Data Science for Engineers
Data Science for Engineers. Instructors: Prof. Raghunathan Rengaswamy and Prof. Shankar Narasimhan, Department of Computer Science and Engineering, IIT Madras. This course will provide an introduction to data analysis for beginners; a framework to understand different data analysis algorithms; a structured approach to convert high level data analysis problem statements into a well-defined workflow for solution; an introduction to R as a programming language with an emphasis on commands required for this course material; a brief description of concepts in linear algebra and statistics that the participants should focus on; conceptual description of selected machine learning algorithms; practical demonstration of the algorithm through a case study with R.
(from nptel.ac.in )

Lecture 38 - Multiple Linear Regression
VIDEO

Go to the Course Home or watch other lectures:

Lecture 01 - Course Philosophy and Expectation
Lecture 02 - Introduction to R (Programming Language)
Lecture 03 - Introduction to R (cont.)
Lecture 04 - Variables and Datatypes in R
Lecture 05 - Data Frames
Lecture 06 - Recasting and Joining of Dataframes
Lecture 07 - Arithmetic, Logical and Matrix Operations in R
Lecture 08 - Advanced Programming in R: Functions
Lecture 09 - Advanced Programming in R: Functions (cont.)
Lecture 10 - Control Structures
Lecture 11 - Data Visualization in R Basic Graphics
Lecture 12 - Linear Algebra for Data Science
Lecture 13 - Solving Linear Equations
Lecture 14 - Solving Linear Equations (cont.)
Lecture 15 - Linear Algebra - Distance, Hyperplanes and Halfspaces, Eigenvalues, Eigenvectors
Lecture 16 - Linear Algebra - Distance, Hyperplanes and Halfspaces, Eigenvalues, Eigenvectors (cont.)
Lecture 17 - Linear Algebra - Distance, Hyperplanes and Halfspaces, Eigenvalues, Eigenvectors (cont.)
Lecture 18 - Linear Algebra - Distance, Hyperplanes and Halfspaces, Eigenvalues, Eigenvectors (cont.)
Lecture 19 - Statistical Modeling
Lecture 20 - Random Variables and Probability Mass/Density Functions
Lecture 21 - Sample Statistics
Lecture 22 - Hypothesis Testing
Lecture 23 - Optimization for Data Science
Lecture 24 - Unconstrained Multivariate Optimization
Lecture 25 - Unconstrained Multivariate Optimization (cont.)
Lecture 26 - Numerical Example: Gradient (Steepest) Descent (OR) Learning Rule
Lecture 27 - Multivariate Optimization with Equality Constraints
Lecture 28 - Multivariate Optimization with Inequality Constraints
Lecture 29 - Introduction to Data Science
Lecture 30 - Solving Data Analysis Problems - A Guided Thought Process
Lecture 31 - Module: Predictive Modeling
Lecture 32 - Linear Regression
Lecture 33 - Model Assessment
Lecture 34 - Diagnostics to Improve Linear Model Fit
Lecture 35 - Simple Linear Regression Model Building
Lecture 36 - Simple Linear Regression Model Assessment
Lecture 37 - Simple Linear Regression Model Assessment (cont.)
Lecture 38 - Multiple Linear Regression
Lecture 39 - Cross Validation
Lecture 40 - Multiple Linear Regression Modeling Building and Section
Lecture 41 - Classification
Lecture 42 - Logistic Regression
Lecture 43 - Logistic Regression (cont.)
Lecture 44 - Performance Measures
Lecture 45 - Logistic Regression Implementation in R
Lecture 46 - K-Nearest Neighbors (K-NN)
Lecture 47 - K-Nearest Neighbors Implementation in R
Lecture 48 - K-Means Clustering
Lecture 49 - K-Means Implementation in R
Lecture 50 - Summary