What you would learn in Linear Regression and Logistic Regression in Python course?
Are you looking for a comprehensive Linear Regression or Logistic Regression training course that will teach all you need to know to build Linear and Logistic Regression models using Python?
You've found the perfect Linear Regression course!
When you've completed the course, you can:
Find the solvable business issue using the logistic and linear regression techniques that are part of Machine Learning.
Create a linear regression model and logistic regression modeling in Python and examine the result.
Be confident in your ability to model and solve classification and regression problems.
A Verifiable Certificate of Completion is given to all students who complete this Machine Learning Basics course.
What's included during this training?
This course will teach you the steps to create a Linear Regression model, the most well-known Machine Learning model, to tackle business challenges.
Here are the contents for this class on Linear Regression:
Section 1: Basics of Statistics
It is broken down into five different lectures that start with kinds of data, then different types of statistics.
Then, graphical representations of the data. Then, an explanation of the measures of center-like mean
Mode and median, and finally measures of dispersion such as standard deviation and range
2. Section 2 Python basic
This section will help you get getting started with Python.
This section will assist you in installing the Python and the Jupyter setting on the computer and will guide you through the process.
You will learn how to carry out basic tasks to perform basic operations in Python. We will learn about the importance of various libraries like Numpy, Pandas & Seaborn.
Section 3 An Introduction to Machine Learning
In this article, we will find out the meaning of Machine Learning means. What are the definitions or different terms used in machine learning? Here are some examples to show the purpose behind machine learning. It also outlines the steps involved in creating a machine learning model, not only linear models but for any modeling of machine learning.
4. Section 4: Data Processing
In this article, you will find out what steps you should take to follow step by step approach to collect the information and later.
Prepare it for analysis; these steps are incredibly crucial.
We begin by understanding the importance of knowledge in business, and then we'll learn how to perform data exploration. We will learn to perform bivariate and univariate analysis, and then we will cover topics such as outlier treatment and missing value imputation. The transformation of variables as well as correlation.
Section 5 Regression Model
This section begins with a linear regression that is simple and covers several linear regressions.
We've covered the fundamental concepts without going into a mathematical discussion about them so that you can be more comfortable with them.
Know where the idea comes from and why it's essential. But even if you don't understand
It will be fine when you understand how to interpret and run the results in the hands-on lectures.
We also examine how to measure the accuracy of models as well as the meaning of F statistic and how categorical variables from the dataset of independent variables can be treated in the analysis as well as other variations of the standard least squared approach and what is the best way to analyze the results to figure the solution to a business issue.
At the end of this course, the confidence you have in developing a model for regression using Python will skyrocket. You'll be able to grasp the basics of utilizing regression modeling to build predictive models to solve business issues.
What will this course do to aid you?
Suppose you're a business director or executive or a student wanting to master and apply machine learning to solve real-world issues in business. In that case, This course will give you a solid foundation for learning the most widely used machine learning techniques, including Linear Regression and Logistic Regregression.
What are the reasons for selecting this particular course?
This course will cover all the actions one should follow when solving a business issue using logistic regression and linear regression.
The majority of courses concentrate on teaching the way to run the analysis. However, we believe that what happens before and after running the analysis is just as important, i.e., before you can run an analysis, it's essential to gather the correct data and perform some preprocessing. After running the analysis, you will be able to assess how well your model works and use the results in a way that is effective for your business.
- Learn to solve real-world problems by using techniques like the Linear as well as Logistic Regression technique
- Initial analysis of data by using Univariate as well as Bivariate analysis before conducting regression analysis
- Know what to make of the results that come from Linear as well as Logistic Regression models and translate these into practical insights
- Deep understanding of data collection and preprocessing to solve Linear as well as Logistic Regression issues
- Basic statistics with Numpy library within Python
- Data representation by using Seaborn library of Python
- Linear Regression is a technique for Machine Learning using Scikit Learn and Statsmodel libraries from Python