Learn · Python for Data Science
Lecture 30 of 40Week 4
Linear Regression
Prof. Ragunathan Rengasamy · IIT Madras
Credits and licence
- From
- Python for Data Science, IIT Madras (at NPTEL)
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- Tweens profiles and older. How courses are placed
All the lectures
- Introduction to Python for Data Science
- Introduction to Python
- Introduction to Spyder - Part 1
- Introduction to Spyder - Part 2
- Variables and Datatypes
- Operators
- Jupyter setup
- Sequence_data_part_1
- Sequence_data_part_2
- Sequence_data_part_3
- Sequence_data_part_4
- Numpy
- Reading data
- Pandas Dataframes I
- Pandas Dataframes II
- Pandas Dataframes III
- Control structures & Functions
- Exploratory data analysis
- Data Visualization-Part I
- Data Visualization-Part II
- Dealing with missing data
- Introduction to Classification Case Study
- Case Study on Classification Part I
- Case Study on Classification Part II
- Introduction to Regression Case Study
- Case Study on Regression Part I
- Case Study on Regression Part II
- Case Study on Regression Part III
- Module : Predictive Modelling
- Linear Regression
- Model Assessment
- Diagnostics to Improve Linear Model Fit
- Cross Validation
- Classification
- Logistic Regression
- K - Nearest Neighbors (kNN)
- K - means Clustering
- Logistic Regression ( Continued )
- Decision Trees
- Multiple Linear Regression