4.23 (952 reviews)
☑ 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
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 modelling to create predictive models and solve business problems.
Go ahead and click the enroll button, and I'll see you in lesson 1!
Below is a list of popular FAQs of 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, that 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 Linear regression technique of Machine learning?
There are four reasons to learn Linear regression technique of Machine learning:
1. Linear Regression is the most popular machine learning technique
2. Linear Regression has 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 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 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 learnt. Therefore, we have also provided you with another data set to work on as a separate project of Linear regression.
What are the steps I should follow to be able to build a Machine Learning model?
You can divide your learning process into 4 parts:
Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.
Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model
Programming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python
Understanding of Linear Regression modelling - Having a good knowledge of Linear Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.
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.
What is the difference between Data Mining, Machine Learning, and Deep Learning?
Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information 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 to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.
Welcome to the course!
Setting up Python and Jupyter Notebook
Installing Python and Anaconda
Opening Jupyter Notebook
Introduction to Jupyter
Arithmetic operators in Python: Python Basics
Strings in Python: Python Basics
Lists, Tuples and Directories: Python Basics
Working with Numpy Library of Python
Working with Pandas Library of Python
Working with Seaborn Library of Python
Basics of Statistics
Types of Data
Types of Statistics
Describing data Graphically
Measures of Centers
Practice Exercise 1
Measures of Dispersion
Practice Exercise 2
Introduction to Machine Learning
Introduction to Machine Learning
Building a Machine Learning Model
Introduction to Machine learning quiz
Gathering Business Knowledge
The Dataset and the Data Dictionary
Importing Data in Python
Project exercise 1
Univariate analysis and EDD
EDD in Python
Project Exercise 2
Outlier Treatment in Python
Project Exercise 3
Missing Value Imputation
Missing Value Imputation in Python
Project Exercise 4
Seasonality in Data
Bi-variate analysis and Variable transformation
Variable transformation and deletion in Python
Project Exercise 5
Dummy variable creation: Handling qualitative data
Dummy variable creation in Python
Project Exercise 6
Correlation Analysis in Python
Project Exercise 7
The Problem Statement
Basic Equations and Ordinary Least Squares (OLS) method
Assessing accuracy of predicted coefficients
Assessing Model Accuracy: RSE and R squared
Simple Linear Regression in Python
Project Exercise 8
Multiple Linear Regression
The F - statistic
Interpreting results of Categorical variables
Multiple Linear Regression in Python
Project Exercise 9
Bias Variance trade-off
Test train split in Python
Linear models other than OLS
Subset selection techniques
Shrinkage methods: Ridge and Lasso
Ridge regression and Lasso in Python
Project Exercise 10
Final Project Exercise
It is a well crafted course. There is only one thing that I want to suggest: please add more questions in regular interval of the course to make it more effective. Having said that, this is a one of the great courses that I came across in recent times. Kudos! Thank you!!
Theoretical explanation could have been better. Throughout the course I struggled to make connection between theory and the practical info.
very difficult to understand the english. some topic I just keep replay again and again still cannot hear what you said. and the subtitles are also wrong. I am so disppointed and finally I stop and will not finish this course.
This is good course that i have learn in Udemy, but some script does not run with my jupiternotebooks.
Yes.i am very interested to learn MACHINE LEARNING so I am very excited to learn this... and I think this will be very useful for me.thank you
Es gratis y de momento ya me ha enseñado a usar un notebook así que el tiempo invertido ya ha merecido la pena
Theoretical Explanations could have been more better. Lot of complex terms used which can create confusion for beginners.
Ya I really like it all so far. Good stuff and very thorough on the programming side. I like that he didn't skimp on the command line instruction or water it all down with GUI interface. Thanks!
Very informative way for teaching, like the course takes you through the theoretic basic than the application in python and this for each element. i find it useful specially for beginners.
Good introductory course to Machine Learning. It is true that a lot of the time is spent in setting up the data. This course covers how to do that too. Well done
Prepare one separate course on 'Data Preprocessing' and include all the detail of preprocessing into it.
For beginners like me, the python crash course is a great thing to have in the starting of the course. Clear instructions.
happy listen my native accent ..... going on good... outlier treatment needs to be elaborated will surely have to revise the whole multiple times..please do not give gaps during the courses....theory is more but managable...need to do a practical problem to understand better...explanation was good and worth taking up the cousre... :)
Buen curso, no solo para empezar a hacer regresiones en Python, sino para aprender los conceptos bàsicos del lenguaje. Se podrìa mejorar poniendo los còdigos en algun archivo para poder descargarlo.
It's my first machine learning course.Really,I found this course very useful.Just want quick reply of my questions in Q& A section.