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Machine Learning for Social Scientists: Learn ML Using R cover
Data Analysis and Research

Machine Learning for Social Scientists: Learn ML Using R

0 students
Last updated Jul 2026

Course Overview

About This Course

According to various estimates, Machine Learning is among the highest-paid job in the industry, and salaries of Machine Learning professionals could usually be above US$1,00,000 per annum. If you are looking forward to a course that can get you gently started with Machine Learning, this course is for you.

Requirements
Familiarity with basic research process will be helpful but not essential.
A keen desire to learn Machine Learning and R
A laptop with internet connection
Familiarity with basic computer and operating system

Learning Outcomes

Fundamentals of Machine Learning

Applications of Machine Learning

Statistical concepts underlying Machine Learning

Supervised Machine Learning Algorithms

Unsupervised Machine Learning Algorithms

Curriculum

4 Sections • 42 Lectures
Know Your Instructor
3:17
Course Introduction
Pre-requisites
5:25
Course Outcomes
2:58
System Requirements
6:03
Installing R-Package & R-Studio
9:43
Getting Familiar with R Environment
5:11
What is Machine Learning?
7:16
Applications of Machine Learning
6:09
Machine Learning Steps
13:41
Types of Machine Learning
11:19
What is Supervised Machine Learning?
9:32
Types of Supervised Machine Learning
5:31
Introduction to Linear Regression
5:15
Applications of Linear Regression Algorithm
2:39
Understanding Equation and Formula of Linear Regression
7:29
Calculating Parameters of Linear Regression Model
5:26
What Does 'Y is Regressed on X' Means?
4:56
Understanding Unstandardized and Standardized Beta Values
4:39
Understanding Error Term
4:51
Understanding Intercept
1:59
Understanding R Squared or Coefficient of Variation
5:09
Understanding Multiple R
7:18
Manual Calculation of Model Parameters
9:20
Calculating Model Parameters in Excel - Part 1
15:09
Calculating Model Parameters in Excel - Part 2
9:14
Calculating Model Parameters in Excel - Part 3 (Model Summary)
5:58
Implementing Linear Regression Algorithm in R
30:51
What is KNN Algorithm?
3:11
Applications of KNN Algorithm
3:25
Concept of Euclidean Distance
5:13
How to Calculate Euclidean Distance?
10:02
Understanding KNN Function in R
3:53
Understanding Confusion Matrix
5:28
Understanding True Positives
3:13
Understanding True Negatives
1:53
Understanding False Positives
2:21
Understanding False Negatives
1:24
Estimating Accuracy of KNN Model
1:00
Kappa Coefficient as an Estimate of KNN Model Accuracy
2:24
Other Measures of KNN Model Accuracy
1:37
Implementing KNN Algorithm in R
23:55
Meet your instructors

Learn from Experts and Leaders

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Students
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An experienced and passionate instructor, [Name] is dedicated to fostering a dynamic and engaging learning environment where students feel motivated to grow both academically and personally. With a strong foundation in their subject area, they bring clarity, structure, and enthusiasm to every lesson, ensuring complex concepts are made accessible and relevant. Their teaching approach blends theoretical knowledge with practical application, encouraging students to think critically and develop problem-solving skills. Known for their supportive and approachable demeanor, [Name] builds meaningful connections with students, understanding that each learner has unique strengths and challenges. They are committed to creating an inclusive classroom atmosphere where curiosity is encouraged, questions are welcomed, and every student feels valued. By adapting teaching methods to suit diverse learning styles, they help students gain confidence and achieve their full potential. Beyond the classroom, [Name] is deeply invested in continuous learning and professional development, staying updated with the latest educational practices and advancements in their field. They often contribute to curriculum development, mentorship programs, and extracurricular activities, reinforcing their role as not just an instructor, but a mentor and guide. Their ultimate goal is to inspire lifelong learning and empower students to succeed in an ever-evolving world.

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