Session 8:
Course Content : Why Normal is important? Basic Conversion to Normality, Test of Normality (AD Test)
ChatGPT Usage : Ask ChatGPT to show Visualization for AD Test and its significance
Session 9:
Course Content : Parameters, likelihood, maximum likelihood estimation (MLE), find MLE with a Simple Distribution
ChatGPT Usage : Ask ChatGPT to derive MLE and show examples
Session 10:
Course Content : Central limit theorem (CLT), continuity corrections, general confidence intervals
ChatGPT Usage : Ask ChatGPT to interpret visualizations
Session 11:
Course Content : Important of z-scores, z-intervals, prediction intervals, How to use it?
ChatGPT Usage : Ask ChatGPT to interpret visualizations
Session 12:
Course Content : Sampling distribution of the mean estimator; t-intervals, binomial intervals
ChatGPT Usage : Ask ChatGPT to interpret visualizations
Session 13:
Course Content : Hypothesis testing, z-tests under different situations
Session 14:
Course Content : Comparison of Data – Notion of ANOVA; Example – one sample t-tests
Session 15:
Course Content : Extension – Two samples T test, paired, binomial tests
Session 16:
Course Content : Extension – MANOVA, Chi-square Goodness of Fit – Where and how to use?
Session 17:
Course Content : Sampling theory details – SRSWR, SRSWOR, Stratified Sampling, Intuition behind Multistage Design
Session 18:
Course Content : Simple regression overview, MLE estimates and their distributions; Determine Regression coefficients – Interpret importance and validate Assumptions
Session 19:
Course Content : Feature Extraction and Feature Engineering related to most of the Statistical Algo
Session 20:
Course Content : Multiple regression – All details including Regression Assumptions
Session 21:
Course Content : Logistic Regression – All details including Log Likelihood Loss Function
Session 22:
Course Content : Decision Tree – Where and where to use? Example – CART, CHAID
Session 23:
Course Content : General Overview of Overfitting; Example – Pruning, Ridge, Lasso
Session 24:
Course Content : Correctness of any Regression/Classification – Intuitive and formulative
Session 25:
Course Content : Time Series data – How and where is it used? Why is it different with Regression?
Session 26:
Course Content : Time Series – Autocorrelation, Stationarity – How to derive and importance
ChatGPT Usage : ChatGPT to show examples
Session 27:
Course Content : Time Series – Autoregression, Moving Average, ARMA – Intuition
ChatGPT Usage : ChatGPT to show examples
Session 28:
Course Content : Clustering – Intuition and Importance – K-means, Hierarchical – Difference
ChatGPT Usage : ChatGPT to show Graphical representation
Session 29:
Course Content : Clustering – Density based Clustering – Why and Where is it used?
ChatGPT Usage : ChatGPT to show Graphical representation
Session 30:
Course Content : Basic Neural Network – Intuition and Loss function (Backpropagation)
ChatGPT Usage : ChatGPT to show Graphical representation
