Fundamentals Of Machine Learning For Predictive Data Analytics Using Sas

/Fundamentals Of Machine Learning For Predictive Data Analytics Using Sas

november, 2017

wed15novAll Dayfri17Fundamentals Of Machine Learning For Predictive Data Analytics Using Sas(All Day) New Horizons Ireland, Strand House, 22-24 Strand Street Great, Dublin 1

Event Details

Duration: 3 Days

Overview

Introduction

Machine learning and predictive data analytics are fast becoming the best way for sophisticated organisations to use data to gain a competitive edge. Predictive analytics applications use machine learning to build predictive models for applications including price prediction, risk assessment, and predicting customer behaviour. Based on the trainers’ book, “Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples and Case Studies” (www.machinelearningbook.com) this course presents a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.

All delegates receive a free copy of the book “Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples and Case Studies”

Presented by Aoife D’Arcy  

Aoife has spent the last 15 years developing analytical models and processes for major national and international companies in banking, finance, insurance, gaming and manufacturing. Aoife has developed particular expertise in customer insight analytics, fraud analytics, and risk analytics.

Aoife founded The Analytics Store in 2009 to peruse her passionate belief in the importance of developing in-house analytics talent in organisations. Aoife works with organisations to help them build world class analytics teams and processes through a unique mix of training, consultancy and mentoring.

Aoife is a co-author of the textbook “Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples & Case Studies” published in 2015 with MIT Press.

Learn how to:

This course has been designed to guide delegates through the most important topics in machine learning, and how they should be applied to build real-world relevant predictive analytics models using SAS. After completing the course delegates will:

  • Understand how to frame a business problem as a predictive analytics problem
  • Understand the fundamental theories of machine learning, and the most important machine learning approaches
  • Be familiar with a wide range of applications of predictive data analytics and machine learning, including the limitations of machine learning
  • Be comfortable analysing the quality of datasets for machine learning models
  • Have an awareness of how SAS can be used to build predictive analytics models using machine learning techniques
  • Be fully prepared to understand newly emerging advanced topics in machine learning

Who Should attend

This course is aimed at people in a technical role who want to fully understand and use machine learning based predictive analytics techniques. This course is for you if:

  • you need to learn about the most important topics in machine learning, and how they should be applied to build real-world relevant predictive analytics models
  • you need to learn how to apply detailed examples and real-world case studies using SAS technology.
  • you like learning from experts, in an instructor led class room environment
  • you are interested in understanding sophisticated machine learning theories, and how they are applied to enable best business practice

Prerequisites

To attend this course delegates should be familiar with basic statistical concepts (such as mean, standard deviation, and correlation) and comfortable with data manipulation tools such as spreadsheets and databases. Some knowledge of SAS would be useful but not essential.

Course Outline

The course will cover the following key topics through a series Lectures, demos and interactive workshop sessions.

Day 1 Topics:

Introduction

  • The analytics process:
    • Data – Insight – Decision
    • Crisp-DM
  • What is predictive data analytics and what is it used for?
  • What is machine learning?
  • Training machine learning models – inductive bias, generalisation, overfitting and underfitting

Data Preparation for Machine Learning

  • Review of data analysis and data visualisation
  • Feature engineering
  • Assessing data quality
  • Standard data manipulation techniques and their pros and cons,
    • Binning
    • Imputations
    • Standardization

Demos & Workshop: Applying these techniques in SAS

Day 2 Topics

Information-based Learning

  • Fundamentals
    • Decision Trees
    • Shannon’s Entropy Model
    • Information Gain
  • Standard Approach: The ID3 Algorithm
  • A Worked Example: Predicting Vegetation Distributions
  • Alternative Feature Selection Metrics
  • Handling Continuous Descriptive Features
  • Noisy Data, Overfitting and Tree Pruning

Model Evaluation

  • Standard Approach: Measuring Misclassification Rate on a Hold-out Test Set
  • Designing Evaluation Experiments
    • Hold-out Sampling
    • k-Fold Cross Validation
  • Performance Measures: Categorical Targets
    • Average Class Accuracy
    • Performance Measures: Multinomial Targets
  • Performance Measures: Prediction Scores
    • Receiver Operating Characteristic Curves
    • Gini and KS Statistics
    • Measuring Gain and Lift
  • Performance Measures: Continuous Targets

Demos & Workshop: Applying these techniques in SAS

Day 3 Topics

Other Machine Learning Method Explored

 

  • Error Based Learning

 

      • Multiple Linear Regression
      • Logistic Regression
      • Neural Networks

 

  • Ensemble Model

 

      • Bagging
      • Boosting
      • Random Forests

 

  • Non-linear Models & Support Vector Machines

 

Demos & Workshop: Applying these techniques in SAS

Time

november 15 (Wednesday) - 17 (Friday)

Location

New Horizons Ireland

Strand House, 22-24 Strand Street Great, Dublin 1

Event Organised by

The Analytics Storeinfo@theanalyticsstore.com

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