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In this Linear & Logistic Regression course, you’ll learn about topics such as: understanding random variables, cause-effect relationships, maximum likelihood estimation, and so much more. Follow along with the experts as they break down these concepts in easy-to-understand lessons.

Learning with Study 365 has many advantages. The course material is delivered straight to you and can be adapted to fit in with your lifestyle. It is created by experts within the industry, meaning you are receiving accurate information, which is up-to-date and easy to understand.

This course is comprised of professional learning materials, all delivered through a system that you will have access to 24 hours a day, 7 days a week for 365 days (12 months).

Who is it for?

  • Data analysts who want to move from summarizing data to explaining and prediction
  • Folks aspiring to be data scientists
  • Any business professionals who want to apply Linear regression to solve relevant problems

Course description:

This course consists of the following modules:

  • Module 01: Introduction
  • Module 02: Connect the Dots with Linear Regression
  • Module 03: Basic Statistics Used for Regression
  • Module 04: Simple Regression
  • Module 05: Applying Simple Regression
  • Module 06: Multiple Regression
  • Module 07: Applying Multiple Regression using Excel
  • Module 08: Logistic Regression for Categorical Dependent Variables
  • Module 09: Solving Logistic Regression
  • Module 10: Applying Logistic Regression

Course Duration:

From the day you purchase the course, you will have 12 months access to the online study platform. As the course is self-paced you can decide how fast or slow the training goes, and are able to complete the course in stages, revisiting the training at any time.

Method of Assessment:

At the end of each module, you will have one assignment to be submitted (you need a mark of 65% to pass) and you can submit the assignment at any time. You will only need to pay £19 for assessment and certification when you submit the assignment. You will receive the results within 72 hours of submittal, and will be sent a certificate in 7-14 days if you have successfully passed.


Successful candidates will be awarded a certificate for Machine Learning – Linear & Logistic Regression.

Entry Requirement:

  • Learners must be age 16 or over and should have a basic understanding of the English Language, numeracy, literacy, and ICT.

Career Path:

A comprehensive and useful course which will teach you many aspects like understanding random variables, cause-effect relationships and maximum likelihood estimation. Although these sound complex, you will be taught in a step by step approach which will break down these concepts so that you can understand better. A career with bright prospects await you with the completion of this course. According to www.payscale.uk, some of the key job positions along with the average UK salary per annum you can go for after completing this course will be as follows.

  • Data scientist – £35,046 per annum
  • Data Analyst – £25,988 per annum
  • Business Analyst, IT – £35,555 per annum
  • Business Manager – £34,360 per annum

Presenter Information:

Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi, and Navdeep Singh have honed their tech expertise at Google and Flipkart. Together, they have created dozens of training courses and are excited to be sharing their content with eager students. The team believes it has distilled the instruction of complicated tech concepts into enjoyable, practical, and engaging courses.

Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft

Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too

Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum

Navdeep: Longtime Flipkart employee too, and IIT Guwahati alum

PLEASE NOTE: We do not provide any software with this course.

Course Curriculum

Free Introduction
Machine Learning – Linear & Logistic Regression FREE 00:00:00
1: Introduction
1. You, This Course, & Us! 00:00:00
2: Connect the Dots with Linear Regression
1. Using Linear Regression to Connect the Dots 00:00:00
2. Two Common Applications of Regression 00:00:00
3. Extending Linear Regression to Fit Non-linear Relationships 00:00:00
3: Basic Statistics Used for Regression
1. Understanding Mean & Variance 00:00:00
2. Understanding Random Variables 00:00:00
3. The Normal Distribution 00:00:00
4: Simple Regression
1. Setting up a Regression Problem 00:00:00
2. Using Simple Regression to Explain Cause-Effect Relationships 00:00:00
3. Using Simple Regression for Explaining Variance 00:00:00
4. Using Simple Regression for Prediction 00:00:00
5. Interpreting the results of a Regression 00:00:00
6. Mitigating Risks in Simple Regression 00:00:00
5: Applying Simple Regression
1. Applying Simple Regression in Excel 00:00:00
2. Applying Simple Regression in R 00:00:00
3. Applying Simple Regression in Python 00:00:00
6: Multiple Regression
1. Introducing Multiple Regression 00:00:00
2. Some Risks inherent to Multiple Regression 00:00:00
3. Benefits of Multiple Regression 00:00:00
4. Introducing Categorical Variables 00:00:00
5. Interpreting Regression results – Adjusted R-squared 00:00:00
6. Interpreting Regression results – Standard Errors of Coefficients 00:00:00
7. Interpreting Regression results – t-statistics & p-values 00:00:00
8. Interpreting Regression results – F-Statistic 00:00:00
7: Applying Multiple Regression using Excel
1. Implementing Multiple Regression in Excel 00:00:00
2. Implementing Multiple Regression in R 00:00:00
3. Implementing Multiple Regression in Python 00:00:00
8: Logistic Regression for Categorical Dependent Variables
1. Understanding the need for Logistic Regression 00:00:00
2. Setting up a Logistic Regression problem 00:00:00
3. Applications of Logistic Regression 00:00:00
4. The link between Linear & Logistic Regression 00:00:00
5. The link between Logistic Regression & Machine Learning 00:00:00
9: Solving Logistic Regression
1. Understanding the intuition behind Logistic Regression & the S-curve 00:00:00
2. Solving Logistic Regression using Maximum Likelihood Estimation 00:00:00
3. Solving Logistic Regression using Linear Regression 00:00:00
4. Binomial vs Multinomial Logistic Regression 00:00:00
10: Applying Logistic Regression
1. Predict Stock Price movements using Logistic Regression in Excel 00:00:00
2. Predict Stock Price movements using Logistic Regression in R 00:00:00
3. Predict Stock Price movements using Rule-based & Linear Regression 00:00:00
4. Predict Stock Price movements using Logistic Regression in Python 00:00:00

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