Linear regression and logistic regression for beginners. Understand the difference between regression and classification
You are looking for the perfect Linear regression and logistics regression course Which teaches you everything you need to create a linear or logistic regression model in R Studio, right?
You’ve found the right linear regression course!
After completing this course, Dr. You will be able to:
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Identify business problems that can be solved using linear and logistic regression techniques of machine learning.
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Create a linear regression and logistic regression model in R Studio and analyze its results.
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Practice, discuss and understand machine learning concepts with confidence
A Verification certificate of completion This machine is introduced to all students doing the Basics of Learning Basics course.
How will this course help you?
If you are a business manager or executive, or a student who wants to learn and apply machine learning in real world problems in business, this course will give you a solid foundation by teaching you the most popular techniques of machine learning. Is linear regression
Why should you choose this course?
This course covers all the steps required to solve a business problem through linear regression.
Most courses only focus on teaching how to run the analysis but we believe that what happens before and after the analysis is more important is that you have the right data before running the analysis and do some pre-processing on it. And after running the analysis, you should be able to determine how good your model is and explain the results to actually help your business.
What qualifies us to teach you?
This course is taught by Abhishek and Pukhraj. As a manager at a global analytics consulting firm, we have helped businesses solve their business problems using machine learning techniques, and we have used our experience to incorporate practical aspects of data analysis into this course.
With over 150,000 enrollments and thousands of 5-star reviews like this – we’re also the creators of some of the most popular online courses:
This is very good, I like that all the explanations given can be understood by the common man – Joshua
Thanks to the author for this wonderful course. You are the best and this course is worth it. – Daisy
Our word
It is our job to teach our students and we are committed to that. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post questions in the course or send us a direct message.
Download practice files, take quizzes and complete assignments
With each lecture, class notes are attached for you to follow. You can also take a quiz to understand your concept. Each section has a practice assignment to put your learning into practice.
What is included in this course?
This course teaches you all the steps to create a linear regression model, which is the most popular machine learning model, for solving business problems.
The content of this course on linear regression is given below:
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Section 1 – Statistics Basics
This section is divided into five different lectures, starting with the types of data and then into the types of statistics
Then a graphical presentation to describe the data and then a lecture on intermediate solutions
Measurements of mean and mode and finally dispersion such as range and standard deviation
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Section 2 – Python Basics
This section starts with Python.
This section will help you set up and teach Python and Jupiter environments on your system
How to perform some basic operations in Python. Let us understand the importance of various libraries like Numpy, Pandas and Seaborn.
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Section 3 – Introduction to Machine Learning
In this section we will learn – what is machine learning. What are the meanings or different terms associated with machine learning? You will see some examples so that you can understand what machine learning is. It also includes the stages of making a machine learning model, not just a linear model but any machine learning model.
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Section 4 – Data preprocessing
In this section you will learn what actions you need to take to get the data and then
Prepare for analysis These steps are very important.
We start by understanding the importance of business knowledge and then let’s see how to do data exploration. We will learn how to do Uni-Variety Analysis and Buy-Variet Analysis, then we will cover the topic. External treatment, missing value charges, variable transformation and correlation.
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Section 5 – Regression Model
This section starts with simple linear regression and then covers multiple linear regression.
Without much math about how we covered the basic theory behind each concept
Understand where the concept came from and how important it is. But even if I don’t understand
That is fine as long as you learn how to run and interpret the results as taught in the practical lectures.
We also look at how to quantify the accuracy of models, what F statistics mean, how clear variables in individual variables datasets are interpreted in results, what are the other differences in the general minimum class method, and how we finally interpret the result. To find the answer to a business problem.
At the end of this course, your confidence to build a regression model in Python will increase. You will have a thorough knowledge of how to use regression modeling to create predictive models and solve business problems.
Go ahead and click on the Enrollment button and I’ll see you in Chapter 1!
Cheers
Start-Tech Academy
A
Below is a list of popular FAQs for students looking to embark on their machine learning journey-
What is machine learning?
Machine learning is a field of computer science that gives computers the ability to learn without explicitly programming. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.
What is the linear regression technique of machine learning?
Linear regression is a simple machine learning model for regression problems, i.e., when the target variable is the actual value.
Linear regression is a linear model, e.g. A model that assumes a linear relationship between input variables (x) and a single output variable (y). More specifically, y can be calculated from the linear combination of input variables (x).
When there is a single input variable (x), the method is referred to as simple linear regression.
When there are multiple input variables, the method is known as multiple linear regression.
Why learn linear regression techniques of machine learning?
There are four reasons to learn linear regression techniques of machine learning:
1. Linear regression is the most popular machine learning technique
2. Estimation accuracy is good in linear regression
3. Linear regression is easy to implement and easy to interpret
4. It gives you a strong foundation to start learning other advanced techniques of machine learning
How long does it take to learn the linear regression technique of machine learning?
Linear regression is easy but no one can determine the learning time required for it. It’s totally up to you. The method we adopted to help you learn linear regression starts with the basics and takes you to an advanced level in a matter of hours. You can follow it, but remember that you can’t learn anything without practicing. Practicing is the only way to remember what you learned. Therefore, we have also provided you with another data set to work as a separate project of linear regression.
What steps should I follow to be able to build a machine learning model?
You can divide your learning process into 4 parts:
Statistics and Probability – Basic knowledge of statistics and probability concepts is required to implement machine learning techniques. This part is included in the second section of the syllabus.
Understanding Machine Learning – Section 4 helps you to understand the terms and concepts related to machine learning and gives you the steps to follow to build a machine learning model.
Programming Experience – An important part of machine learning is programming. Python and R are clearly leaders in recent days. The third section will help you set up the Python environment and teach you some basic operations. The next part is a video on how to implement each of the concepts taught in the theory lecture in Python
Understanding Linear Regression Modeling – With a good knowledge of linear regression you get a solid understanding of how machine learning works. Although linear regression is the simplest technique of machine learning, it is still the most popular with good predictability. The fifth and sixth sections cover the end-to-end cover of the linear regression topic and provide a relevant practical lecture with each theory lecture where we run each query with you.
Why use Python for data machine learning?
Understanding Python is one of the most valuable skills required for a career in machine learning.
Although this is not always the case, Python is the programming language of choice for data science. Here is a brief history:
In 2016, it surpassed R on Kaggle, the premier platform for data science competitions.
In 2017, it surpassed R in KDNugges ‘annual survey of data scientists’ most used tools.
In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.
Machine learning experts expect this trend to continue with the growing development in the Python ecosystem. And while your journey of learning Python programming has only just begun, it’s a pleasure to learn that there are plenty (and growing) job opportunities.
What is the difference between data mining, machine learning and deep learning?
Simply put, machine learning and data mining use the same algorithms and techniques as data mining, except that the estimates are different. While data mining seeks previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge — and further that information is automatically applied to data, decision making, and actions.
Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data for learning, understanding, and identifying complex patterns. Automatic language translation and medical diagnostics are examples of in-depth learning.
Linear regression and logistic regression for beginners. Understand the difference between regression and taxonomy
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