Categories

2022 Python for Linear Regression in Machine Learning

Description

This course teaches you an in-depth analysis of Linear Regression. We cowl the hypothesis and coding half collectively for larger understanding. You’ll be taught to do an exhaustive analysis of machine finding out fashions. We’ll current you result-oriented strategies to boost the accuracy of your machine finding out fashions. This course teaches you all of the items it’s advisable create an right Linear Regression model in Python.

You should have an introductory information of Python sooner than enrolling on this course in another case please don’t enroll on this course.

After ending this course it’s doable so that you can to:

  • Interpret and Make clear machine finding out fashions which are dealt with as a black-box

  • Create an right Linear Regression model in python and visually analyze it

  • Select the simplest choices for a enterprise downside

  • Take away outliers and variable transformations for larger effectivity

  • Confidently resolve and make clear regression points

What’s roofed on this course?

This course teaches you, step-by-step coding for Linear Regression in Python. The Linear Regression model is no doubt one of many extensively utilized in machine finding out and it’s one the one ones, however there could also be quite a bit depth that we’re going to find in 14+ hours of flicks.

Beneath are the course contents of this course:

  • Half 1- Introduction

    This half will get you to get started with the setup. Get hold of sources info for code alongside.

  • Half 2- Python Crash Course

    This half introduces you to the basics of Python programming.

  • Half 3- Numpy Introduction

    This half is optionally accessible, you possibly can skip it nevertheless I’d counsel you to take a look at it in case you aren’t comfortable with NumPy.

  • Half 4- Pandas Introduction

    This half introduces you to the elemental concepts of Pandas. It could permit you to later within the course to compensate for the coding.

  • Half 5- Matplotlib Introduction

    Don’t skip this half. We’ll probably be using matplotlib plots extensively within the approaching sections. It builds a foundation for a strong visualization of linear regression outcomes.

  • Half 6- Linear Regression Introduction

    We’ll kick-start our Linear Regression finding out. You’ll examine the basics of linear regression. You’ll be aware some examples in order to understand how Linear Regression works and recommendations on how one can analyze the outcomes.

  • Half 7- Data Preprocessing for Linear Regression

    This half is a really highly effective half. DO NOT SKIP IT. It builds the inspiration of information preprocessing for linear regression and totally different linear machine finding out fashions. Chances are you’ll be finding out, what are the strategies which we’re in a position to make use of to reinforce the effectivity of the model. Moreover, you’ll be taught to confirm in case your information is satisfying the coding of Linear Model Assumptions.

  • Half 8- Machine Studying Fashions Interpretability and Explainer

    This half teaches you recommendations on how one can open-up any machine finding out fashions. Now you needn’t take care of machine finding out fashions as black-box, you’ll get to be taught to open this area and recommendations on how one can analyze each a part of machine finding out fashions.

  • Half 9- Linear Regression Model Optimization

    This half extensively makes use of the information of earlier sections so don’t skip these. You’ll examine quite a few strategies to reinforce model effectivity. We’ll current you recommendations on how one can do outliers eradicating and have transformations.

  • Half 10- Perform Selection for Linear Regression

    This half teaches you various the best strategies of attribute alternative. Perform alternative reduces the model complexity and prospects of model overfitting. Typically the model moreover will get expert faster nevertheless largely relies upon what variety of choices are chosen and the kinds of machine finding out fashions.

  • Half 11- Ridge & Lasso Regression, ElasticNet, and Nonlinear Regression

    This half covers, quite a few types of regression strategies. Chances are you’ll be seeing recommendations on how one can receive the simplest accuracy by means of the usage of the above strategies.

By the highest of this course, your confidence will improve in creating and analyzing the Linear Regression model in Python. You’ll have an intensive understanding of recommendations on how one can use regression modeling to create predictive fashions and resolve real-world enterprise points.

How this course will permit you to?

This course provides you a very robust foundation in machine finding out. It’s doable so that you can to utilize the concepts of this course in several machine finding out fashions. Should you’re a enterprise supervisor or an authorities or a pupil who wishes to review and excel in machine finding out, that’s the correct course for you.

What makes us licensed to indicate you?

I’m a Ph.D. in Machine Studying and taught tens of 1000’s of students by means of the years through my programs at IIT and KGP Talkie YouTube channel. Few of my applications are part of Udemy’s excessive 5000 applications assortment and curated for Udemy Enterprise. I promise you’ll not regret it.


1,034

0$
19.99$


Get Coupon

Join us on telegram for Course Updates
Views:
3
Article Categories:
Udemy Free Courses

Leave a Reply

Your email address will not be published. Required fields are marked *