NOC:Introduction to Data analytics


Lecture 1 - Course Overview


Lecture 2 - Course Overview (Continued...)


Lecture 3 - Descriptive Statistics - Graphical Approaches


Lecture 4 - Descriptive Statistics - Measures of Central Tendency


Lecture 5 - Descriptive Statistics - Measures of Dispersion


Lecture 6 - Random Variables and Probability Distributions


Lecture 7 - Probability Distributions (Continued...)


Lecture 8 - Probability Distributions (Continued...)


Lecture 9 - Inferential Statistics - Motivation


Lecture 10 - Inferential Statistics - Single sample tests


Lecture 11 - Two Sample tests


Lecture 12 - Type 1 and Type 2 Errors


Lecture 13 - Confidence Intervals


Lecture 14 - ANOVA and Test of Independence


Lecture 15 - Short Introduction to Regression


Lecture 16 - Introduction to Machine Learning


Lecture 17 - Supervised Learning


Lecture 18 - Unsupervised Learning


Lecture 19 - Ordinary Least Squares Regression


Lecture 20 - Simple and Multiple Regression in Excel and Matlab


Lecture 21 - Regularization/ Coefficients Shrinkage


Lecture 22 - Data Modelling and Algorithmic Modelling Approaches


Lecture 23 - Logistic Regression


Lecture 24 - Training a Logistic Regression Classifier


Lecture 25 - Classification and Regression Trees


Lecture 26 - Classification and Regression Trees (Continued...)


Lecture 27 - Bias Variance Dichotomy


Lecture 28 - Model Assessment and Selection


Lecture 29 - Support Vector Machines


Lecture 30 - Support Vector Machines (Continued...)


Lecture 31 - Support Vector Machines for Non Linearly Separable Data


Lecture 32 - Support Vector Machines and Kernel Transformations


Lecture 33 - Ensemble Methods and Random Forests


Lecture 34 - Artificial Neural Networks


Lecture 35 - Artificial Neural Networks (Continued...)


Lecture 36 - Deep Learning


Lecture 37 - Associative Rule Mining


Lecture 38 - Association Rule Mining (Continued...)


Lecture 39 - Big Data, A small introduction


Lecture 40 - Big Data, A small introduction (Continued...)


Lecture 41 - Clustering Analysis


Lecture 42 - Clustering Analysis (Continued...)


Lecture 43 - Introduction to Experimentation and Active Learning


Lecture 44 - Introduction to Experimentation and Active Learning (Continued...)


Lecture 45 - An Introduction to Online Learning - Reinforcement Learning


Lecture 46 - An Introduction to Online Learning - Reinforcement Learning (Continued...)


Lecture 47 - Summary - Insights into the Final Exam


Lecture 48 - Tutorial on weka


Lecture 49 - Tutorial on Decision Trees


Lecture 50 - Big Data - A Small Introduction (Continued...)