Introduction to PM Methodologies & Detailed Understanding
  • Introduction
  • Underatanding KDD, SEMMA & CRISP-DM
  • Comparison between KDD, SEMMA, CRISP-DM
Phase-1 | Business Understanding
  • Business Problem -A
  • Business Problem -B
  • Business Problem -C
  • Business Problem -D & E
Phase-2 | Data Understanding
  • Data Understanding Introduction
  • A. Data Collection
  • A-1: Differentiation of Various Data Collection
  • A-2 : Data Collection Using Survey
  • A-3 : Data Collection Using Design Of Experiments
  • A-4 : Exploratory Data Analysis
  • A-5 : Data Quality Analysis
Phase-3 | Data Preparation
  • Data Integration & Wrangling
  • Attribute Generation and Selection
Phase-4 | Modelling
  • Modelling Introduction
  • Model Building Supervised and Unsupervised Learning
  • Model Building Re-inforcement Learning
  • Model Building Semi-Supervised and Other Learnings
  • Model Building Learning Examples
  • Selecting Model Technique-When Y is known ?
  • Selecting Model Technique-When Y is unknown ?
  • Selecting Model Technique- For Forecasting and Time-series data
  • Data Separation Technique
  • Model Evaluation Technique - Over fitting Vs Under fitting Datasets
  • Model Evaluation Technique-Balanced and Imbalanced Datasets
  • Errors Accuracy & Measures | When Y is continuous ?
  • Errors Accuracy & Measures | When Y is discrete ?
  • Conti....| When Y is discrete ?
  • Model Evaluation Technique-ROC Curve and Model Assessment
Phase-5 | Evaluation
  • Evaluation
Phase-6 | Deployment
  • Deployment