You're looking for a complete Linear Regression course that teaches you everything you need to create a Linear Regression model in Python, right?
You've found the right Linear Regression course!
After completing this course you will be able to:
- Identify the business problem which can be solved using linear regression technique of Machine Learning.
- Create a linear regression model in Python and analyze its result.
- Confidently practice, discuss and understand Machine Learning concepts
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 in 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 analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running 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 Global Analytics Consulting firm, we have helped businesses solve their business problem 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
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 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 does Machine Learning mean. 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, any machine learning model.
Section 4 - Data Preprocessing
In this section you will learn what actions you need to take a 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 models 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 do 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!
- Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same
What you'll learn:
- Learn how to solve real life problem using the Linear Regression technique
- Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression
- Predict future outcomes basis past data by implementing Simplest Machine Learning algorithm
- Understand how to interpret the result of Linear Regression model and translate them into actionable insight
- Understanding of basics of statistics and concepts of Machine Learning
- Indepth knowledge of data collection and data preprocessing for Machine Learning Linear Regression problem
- Learn advanced variations of OLS method of Linear Regression
- Course contains a end-to-end DIY project to implement your learnings from the lectures
- How to convert business problem into a Machine learning Linear Regression problem
- Basic statistics using Numpy library in Python
- Data representation using Seaborn library in Python
- Linear Regression technique of Machine Learning using Scikit Learn and Statsmodel libraries of Python
- 003 Installing Python and Anaconda
- 004 Opening Jupyter Notebook
- 005 Introduction to Jupyter
- 006 Arithmetic operators in Python Python Basics
- 010 Working with Pandas Library of Python
- 008 Lists Tuples and Directories Python Basics
- 007 Strings in Python Python Basics
- 009 Working with Numpy Library of Python
- 011 Working with Seaborn Library of Python
- 012 Types of Data
- 013 Types of Statistics
- 014 Describing data Graphically
- 015 Measures of Centers
- 017 Measures of Dispersion
- 019 Introduction to Machine Learning
- 020 Building a Machine Learning Model
- 021 Gathering Business Knowledge
- 022 Data Exploration
- 023 The Dataset and the Data Dictionary
- 024 Importing Data in Python
- 026 Univariate analysis and EDD
- 027 EDD in Python
- 029 Outlier Treatment
- 030 Outlier Treatment in Python
- 032 Missing Value Imputation
- 033 Missing Value Imputation in Python
- 035 Seasonality in Data
- 036 Bi-variate analysis and Variable transformation
- 037 Variable transformation and deletion in Python
- 039 Non-usable variables
- 040 Dummy variable creation Handling qualitative data
- 041 Dummy variable creation in Python
- 043 Correlation Analysis
- 044 Correlation Analysis in Python
- 046 The Problem Statement
- 047 Basic Equations and Ordinary Least Squares (OLS) method
- 048 Assessing accuracy of predicted coefficients
- 049 Assessing Model Accuracy RSE and R squared
- 050 Simple Linear Regression in Python
- 052 Multiple Linear Regression
- 053 The F - statistic
- 054 Interpreting results of Categorical variables
- 055 Multiple Linear Regression in Python
- 057 Test-train split
- 058 Bias Variance trade-off
- 059 Test train split in Python
- 060 Linear models other than OLS
- 061 Subset selection techniques
- 062 Shrinkage methods Ridge and Lasso
- 063 Ridge regression and Lasso in Python
- 064 Heteroscedasticity
A technology-based analytics education company
Founded by Abhishek Bansal and Pukhraj Parikh, Start-Tech Academy is a technology-based analytics education company and aims at bringing together the analytics companies and interested Learners.
Our top quality training content along with internships and project opportunities helps students in launching their Analytics journey.
Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics and Python.
Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence.
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