Introduction About Trainer
- Introduction About Trainer & Course
Introduction to PM Methodologies & Detailed Understanding
- Introduction
Phase-1 | Business Understanding
- Underatanding KDD, SEMMA & CRISP-DM
- Business Problem -A
- Comparison between KDD, SEMMA, CRISP-DM
- 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
