Machine Learning & Predictive Analytics Boot Camp

3 Day Classroom  •  3 Day Live Online
3 Day Training at your location.
Adjustable to meet your needs.
Individual:
$2495.00
Group Rate:
$2295.00
GSA Discount:
$1821.35
When training eight or more people, onsite team training offers a more affordable and convenient option.
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Overview

The stores of data relevant to our organizations, customers, operations, and goals have never accumulated at a faster pace or to a larger volume. Likewise, the need for intelligent data analysis has never been greater. Vast reserves of value hidden within huge and sophisticated data sets. It can be a challenge to find that value – but if we can tease out the insights and answers lurking within our information, they can be translated into a host of opportunities and advantages. With the right skills, only your own creativity limits how you can leverage your stores of data for better decisions, analytics, and prediction.

Fortunately, today's data science methods are more practical and accessible than ever. The open-source R environment provides a straightforward yet incredibly powerful toolbox for performing useful predictive modeling and deep analysis. This hands-on machine learning course advances your data analysis skills into the realm of real-world data science. If you have a working familiarity with R, our three-day class equips you to go back to work with real-world predictive modeling and basic machine learning techniques. Led by expert data scientists, you will work in R to lay your data science foundation and learn techniques that allow you to leverage your data in sophisticated, powerful new ways.

Upcoming Dates and Locations
Guaranteed To Run
Dec 3, 2018 – Dec 5, 2018    8:30am – 4:30pm Chicago, Illinois

Microtek Chicago
230 W. Monroe
Suite 900
Chicago, IL 60606
United States

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Dec 3, 2018 – Dec 5, 2018    10:30am – 5:30pm Live Online
10:30am – 5:30pm
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Jan 7, 2019 – Jan 9, 2019    8:30am – 4:30pm Live Online
8:30am – 4:30pm
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Jan 7, 2019 – Jan 9, 2019    8:30am – 4:30pm Reston, Virginia

Microtek Reston
12950 Worldgate Drive
Monument II Bldg 4th Flr
Herndon, VA 20170
United States

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Feb 4, 2019 – Feb 6, 2019    8:30am – 4:30pm Live Online
8:30am – 4:30pm
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Feb 4, 2019 – Feb 6, 2019    8:30am – 4:30pm Seattle, Washington

Allied Business Systems - Computer Classrooms
10604 NE 38th Place, Suite 118
Yarrow Bay Office Park-1 North
Kirkland, WA 98033
United States

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Mar 4, 2019 – Mar 6, 2019    8:30am – 4:30pm Live Online
8:30am – 4:30pm
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Mar 4, 2019 – Mar 6, 2019    8:30am – 4:30pm Charlotte, North Carolina

Doubletree Hotel Charlotte Airport
2600 Yorkmont Road
Charlotte, NC 28208
United States

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Apr 8, 2019 – Apr 10, 2019    8:30am – 4:30pm San Diego, California

San Diego Training and Conference Center
450 B Street
Suite 650
San Diego, CA 92101
United States

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Apr 8, 2019 – Apr 10, 2019    11:30am – 7:30pm Live Online
11:30am – 7:30pm
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May 6, 2019 – May 8, 2019    8:30am – 4:30pm Live Online
8:30am – 4:30pm
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May 6, 2019 – May 8, 2019    8:30am – 4:30pm New York, New York

NYC Seminar and Conference Center
71 West 23rd
Suite 515-Lower Level
New York, NY 10010
United States

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Jun 3, 2019 – Jun 5, 2019    8:30am – 4:30pm Portland, Oregon

Kinetic Technology Solutions
15495 SW Sequoia Parkway
Suite 100
Portland, OR 97224
United States

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Jun 3, 2019 – Jun 5, 2019    11:30am – 7:30pm Live Online
11:30am – 7:30pm
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Jul 8, 2019 – Jul 10, 2019    8:30am – 4:30pm Minneapolis, Minnesota

Embassy Suites Airport
7901 34th Avenue South
Bloomington, MN 55425
United States

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Jul 8, 2019 – Jul 10, 2019    9:30am – 5:30pm Live Online
9:30am – 5:30pm
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Aug 5, 2019 – Aug 7, 2019    8:30am – 4:30pm Austin, Texas

Embassy Suites Austin Central
5901 North IH-35
Frontage Rd
Austin, TX 78723
United States

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Aug 5, 2019 – Aug 7, 2019    9:30am – 5:30pm Live Online
9:30am – 5:30pm
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Sep 9, 2019 – Sep 11, 2019    8:30am – 4:30pm Indianapolis, Indiana

Courtyard Indianapolis Castleton
8670 Allisonville Road
Indianapolis, IN 46250
United States

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Sep 9, 2019 – Sep 11, 2019    9:30am – 5:30pm Live Online
9:30am – 5:30pm
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Course Outline

Part 1: Overview of Data Science

  1. 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
  2. Overview of a 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

Part 2: The Data Foundation

  1. Data sources
  2. 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
  3. 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
  4. 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
  5. Working with duplicate records
    • Defining duplicates
    • Understanding sources of duplicates
    • Deciding on duplicate removal

Part 3: Sampling and Hypothesis Testing

  1. Why sampling may be important for Machine Learning
  2. Sampling techniques and sample bias
  3. Statistical hypothesis
  4. Z-score, t-score and F statistic
  5. P-values
  6. Implementation of hypothesis testing for model evaluation analysis

Part 4: Machine Learning Fundamentals

  1. What is Machine Learning?
  2. Supervised vs. unsupervised learning
  3. Overview of supervised Machine Learning
    • Regression models
    • Classification models
  4. Overview of unsupervised Machine Learning
    • Clustering methods
    • Principal component analysis and dimension reduction
    • Association rules
  5. 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

Part 5: Building a Linear Regression Model with R.

  1. Univariate regression vs. multiple regression
  2. Mathematical foundation of linear regression overview: least square method vs. maximum likelihood method
  3. Model assumptions
  4. Working with continuous attributes
  5. Dealing with collinear variable
  6. Model subset selection:
    • Forward stepwise selection
    • Backward selection
    • Shrinkage methods: ridge regression and Lasso
    • Dimension reduction
    • Information criteria
  7. Automating model selection procedure
  8. Model parameter evaluation, R squared vs. adjusted R squared
  9. Validating the model
  10. Working with categorical variables
  11. Considering input variable interactions

Part 6: Example of building a Classification Model with R

  1. Dealing with imbalanced training sets
  2. Understanding confusion matrix
  3. Evaluating binary classifiers using ROC / AUC

Part 7: Example of Cluster Analysis with R

  1. Overview of cluster analysis mathematical foundation
  2. K-means clustering method
    • Algorithm overview
    • Convergence criteria
    • How to determine the number of clusters

Part 8: Dimension Reduction techniques with R

  1. What is dimension reduction?
  2. The practical goals of dimension reduction implementation
  3. Principal component analysis vs. singular value decomposition
  4. How many components to choose

Part 9: Class Conclusion

  1. What was not covered in the class
  2. Big Data Analytics – the future of machine learning: main tools and concepts
Who should attend

Intermediate level data analysts interested in expanding their data mining processes. We emphasize Data Foundation and Machine Learning concepts. All exercises are performed in R.

 

Pre-Requisites

This machine learning course is for individuals with intermediate data analysis skills and basic knowledge of descriptive statistics. Any experience with R is also beneficial. 

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

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