[Udemy] Linear Regression and Logistic Regression in Python
What you’ll learn
Discover ways to clear up actual life drawback utilizing the Linear and Logistic Regression method
Preliminary evaluation of information utilizing Univariate and Bivariate evaluation earlier than operating regression evaluation
Perceive easy methods to interpret the results of Linear and Logistic Regression mannequin and translate them into actionable perception
Indepth information of information assortment and knowledge preprocessing for Linear and Logistic Regression drawback
This course begins from fundamentals and you don’t even want coding background to construct these fashions in Python
College students might want to set up Python and Anaconda software program however we now have a separate lecture that can assist you set up the identical
You are on the lookout for an entire Linear Regression and Logistic Regression course that teaches you the whole lot it’s worthwhile to create a Linear or Logistic Regression mannequin in Python, proper?
You’ve got discovered the suitable Linear Regression course!
After finishing this course it is possible for you to to:
- Establish the enterprise drawback which could be solved utilizing linear and logistic regression strategy of Machine Studying.
- Create a linear regression and logistic regression mannequin in Python and analyze its consequence.
- Confidently mannequin and clear up regression and classification issues
A Verifiable Certificates of Completion is introduced to all college students who undertake this Machine studying fundamentals course.
What is roofed in this course?
This course teaches you all of the steps of making a Linear Regression mannequin, which is the most well-liked Machine Studying mannequin, to unravel enterprise issues.
Beneath are the course contents of this course on Linear Regression:
- Part 1 – Fundamentals of StatisticsThis part is split into 5 totally different lectures ranging from kinds of knowledge then kinds of statisticsthen graphical representations to explain the information and then a lecture on measures of middle like implymedian and mode and lastly measures of dispersion like vary and normal deviation
- Part 2 – Python fundamentalThis part will get you began with Python.This part will assist you to arrange the python and Jupyter surroundings in your system and it will educateyou easy methods to carry out some fundamental operations in Python. We’ll perceive the significance of various libraries corresponding to Numpy, Pandas & Seaborn.
- Part 3 – Introduction to Machine StudyingOn this part we’ll study – What does Machine Studying imply. What are the meanings or totally different phrases related to machine studying? You will note some examples so that you just perceive what machine studying really is. It additionally accommodates steps concerned in constructing a machine studying mannequin, not simply linear fashions, any machine studying mannequin.
- Part 4 – Information PreprocessingOn this part you’ll study what actions it’s worthwhile to take a step-by-step to get the information and thenput together it for the evaluation these steps are crucial.We begin with understanding the significance of enterprise information then we’ll see easy methods to do knowledge exploration. We discover ways to do uni-variate evaluation and bi-variate evaluation then we cowl subjects like outlier remedy, lacking worth imputation, variable transformation and correlation.
- Part 5 – Regression MannequinThis part begins with easy linear regression and then covers a number of linear regression.We now have coated the fundamental concept behind every idea with out getting too mathematical about it so that you justperceive the place the idea is coming from and how it’s important. However even in the event you do not perceiveit, will probably be okay so long as you discover ways to run and interpret the consequence as taught in the sensible lectures.We additionally take a look at easy methods to quantify fashions accuracy, what’s the which means of F statistic, how categorical variables in the impartial variables dataset are interpreted in the outcomes, what are different variations to the peculiar least squared methodology and how can we lastly interpret the consequence to seek out out the reply to a enterprise drawback.
By the top of this course, your confidence in making a regression mannequin in Python will soar. You may have an intensive understanding of easy methods to use regression modelling to create predictive fashions and clear up enterprise issues.
Who this course is for:
- Individuals pursuing a profession in knowledge science
- Working Professionals starting their Information journey
- Statisticians needing extra sensible expertise