higher Order Features
      10m0s
    • videocam
      Lab: Performance Of Model With Degree 1 Features
      10m0s
    • videocam
      Lab: Performance of Model with Degree 4 Features
      10m0s
    • videocam
      Lab: Performance of Model With Degree 15 Features
      10m0s
    • videocam
      Summary
      10m0s
  • 8. Kaggle Bike Hourly Rental Prediction
    • videocam
      Review Kaggle Bike Train Problem And Dataset
      10m0s
    • videocam
      Lab: Train Model To Predict Hourly Rental
      10m0s
    • videocam
      Lab: Evaluate Prediction Quality
      10m0s
    • videocam
      Linear Regression Wrapup and Summary
      10m0s
  • 9. Logistic Regression
    • videocam
      Binary Classification - Logistic Regression, Loss Function, Optimization
      10m0s
    • videocam
      Lab: Binary Classification Approach
      10m0s
    • videocam
      True Positive, True Negative, False Positive and False Negative
      10m0s
    • videocam
      Lab: Logistic Optimization Objectives
      10m0s
    • videocam
      Lab: Logistic Cost Function
      10m0s
    • videocam
      Lab: Cost Example
      10m0s
    • videocam
      Optimizing Weights
      10m0s
    • videocam
      Summary
      10m0s
    • videocam
      Logistic Regression Quiz
      10m0s
  • 10. Onset of Diabetes Prediction
    • videocam
      Problem Objective, Input Data and Strategy
      10m0s
    • videocam
      Lab: Prepare For Training
      10m0s
    • videocam
      Lab: Training a Classification Model
      10m0s
    • videocam
      Concept: Classification Metrics
      10m0s
    • videocam
      Concept: Classification Insights with AWS Histograms
      10m0s
    • videocam
      Concept: AUC Metric
      10m0s
    • videocam
      Lab: Review Diabetes Model Performance
      10m0s
    • videocam
      Lab: Cutoff Threshold Interactive Testing
      10m0s
    • videocam
      Lab: Evaluating Prediction Quality With Additional Dataset
      10m0s
    • videocam
      Lab: Batch Prediction and Compute Metrics
      10m0s
    • videocam
      Summary
      10m0s
    • videocam
      Logistic Regression Metrics Quiz
      10m0s
  • 11. Multiclass Classifiers using Multinomial Logistic Regression
    • videocam
      Lab: Iris Classifcation
      10m0s
    • videocam
      Lab: Train Classifier with Default and Custom Recipe
      10m0s
    • videocam
      Concept: Evaluating Predictive Quality of Multiclass Classifiers
      10m0s
    • videocam
      Concept: Confusion Matrix To Evaluating Predictive Quality
      10m0s
    • videocam
      Lab: Evaluate Performance of Iris Classifiers using Default Recipe
      10m0s
    • videocam
      Lab: Evaluate Performance of Iris Classifiers using Custom Recipe
      10m0s
    • videocam
      Lab: Batch Prediction and Computing Metrics using Python Code
      10m0s
    • videocam
      Summary
      10m0s
  • 12. Text Based Classification with AWS Twitter Dataset
    • videocam
      AWS Twitter Feed Classification for Customer Service
      10m0s
    • videocam
      Lab: Train, Evaluate Model and Assess Predictive Quality
      10m0s
    • videocam
      Lab: Interactive Prediction with AWS
      10m0s
    • videocam
      Logistic Regression Summary
      10m0s
  • 13. Data Transformation using Recipes
    • videocam
      Recipe Overview
      10m0s
    • videocam
      Recipe Example
      10m0s
    • videocam
      Text Transformation
      10m0s
    • videocam
      Numeric Transformation - Quantile Binning
      10m0s
    • videocam
      Numeric Transformation - Normalization
      10m0s
    • videocam
      Cartesian Product Transformation - Categorical and Text
      10m0s
    • videocam
      Summary
      10m0s
  • 14. Hyper Parameters, Model Optimization and Lifecycle
    • videocam
      Introduction
      10m0s
    • videocam
      Data Rearrangement, Maximum Model Size, Passes, Shuffle Type
      10m0s
    • videocam
      Regularization, Learning Rate
      10m0s
    • videocam
      Regularization Effect
      10m0s
    • videocam
      Improving Model Quality
      10m0s
    • videocam
      Model Maintenance
      10m0s
    • videocam
      AWS Machine Learning System Limits
      10m0s
    • videocam
      AWS Machine Learning Pricing
      10m0s
  • 15. Integration of AWS Machine Learning With Your Application
    • videocam
      Introduction
      10m0s
    • videocam
      Integration Scenarios
      10m0s
    • videocam
      Security using IAM
      10m0s
    • videocam
      Hands-on lab - List of Demos and Objective
      10m0s
    • videocam
      Lab: Enable Real Time End Point and Configure IAM Prediction User
      10m0s
    • videocam
      Lab: Invoking Prediction From AWS Command Line Interface
      10m0s
    • videocam
      Lab: Invoking Prediction From Python Client
      10m0s
    • videocam
      Lab: Python Client to Train, Evaluate Models and Integrate with AWS
      10m0s
    • videocam
      Lab: Invoking Prediction From Web Page AngularJS Client
      10m0s
    • videocam
      Demo Allowing Prediction Only For Registered Users
      10m0s
    • videocam
      Cognito Overview
      10m0s
    • videocam
      Lab: Cognito User Pool Configuration
      10m0s
    • videocam
      Lab: AngularJS Web Client - Invoke Prediction for authorized users
      10m0s
    • videocam
      Lab: Invoke Machine Learning Service From AWS EC2 Instance
      10m0s
    • videocam
      Summary
      10m0s
  • 16. SageMaker XGBoost
    • videocam
      SageMaker Overview
      10m0s
    • videocam
      Compute Instance Families and Pricing
      10m0s
    • videocam
      Algorithms and Data Formats Supported For Training and Inference
      10m0s
    • videocam
      XGBoost - Introduction and Comparison with Other Approaches
      10m0s
    • videocam
      Demo 1 : S3 Bucket Setup
      10m0s
    • videocam
      Demo 2 : Setup Notebook Instance on SageMaker
      10m0s
    • videocam
      Source Code and Data Setup
      10m0s
    • videocam
      Demo 3 : Source Code and Data Setup
      10m0s
    • videocam
      Demo 4 : Create Files in SageMaker Data Formats and Save Files To S3
      10m0s
    • videocam
      Issues and Fix for XGBoost Local Mode
      10m0s
    • videocam
      Demo 5 : Working with XGBoost - Linear Regression Straight Line Fit
      10m0s
    • videocam
      Demo 6 : XGBoost Example with Quadratic Fit
      10m0s
    • videocam
      Demo 7 : Kaggle Bike Rental Data Setup, Exploration and Preparation
      10m0s
    • videocam
      Demo 8 : Kaggle Bike Rental Model Version 1
      10m0s
    • videocam
      Demo 9 : Kaggle Bike Rental Model Version 2
      10m0s
    • videocam
      Demo 10 : Kaggle Bike Rental Model Version 3
      10m0s
    • videocam
      Demo 11: Training on SageMaker Cloud - Kaggle Bike Rental Model Version 3
      10m0s
    • videocam
      SageMaker Endpoint, Serializer, Deserializer
      10m0s
    • videocam
      Demo 12 : Invoking SageMaker Model Endpoints For Real Time Predictions
      10m0s
    • videocam
      Demo 13 : Invoking SageMaker Model Endpoints From Client Outside of AWS
      10m0s
    • videocam
      How to remove SageMaker endpoints and Shutdown Notebook Instance
      10m0s
    • videocam
      Creating EndPoint From Existing Model Artifacts
      10m0s
    • videocam
      XGBoost Hyper Parameter Tuning
      10m0s
    • videocam
      Demo 14 : XGBoost Multi-Class Classification Iris Data
      10m0s
    • videocam
      Demo 15 : XGBoost Binary Classifier For Diabetes Prediction
      10m0s
    • videocam
      Demo 16 : XGBoost Binary Classifier for Edible Mushroom Prediction
      10m0s
    • videocam
      Summary - XGBoost
      10m0s
  • 17. SageMaker - Principal Component Analysis (PCA)
    • videocam
      Introduction PCA and SageMaker PCA
      10m0s
    • videocam
      PCA Demo Source Code setup
      10m0s
    • videocam
      Demo 1: PCA with Random Dataset
      10m0s
    • videocam
      Demo 2: PCA with Correlated Dataset
      10m0s
    • videocam
      How to Cleanup Resources on SageMaker?
      10m0s
    • videocam
      Demo 3.1: PCA with Kaggle Bike Sharing - Overview and Normalization
      10m0s
    • videocam
      Demo 3.2: PCA Local Model with Kaggle Bike Train
      10m0s
    • videocam
      Demo 3.3: PCA training with SageMaker
      10m0s
    • videocam
      Demo 3.4: PCA Projection with SageMaker
      10m0s
    • videocam
      Exercise : Kaggle Bike Train and PCA
      10m0s
    • videocam
      Summary
      10m0s
  • 18. Factorization Machines For Recommender Systems and Click Rate Prediction
    • videocam
      Introduction to Factorization Machines
      10m0s
    • videocam
      Demo - Movie Recommender Data Preparation
      10m0s
    • videocam
      Demo - Movie Recommender Model Training
      10m0s
    • videocam
      Demo - Movie Predictions By User
      10m0s
    • videocam
      More SageMaker Algorithms - Coming Soon
      10m0s
Authors

