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Predictive Modeling and Machine Learning



This course uses case studies, exercises, and interactive discussion. Each machine learning method has a case study to back it up, along with step-by-step outputs that run concurrently with its multi-stage analysis. On comparable technologies like SPSS, SAS, Statistica, and Excel, all algorithms are described with consecutive screen shots.

Course Objectives:

At the end of this course the participants will be able to: 

  • Learn what machine learning really means.
  • Understand the main distinctions between machine learning and data analysis.
  • Integrate testing and sample validation into machine learning models
  • Please provide a summary of the top analytical solutions.
  • Apply precise estimation using comprehensive prediction models.

Targeted Audience:

This training would be useful for any level of professional interested in how machine learning may help their organization. These include experts from a variety of fields, such as finance, insurance, retail, government, manufacturing, healthcare, telecom, and aviation, among others.

Course Outline:

Unit1:Analysis of the Data and Simple Regression

  • the means and proportions of two groups are examined
  • Using a single chart to profile two groups
  • comparing the averages and proportions of several groupings
  • combining numerous group profiles into a single chart
  • an easy regression
  • Regression versus correlation
  • Sensitivity evaluation of numerical variables

Unit2: Logistic and Multiple Regressions

  • Machine Learning Introduction
  • Logic of Gradient Descent
  • Regression Comparison: Multiple vs. Simple
  • estimate variability analysis
  • fake variables
  • Logistic and multiple regressions: Similarities and Disparities
  • reducing complexity of models
  • Regression with steps

Unit3: Analysis of Discrimination

  • Enhanced Profiling
  • Discriminant Function for Two Groups
  • Assignment of Cases
  • Model Assessment
  • Functions of classification
  • Squared Mahalanobis Distances
  • probabilistic approach
  • Reduction of the Model
  • Common Discriminant Analysis


Unit4: Decision Trees

  • How do decision trees work?
  • Borland Trees
  • Decision Tree's Qualities
  • prudence guidelines
  • Classification Tree (CART)
  • Regression Tree, CART
  • Tree CHAID
  • Unexpected Forest Tree

Unit5: Bayesian, neural networks, nearest neighbor, and deep learning

  • Conditions for probabilities
  • Probabilistic forecasting
  • Distance between neighbors
  • K-nearest neighbors' distances from you
  • Weights in a Neural Network model
  • Hidden layers play a role
  • Advantages and disadvantages of neural networks
  • Deep Learning
  • Introduction to Big Data

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