Machine Learning and Predictive Analysis Boot Camp

Master real-world predictive modeling and basic machine learning techniques

Course Code : 1921

$1995

Overview

This hands-on machine learning courses advances the participants’ data analysis skills. The course covers real-world predictive modeling and basic machine learning techniques that will help participants excel at data analysis in their organizations. The course immerses participants in working with R to lay a solid data science foundation and trains them in techniques that enables them to leverage their data in more sophisticated and powerful ways.

Schedule Classes

Delivery Format
Starting Date
Starting Time
Duration
Location

Live Classroom
Monday, 9 September 2019
8:30 AM - 4:30 PM EST
3 Days
Indianapolis, IN

Delivery Format
Starting Date
Starting Time
Duration
Location

Live Classroom
Monday, 7 October 2019
8:30 AM - 4:30 PM EST
3 Days
Sacramento, CA

Delivery Format
Starting Date
Starting Time
Duration
Location

Live Classroom
Monday, 4 November 2019
8:30 AM - 4:30 PM EST
3 Days
Atlanta, GA

Delivery Format
Starting Date
Starting Time
Duration
Location

Live Classroom
Wednesday, 4 December 2019
8:30 AM - 4:30 PM EST
3 Days
Dallas, TX

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Course Delivery

This course is available in the following formats:

Live Classroom
Duration: 3 days

Live Virtual Classroom
Duration: 3 days

What You'll learn

  • Understanding machine learning and data science
  • Introduction to data mining
  • Working with missing values, outliers and duplicate records
  • Working with linear regression models and classification models
  • Performing cluster analysis
  • Learning the dimension reduction techniques

Outline

  • Data science as a quantitative discipline
    • How to define Data Science scopes
    • The many faces of Data Science: Data Mining, Data Analysis, Data Analytics, Machine Learning, Predictive Modeling, Statistical Learning, Mathematical Modeling. What are these all about?
    • Data Mining as a data exploration process
    • Machine Learning: supervised vs. unsupervised
    • Machine Learning vs. Predictive Analytics
    • Big Data Analytics: what is it and why it’s important
  • Overview of data mining process cycle
    • Understanding business needs and identifying new business opportunities
    • Formulating a business problem and associated requirements
    • Defining key quantitative metrics to measure success and evaluating business benefits
    • Translating business requirements into technical requirements and documentation
    • Formulating data models based on business and technical requirements
    • Identifying a set of quantitative models based on technical requirements and metrics of success
    • Running the models and evaluating results
    • Selecting the best model
    • Deploying the model
  • Data sources
  • Types of data
    • Structured vs. unstructured data
    • Static data vs. real-time data
    • Types of data attributes: numerical vs. categorical
    • Role of time factor and time trends in data analysis
  • Working with missing values
    • Main causes of missing data
    • Understanding the importance of missing information
    • Types of missing information
    • Restoring missing values
    • Imputing missing values and selecting imputation techniques
    • Understanding and evaluating potential consequences of manipulating records with missing values
  • Working with outliers
    • Defining quantitative criteria for outlier detection in 1D cases
    • Understanding role of outliers in model building
    • Deciding on outlier removal
    • Defining outlier detection metrics in multi-dimensional space
  • Working with duplicate records
    • Defining duplicates
    • Understanding sources of duplicates
    • Deciding on duplicate removal
  • Why sampling may be important for Machine Learning
  • Sampling techniques and sample bias
  • Statistical hypothesis
  • Z-score, t-score and F statistic
  • P-values
  • Implementation of hypothesis testing for model evaluation analysis
  • What is Machine Learning?
  • Supervised vs. unsupervised learning
  • Overview of supervised Machine Learning
    • Regression models
    • Classification models
  • Overview of unsupervised Machine Learning
    • Clustering methods
    • Principal component analysis and dimension reduction
    • Association rules
  • Overview of major steps in building and testing quantitative models
    • Criteria for model selection
    • How to prepare a training set
    • Criteria for selecting model attributes/predictors
    • Working with collinear variables
    • Addressing imbalance problem
    • Dealing with over-fitting; bias-variance tradeoff
    • Validation and cross-validation
  • Univariate regression vs. multiple regression
  • Mathematical foundation of linear regression overview: least square method vs. maximum likelihood method
  • Model assumptions
  • Working with continuous attributes
  • Dealing with collinear variable
  • Model subset selection:
    • Forward stepwise selection
    • Backward selection
    • Shrinkage methods: ridge regression and Lasso
    • Dimension reduction
    • Information criteria
  • Automating model selection procedure
  • Model parameter evaluation, R squared vs. adjusted R squared
  • Validating the model
  • Working with categorical variables
  • Considering input variable interactions
  • Dealing with imbalanced training sets
  • Understanding confusion matrix
  • Evaluating binary classifiers using ROC / AUC
  • Overview of cluster analysis mathematical foundation
  • K-means clustering method
    • Algorithm overview
    • Convergence criteria
    • How to determine the number of clusters
  • What is dimension reduction?
  • The practical goals of dimension reduction implementation
  • Principal component analysis vs. singular value decomposition
  • How many components to choose
  • What was not covered in the class
  • Big Data Analytics – the future of machine learning: main tools and concepts
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Prerequisites

Participants need to have intermediate-level data analysis skills and basic knowledge of descriptive statistics. Having experience working with R would be beneficial.

Technical requirements: Installed R and some R packages. Installation of RStudio is helpful, but not required.

Who Should Attend

The course is highly recommended for –

  • Data analysts
  • Machine learning professionals
  • Business analysts
  • Data mining specialists

Interested in this course? Let’s connect!

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