Amazon Machine Learning Training

Amazon Machine Learning generally helps the Data Scientists and Data Analysts in creating Machine Learning Application using AWS


Duration: 2 Days

Course fee:$399.00 (₹0)


Product Description

Amazon Machine Learning focuses on the creation of Machine Learning Application using Amazon Web Services (AWS) making the complete Application on Cloud Computing Platforms. The Amazon Machine Learning Training discusses in detail the basic information about the Machine Learning Application  including predictive analysis generated for the application. Many of the important and relevant information including the core concepts are discussed in Amazon Machine Learning online training. Large number of algorithms are focused primarily for the developers intending to work with Machine Learning with Amazon.  Amazon Machine Learning Online Training will bring the aspirants particularly for Data Scientists and Managers of Predictive Analytics Projects. The training will teach the aspirants on leveraging Amazon Machine Learning concepts.


  • Gain the use of Learning Machine Learning Service from the scratch to predictive analysis.
  • Gain Hands-on experience on key Data Science Concepts
  • Solving Classic Regression and Classification Problems

Advantages of Amazon Machine Learning Training

  • Complete Understanding of Amazon Machine Learning Services
  • Leveraging the Amazon Web Services Ecosystem for accessing Extended Data Source.
  • Run Projects programmatically through Command Line and SDK

Additional Information

Day 1

1. Introduction to Machine Learning
– Introducing Amazon Machine Learning
   – Machine Learning as a Service
      – Leveraging full AWS Integration
      – Performance Comparison
– Basics of Predictive Analytics
   – Building simple predictive analytics algorithm
   – Difference between Regression and Classification
   – Expanding Regression to Classification with logistic regression
   – Extraction of features to predict outcomes
– Dive further into linear modeling for prediction
   – Dataset Validation
   – Missing from Amazon ML
   – Difference between Statistical Approach and Machine Learning Approach

2. Machine Learning Definition and Concepts
– Define Algorithm
– Define Model
– Overview of Messy Data
– Predictive Analytics Workflow
– Identifying and Correcting Poor performance

3. Introducing Amazon Machine Learning
– Opening Amazon Web Services Account
– Setting Up Account
   – Creating User
   – Defining Policies
   – Creating Login Credentials
– Overview of Standard Amazon Machine Learning
   – Data Set Configuration
   – Model
   – Model Evaluation
   – Making Batch Predictions

4. Data Preparation and Loading
– Introducing Data Sets
– Data Preparation Terminology
– Data Source Creation
– Data Statistics Examination

5. Feature Engineering with Athena
– Introduction to Athena
– Titanic Database creation
– Data Munging in SQL
– Improved Data Source Configuration

Day 2

1. Working with Model
– Data Transformation Terminology
   – Variable Management
   – Data Processing through Seven Transformation
– Model Creation
   – Editing the Suggested Recipes
   – Model Parameterization
– Evaluation Creation
– Log Analyzing Techniques

2. Predictions and Performances
– Making Batch Predictions
   – Creation of Batch Prediction job
   – Interpreting prediction outputs
– Making Real-Time Predictions
   – Manually Exploring variable Influence
   – Setting Up Real-Time Predictions

3. Working with Command Line and SDK
– Basic Overview
– Simple Project using CLI
– Boto3, the Python SDK

4. Datasource Creation from RedShift
– Selection between RDS and Redshift
– Introduction to Polynomial Regression
– Polynomial Regression in Amazon ML

5. Building Streaming Data Analysis Pipeline
– Streaming Twitter Sentiment Analysis
   – Popularity Contest on Twitter
   – Training Dataset and Model
   – Kinesis
   – Producing Tweets
   – Redshift Database
   – Adding Redshift to Kinesis Firehose
   – Lambda Preprocessing
   – Result Analysis


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