Kevin Gautama is a systems design and programming engineer with 16 years of expertise in the fields of electrical and electronics and information technology.

He teaches at the Hanoi University of Industry in the period 2003-2011 and he has a certificate of vocational training by the Ministry of Industry and Commerce and the Hanoi University of Industry.

From extensive design experience through numerous engineering projects, the author founded the Enziin Academy.

The Enziin Academy is a startup in the field of educational, it's core goal is to training design engineers in the fields technology related.

The Enziin Academy is headquartered in Stockholm-Sweden with an orientation operating multi-lingual and global.

The author's skills in IT:

  • Implementing the application infrastructure on Amazon's cloud computing platform.
  • Linux server system administration (Sysadmin).
  • Design load balancing and content distribution system.
  • MySQL database administration.
  • C/C++/C# Programming
  • Ruby and Ruby on Rails Programming
  • Python and Django Programming
  • The WPF/C# on the .NET Framework Programming
  • The PHP/JAVA Programming
  • Machine Learning and Expert System.
  • Internet of Things.

The author's skills in the fields of electric and electronic:

  • The design of popular CPU / MCU systems.
  • Design FPGA / CPLD system (Xilinx - Altera).
  • Design and programming of DSP systems (Texas Instruments).
  • Embedded ARM system design.
  • The RTOS Programming
  • Design and programming electronic power systems.
  • PLC - inverter - sensor - electric control cabinet industrial.
  • Control systems distributed connection with Server.

Read more...

AWS Machine Learning, AI, SageMaker - With Python


AWS Machine Learning, AI, SageMaker - With Python

Discussions
You must login to comment.