आप एक पूर्ण Linear Regression course की तलाश कर रहे हैं जो आपको वह सब कुछ सिखाता है जो आपको Python में Linear Regression model बनाने के लिए चाहिए, है ना?
आपको सही Linear Regression course मिल गया है!
After completing this course you will be able to:
A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.
How this course will help you?
If you are a business manager or an executive, or a student who wants to learn and apply machine learning to real-world problems of business, this course will give you a solid base for that by teaching you the most popular technique of machine learning, which is Linear Regression
Why should you choose this course?
This course covers all the steps that one should take while solving 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 running the analysis is even more important i.e. before running the analysis it is very important that you have the right data and do some pre-processing on it. After running the analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in a Global Analytics Consulting firm, we have helped businesses solve their business problems using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course
We are also the creators of some of the most popular online courses – with over 150,000 enrollments and thousands of 5-star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman – Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price. – Daisy
Our Promise
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet, or anything related to any topic, you can always post a question in the course or send us a direct message.
Download Practice files, take Quizzes, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.
What is covered in this course?
This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems.
Below are the course contents of this course on Linear Regression:
Section 1 – Basics of Statistics
This section is divided into five different lectures starting from types of data then types of statistics
then graphical representations to describe the data and then a lecture on measures of a center like mean
median and mode and lastly measures of dispersion like range and standard deviation
Section 2 – Python basic
This section gets you started with Python.
This section will help you set up the Python and Jupyter environment on your system and it’ll teach
you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.
Section 3 – Introduction to Machine Learning
In this section we will learn – What Machine Learning means. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, but any machine learning model.
Section 4 – Data Preprocessing
In this section you will learn what actions you need to take step by step to get the data and then prepare it for the analysis these steps are very important.
We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation, and correlation.
Section 5 – Regression Model
This section starts with simple linear regression and then covers multiple linear regression.
We have covered the basic theory behind each concept without getting too mathematical about it so that you understand where the concept is coming from and how it is important. But even if you don’t understand it, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.
We also look at how to quantify model accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method, and how we finally interpret the result to find out the answer to a business problem.
By the end of this course, your confidence in creating a regression model in Python will soar. You’ll have a thorough understanding of how to use regression modeling to create predictive models and solve business problems.
Go ahead and click the enroll button, and I’ll see you in lesson 1!
Cheers
Start-Tech Academy
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Below is a list of popular FAQs for students who want to start their Machine learning journey-
What is Machine Learning?
Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. 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 a real value.
Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, y can be calculated from a linear combination of the 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 the Linear regression technique of Machine learning?
There are four reasons to learn the Linear regression technique of Machine learning:
1. Linear Regression is the most popular machine-learning technique
2. Linear Regression has a fairly good prediction accuracy
3. Linear Regression is simple to implement and easy to interpret
4. It gives you a firm base to start learning other advanced techniques of Machine Learning
How much time does it take to learn the Linear regression technique of machine learning?
Linear Regression is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn Linear regression starts from the basics and takes you to an advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learned. Therefore, we have also provided you with another data set to work on as a separate project of Linear regression.
Why use Python for data Machine Learning?
Understanding Python is one of the valuable skills needed for a career in Machine Learning.
Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:
In 2016, it overtook R on Kaggle, the premier platform for data science competitions.
In 2017, it overtook R on KDNuggets’s annual poll 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 increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.