You’re looking for an entire rectilinear regression and Logistic Regression course that teaches you everything you would like to make a Linear or Logistic Regression model in R Studio, right?
You’ve found the proper rectilinear regression course!
After this course:
Identify the business problem which may be solved using linear and logistic regression technique of Machine Learning.
Create a rectilinear regression and logistic regression model in R Studio and analyze its result.
Confidently practice, discuss and understand Machine Learning concepts
A Verifiable Certificate of Completion is presented to all or any students who undertake this Machine learning basics course.
How this course will help you?
If you’re a business manager or an executive, or a student who wants to find out and apply machine learning in world problems of business, this course will offer you a solid base for that by teaching you the foremost popular technique of machine learning, which is rectilinear regression
Why do you have to choose this course?
This course covers all the steps that one should take while solving a business problem through rectilinear regression .
Most courses only specialise in teaching the way 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’s vital that you simply have the proper data and do some pre-processing thereon . And after running analysis, you ought to be ready to judge how good your model is and interpret the results to truly be ready to help your business.
What makes us qualified to show you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics consulting company , we’ve helped businesses solve their business problem using machine learning techniques and that we have used our experience to incorporate the sensible aspects of knowledge analysis during this course
We also are the creators of a number of the foremost popular online courses – with over 150,000 enrollments and thousands of 5-star reviews like these ones:
This is excellent , i really like the very fact the all explanation given are often understood by a layman – Joshua
Thank you Author for this excellent course. you’re the simplest and this course is worth any price. – Daisy
Teaching our students is our job and that we are committed thereto . If you’ve got any questions on the course content, practice sheet or anything associated with any topic, you’ll always post an issue within the course or send us an immediate message.
Download Practice files, take Quizzes, and complete Assignments
With each lecture, there are class notes attached for you to follow along. you’ll also take quizzes to see your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.
What is covered during this course?
This course teaches you all the steps of making a rectilinear regression model, which is that the hottest Machine Learning model, to unravel business problems.
Below are the course contents of this course on Linear Regression:
Section 1 – Basics of Statistics
This section is split into five different lectures ranging from sorts of data then sorts of statistics
then graphical representations to explain the info then a lecture on measures of center like mean
median and mode and lastly measures of dispersion like range and variance
Section 2 – Python basic
This section gets you started with Python.
This section will assist you found out the python and Jupyter environment on your system and it will teach
you how to perform some basic operations in Python. we’ll understand the importance of various libraries like Numpy, Pandas & Seaborn.
Section 3 – Introduction to Machine Learning
In this section we’ll learn – What does Machine Learning mean. What are the meanings or different terms related to machine learning? you’ll see some examples in order 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’ll learn what actions you would like to require a step by step to urge the info then
prepare it for the analysis these steps are vital .
We start with understanding the importance of business knowledge then we’ll see the way to do data exploration. We find out how to try to to 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 rectilinear regression then covers multiple rectilinear regression .
We have covered the essential theory behind each concept without getting too mathematical about it in order that you
understand where the concept is coming from and the way it’s important. But albeit you do not understand
it, it’ll be okay as long as you find out how to run and interpret the result as taught within the practical lectures.
We also check out the way to quantify models accuracy, what’s the meaning of F statistic, how categorical variables within the independent variables dataset are interpreted within the results, what are other variations to the standard least squared method and the way can we finally interpret the result to seek out out the solution to a business problem.
By the top of this course, your confidence in creating a regression model in Python will soar. you will have a radical understanding of the way to use regression modelling to make predictive models and solve business problems.
Go ahead and click on the enroll button, and I’ll see you in lesson 1!
Below may be a list of popular FAQs of scholars who want to start out their Machine learning journey-
What is Machine Learning?
Machine Learning may be a field of computing which provides the pc the power to find out without being explicitly programmed. it’s a branch of AI supported the thought that systems can learn from data, identify patterns and make decisions with minimal human intervention.
What is the rectilinear regression technique of Machine learning?
Linear Regression may be a machine learning model for regression problems, i.e., when the target variable may be a real value.
Linear regression may be a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and therefore the single output variable (y). More specifically, that y are often calculated from a linear combination of the input variables (x).
When there’s one input variable (x), the tactic is mentioned as simple rectilinear regression .
When there are multiple input variables, the tactic is understood as multiple rectilinear regression .
Why learn rectilinear regression technique of Machine learning?
There are four reasons to find out rectilinear regression technique of Machine learning:
1. rectilinear regression is that the hottest machine learning technique
2. rectilinear regression has fairly good prediction accuracy
3. rectilinear regression is straightforward to implement and straightforward to interpret
4. It gives you a firm base to start out learning other advanced techniques of Machine Learning
How much time does it fancy learn rectilinear regression technique of machine learning?
Linear Regression is straightforward but nobody can determine the training time it takes. It totally depends on you. the tactic we adopted to assist you learn rectilinear regression starts from the fundamentals and takes you to advanced level within hours. you’ll follow an equivalent , but remember you’ll learn nothing without practicing it. Practice is that the only thanks to remember whatever you’ve got learnt. Therefore, we’ve also provided you with another data set to figure on as a separate project of rectilinear regression .
What are the steps I should follow to be ready 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 ideas related to Machine learning and provides you the steps to be followed to create a machine learning model
Programming Experience – a big a part of machine learning is programming. Python and R clearly stand bent be the leaders within the recent days. Third section will assist you found out the Python environment and teach you some basic operations. In later sections there’s a video on the way to implement each concept taught in theory lecture in Python
Understanding of rectilinear regression modelling – Having an honest knowledge of rectilinear regression gives you a solid understanding of how machine learning works. albeit rectilinear regression is that the simplest technique of Machine learning, it’s still the foremost popular one with fairly good prediction ability. Fifth and sixth section cover rectilinear 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 among the precious skills needed for a career in Machine Learning.
Though it hasn’t always been, Python is that the programing language of choice for data science. Here’s a quick 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 knowledge scientists’ most used tools.
In 2018, 66% of knowledge scientists reported using Python daily, making it the amount one tool for analytics professionals.
Machine Learning experts expect this trend to continue with increasing development within the Python ecosystem. And while your journey to find out Python programming could also be just beginning, it’s nice to understand that employment opportunities are abundant (and growing) also .
What is the difference between data processing , Machine Learning, and Deep Learning?
Put simply, machine learning and data processing use an equivalent algorithms and techniques as data processing , except the sorts of predictions vary. While data processing 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 opposite hand, uses advanced computing power and special sorts of neural networks and applies them to large amounts of knowledge to find out , understand, and identify complicated patterns. Automatic language translation and medical diagnoses are samples of deep learning.
Who this course is for:
People pursuing a career in data science
Working Professionals beginning their Data journey
Statisticians needing more practical experience
Anyone curious to master Linear and Logistic Regression from beginner to advanced level during a short span of your time