Unit 1 : Machine Learning Model Building

• Machine Learning Foundation
• Hypothesis Testing & P values
• Statistical Tests in R
• Sampling Methods & CI
• Feature Engineering Methods
• Dimensionality Reduction
• Overfitting & Under-fitting
• Bias & Variance Tradeoff
• Dataset Sampling & Partitions
• Computational Limitations
• Model Building Steps in R
• Model Building Steps in Python

Unit 2: Predictive Analytics

• Data Exploration Case Study
• Linear Regression
• Linear Model Fits & Evaluation Metrics
• Non Linear Regression
• L1 & L2 Regularization
• Classification Methods Overview
• Accuracy,Confusion Matrix & ROC curve
• Logistic Regression
• Logistic Model Fits & Evaluation Metrics
• Decision Trees & Rule Learners
• Trees Model Fits & Evaluation Metrics
• Naïve Bayes Classifier Model
• KNN Classifier Model
• Model Selection Parameters
• Case Study 1 : Classification
• Case Study 2 : Trees
• Case Study 3 : Regression

Unit 3: ML Advanced : Black Box & Ensemble

• Support Vector Mahcines
• Neural Networks
• ML Ensemble Methods
• Bagging & Random Forest
• Gradient Boosting Methods (GBM)
• Case Study 4 : SVM
• Case Study 5 : Random Forest
• Case Study 6 : GBM

Unit 4: Unsuperwised Machine Learning

• K-means Clustering
• Hierarchical Clustering
• Principle Component Analysis(PCA)
• Feature Hashing

Unit 5: Machine Learning Tools

• Hadoop Architecture for big data
• R & h2o
• Python SciKit Learn
• Spark Architecture
• Spark ML and PySpark
• Spark ML and Sparklyr
• Microsoft AzureML
• Working with Azure Cloud
• Project selection on real life case study
• Best Practices & Project progress discussion

Unit 6: Job Interview Essentials

• CV Preparation
• Effective selling of CV in job sites
• Soft Skills for Data Scientist
• Project: Final presentation
• Executive presence for Interview