Machine Learning and Predictive Analytics

Who Should Attend?


Duration: 4 Days

This course prepares you to take the knowledge gained and apply it to their own respective data mining problems, solving them quickly and easily. First part in this course will introduce the overview of a basic analytic process while second part brings in more complicated cases extended from first part. The lessons learnt will be applicable to areas such as customer analytics, targeted marketing, social media analytics, fraud detection, predictive maintenance, resource management,etc.

  • Perform common data preparations
  • Build sophisticated predictive models
  • Evaluate model quality with respect to different criteria
  • Deploy analytical predictive models
  • Apply more sophisticated analytical approaches
Part A: Basic Machine Learning & Predictive Analytics
  1. Overview
    • Business Scenario
    • Analytics Taxonomy & Hierarchy
    • CRISP-DM
    • Data Analytics in the Enterprise
  2. EDA: Exploratory Data Analysis
    • Loading Data
    • Quick Summary Statistics
    • Visualizing Data & Basic Chart
  3. Data Preparation
    • Basic ETL (Extract, Transform, and Load)
    • Data Types and Transformations
    • Handling Missing Values
    • Handling Attribute Roles
    • Normalization and Standardization
    • Filtering Examples and Attributes
  4. Predictive Model’s Algorithms
    • K-Nearest Neighbors
    • Naive Bayes
    • Linear Regression
    • Decision Tree
  5. Model Construction and Evaluation
    • Machine Learning Theory: Bias, Variance, Overfitting and Underfitting
    • Split and Cross Validation
    • Applying Models
    • Optimization and Parameter Tuning
    • Evaluation Methods & Performance Criteria
  6. Additional Workshops
    • Outlier Detection
    • Random Forests
    • Ensemble Modeling
Part B: Advanced Machine Learning & Predictive Analytics
  1. Overview
    • Business Case
    • Intro Course Review
    • Loading New Data
  2. EDA: Exploratory Data Analysis
    • Multiple Data Sources
    • Joins & Set Theory
    • Understanding New Attributes
  3. Data Preparation ▪ Advanced ETL
    • Aggregation & Multi-Level Aggregation
    • Pivot & De-Pivot
    • Regular Expressions
    • Changing Value Types
    • Feature Generation and Engineering
    • Loops
    • Macros
  4. Predictive Model’s Algorithms
    • Support Vector Machines
    • Neural Networks
    • Logistic Regression
  5. Model Construction and Evaluation
    • Advanced Performance Criteria
    • ROC Plots
    • Comparison between Models
    • Sampling
    • Weighting
    • Feature Selection
    • Preprocessing Models & Validation
    • Optimization & Logging Results
  6. Additional Workshops
    • Principal Components Analysis
    • Logistic Regression
    • Performance (Cost) Model Optimization

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