This is what you will be learning in the course:

• Introduction to Python
• Inferential Statistics
  • Probability
  • Statistics (Central Tendency, Spread of Data, Data Distributions, ANOVA, CHI SQUARE Test)
  • Univariate Analysis
  • Bivariate Analysis
  • Hypothesis Testing (t-test, z-test, p-value)
  • Quiz
• Introduction to Machine Learning
  • Scikit Learn Package
  • Supervised and Unsupervised concepts
• Linear Regression
  • Understanding the concept Linear Regression
  • Maths behind Linear Regression
  • Lasso Regression and Ridge Regression
  • Case Study 1 (Theory + Practicals + Reporting)
  • Case Study 2 (Theory + Practicals + Reporting)
  • Quiz
• Logistic Regression
  • Understanding the concept of Logistic Regression
  • Maths behind Logistic Regression
  • AUC and ROC Curves, Confusion Matrix (Accuracy, Sensitivity and Specificity, Precision and Recall)
  • Cutoff Methods in Logistic Regression (Min Distance method, KS Method, Lift Method, F1 Beta Method)
  • Case Study 1 (Theory + Practicals + Reporting)
  • Case Study 2 (Theory + Practicals + Reporting)
  • Quiz
• Capstone Project 1 for Certification
• Clustering
  • Hierarchical Clustering (Maths + Case Study + Practicals + Reporting)
  • K-Means Clustering (Maths + Case Study + Practicals + Reporting)
  • DB Scan (Maths + Case Study + Practicals + Reporting)
  • Quiz
• Decision Trees
  • Maths behind decision trees – Information Gain, Remainder,  Loss
  • C 4.5 (Maths + Case Study + Practicals + Reporting)
  • Random Forest (Maths + Case Study + Practicals + Reporting)
  • Quiz
• Time Series
  • Understanding Time Series and its concepts
  • Exponential Smoothening 1 (Maths + Case Study + Practicals + Reporting)
  • Exponential Smoothening 2 (Maths + Case Study + Practicals + Reporting)
  • Exponential Smoothening 3 (Maths + Case Study + Practicals + Reporting)
  • Test for Stationarity
  • ARIMA (Maths + Case Study + Practicals + Reporting)
  • Quiz
• Introduction to Boosting, Bagging and Cross Validation
• Dimensionality Reduction
  • PCA (Principal Component Analysis)
  • LDA (Linear Discriminant Analysis)
• Ensemble Techniques
• Master Quiz for Certification
• Capstone Project 2 for Certification