Machine Learning and Predictive Analysis Boot Camp

Live Classroom
Duration: 3 days
Live Virtual Classroom
Duration: 3 days
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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.

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


  • 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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Who should attend

The course is highly recommended for –


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


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.

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