2021 Python for Linear Regression in Machine Learning (Udemy)

This course will teach you how to analyze Linear Regression in depth. For a better understanding, let's go over the theory and coding together. You will learn how to conduct a thorough analysis of machine learning models. They will demonstrate outcome-oriented techniques for improving the accuracy of your machine learning models. This course will teach you how to build an accurate Linear Regression model in Python.

Developing a solution that checks all the boxes necessitates several stages and the integration of numerous libraries. This curriculum ensures that you have a thorough understanding of all aspects of computational modeling. You will also learn how to share your insights with the team in order to make critical corporate decisions. 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?

• Section 1- Introduction
• Section 2- Python Crash Course
• Section 3- Numpy Introduction
• Section 4- Pandas IntroductionSection 5- Matplotlib Introduction
• Section 6- Linear Regression Introduction
• Section 7- Data Preprocessing for Linear Regression
• Section 8- Machine Learning Models Interpretability and Explainer
• Section 9- Linear Regression Model Optimization
• Section 10- Feature Selection for Linear Regression
• Section 11- Ridge & Lasso Regression, ElasticNet, and Nonlinear Regression.

Highlights

• Installation guidelines for Python and Anaconda are available
• Brush up the fundamental syntax in the crash course section
• Explore the functions of libraries such as NumPy, Pandas, and Matplotlib
• Cleanse, preprocess and transform data and variables
• Interpret, and plot your observations with professional accuracy
• Interact with predictions using SHAP, YellowBrick, and LIME
• Learn about optimization techniques and feature selection
• 138 Lectures + 2 Articles + 1 Downloadable resource + Full lifetime access

What you will 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

Duration: 14.5 hours
Rating: 4.3/5

Enroll here: udemy.com/course/python-for-advanced-linear-regression-masterclass