2021 Python for Linear Regression in Machine Learning

2021 Python for Linear Regression in Machine Learning - Linear & Non-Linear Regression, Lasso & Ridge Regression, SHAP, LIME, Yellowbrick, Feature Selection & Outliers Removal

Created by Laxmi Kant
English [Auto]

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What you'll learn

Analyse and visualize data using Linear Regression
Plot the graph of results of Linear Regression to visually analyze the results
Learn how to interpret and explain machine learning models
Do in-depth analysis of various forms of Linear and Non-Linear Regression
Use YellowBrick, SHAP, and LIME to interact with predictions of machine learning models
Do feature selection and transformations to fine tune machine learning models
Course contains result oriented algorithms and data explorations techniques

Description

This course teaches you an in-depth analysis of Linear Regression. We cover the theory and coding part together for better understanding. You will learn how to do an exhaustive analysis of machine learning models. We will show you result-oriented techniques to boost the accuracy of your machine learning models. This course teaches you everything you need to create an accurate Linear Regression model in Python.

You should have an introductory knowledge of Python before enrolling in this course otherwise please do not enroll in this course.

After completing this course you will be able to:

Interpret and Explain machine learning models which are treated as a black-box

Create an accurate Linear Regression model in python and visually analyze it

Select the best features for a business problem

Remove outliers and variable transformations for better performance

Confidently solve and explain regression problems

What is covered in this course?

This course teaches you, step by step coding for Linear Regression in Python. The Linear Regression model is one of the widely used in machine learning and it is one the simplest ones, yet there is so much depth that we are going to explore in 14+ hours of videos.

Below are the course contents of this course:

Section 1- Introduction

This section gets you to get started with the setup. Download resources files for code along.

Section 2- Python Crash Course

This section introduces you to the basics of Python programming.

Section 3- Numpy Introduction

This section is optional, you may skip it but I would recommend you to watch it if you are not comfortable with NumPy.

Section 4- Pandas Introduction

This section introduces you to the basic concepts of Pandas. It will help you later in the course to catch up on the coding.

Section 5- Matplotlib Introduction

Do not skip this section. We will be using matplotlib plots extensively in the coming sections. It builds a foundation for a strong visualization of linear regression results.

Section 6- Linear Regression Introduction

We will kick-start our Linear Regression learning. You will learn the basics of linear regression. You will see some examples so that you can understand how Linear Regression works and how to analyze the results.

Section 7- Data Preprocessing for Linear Regression

This section is the most important section. DO NOT SKIP IT. It builds the foundation of data preprocessing for linear regression and other linear machine learning models. You will be learning, what are the techniques which we can use to improve the performance of the model. You will also learn how to check if your data is satisfying the coding of Linear Model Assumptions.

Section 8- Machine Learning Models Interpretability and Explainer

This section teaches you how to open-up any machine learning models. Now you don't need to treat machine learning models as black-box, you will get to learn how to open this box and how to analyze each and every component of machine learning models.

Section 9- Linear Regression Model Optimization

This section extensively uses the knowledge of previous sections so don't skip those. You will learn various techniques to improve model performance. We will show you how to do outliers removal and feature transformations.

Section 10- Feature Selection for Linear Regression

This section teaches you some of the best techniques of feature selection. Feature selection reduces the model complexity and chances of model overfitting. Sometimes the model also gets trained faster but mostly depends on how many features are selected and the types of machine learning models.

Section 11- Ridge & Lasso Regression, ElasticNet, and Nonlinear Regression

This section covers, various types of regression techniques. You will be seeing how to achieve the best accuracy by using the above techniques.

By the end of this course, your confidence will boost in creating and analyzing the Linear Regression model in Python. You'll have a thorough understanding of how to use regression modeling to create predictive models and solve real-world business problems.

How this course will help you?

This course will give you a very solid foundation in machine learning. You will be able to use the concepts of this course in other machine learning models. If you are a business manager or an executive or a student who wants to learn and excel in machine learning, this is the perfect course for you.

What makes us qualified to teach you?

I am a Ph.D. in Machine Learning and taught tens of thousands of students over the years through my classes at IIT and KGP Talkie YouTube channel. Few of my courses are part of Udemy's top 5000 courses collection and curated for Udemy Business. I promise you will not regret it.

Who this course is for:

Beginners python programmers.
Beginners Data Science programmers.
Students of Data Science and Machine Learning.
Anyone interested in learning Linear Regression and Feature Selection
Anyone interested about the rapidly expanding world of data science!
Developers who want to work in analytics and visualization project.
Anyone who wants to explore and understand data before applying machine learning.

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