Machine Learning for Social Science

Machine Learning for Social Science, available at $64.99, has an average rating of 4, with 44 lectures, 1 quizzes, based on 4 reviews, and has 144 subscribers.

You will learn about Get started with Machine Learning Using R Programming Language Build and Test Your Own Machine Learning Model Analyze Data Using Supervised Machine Learning Models Analyze Data Using Unsupervised Machine Learning Models This course is ideal for individuals who are Researchers looking to learn and apply Machine Learning or Data Scientists looking to Master R for Machine Learning It is particularly useful for Researchers looking to learn and apply Machine Learning or Data Scientists looking to Master R for Machine Learning.

Enroll now: Machine Learning for Social Science

Summary

Title: Machine Learning for Social Science

Price: $64.99

Average Rating: 4

Number of Lectures: 44

Number of Quizzes: 1

Number of Published Lectures: 44

Number of Published Quizzes: 1

Number of Curriculum Items: 45

Number of Published Curriculum Objects: 45

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Get started with Machine Learning Using R Programming Language
Build and Test Your Own Machine Learning Model
Analyze Data Using Supervised Machine Learning Models
Analyze Data Using Unsupervised Machine Learning Models

Who Should Attend

Researchers looking to learn and apply Machine Learning
Data Scientists looking to Master R for Machine Learning

Target Audiences

Researchers looking to learn and apply Machine Learning
Data Scientists looking to Master R for Machine Learning

“We are bringing technology to philosophers and poets.”

Machine Learning is usually considered to be the forte of professionals belonging to the programming and technology domain. People from arts and social science with no background in programming/technology often find it challenging to learn Machine Learning. However, Machine learning is not for technologists and programmers only. It is for everyone who wants to be a better researcher and decision-maker.

Machine Learning is for anyone looking to model how humans and machines make decisions, develop mathematical models of decisions, improve decision-making accuracy based on data, and do science with data.

Machine Learning brings you closer to the fascinating world of artificial intelligence. Machine Learning is a cross-disciplinary field encompassing computer science, mathematics, statistics, psychology, and management. It’s currently tough for normal learners to understand so many subjects, making Machine Learning inaccessible to many, especially those from social science backgrounds.

We built this course, “Machine Learning for Social Scientists,” to help learners master this topic without getting stuck in its technicalities or fear of coding. This course is built as a scratch to the advanced level course for Machine Learning. All the topics are explained with the basics.The instructor creates a connection with everyday instances and fundamental tools so that learners feel connected to their previous learning. For example, we demo some Excel calculations to ensure learners can see the connection between Excel spreadsheet analysis and Machine Learning using R language.

The course covers the following topics:

· Fundamentals of Machine Learning

· Applications of Machine Learning

· Statistical concepts underlying Machine Learning

· Supervised Machine Learning Algorithms

· Unsupervised Machine Learning Algorithms

· How to Use R to Implement Machine Learning Algorithms

· How to create Training and Testing datasets and train Machine Learning Models

· How to improve the accuracy of Machine Learning Models

· Linear Regression Algorithm

· Calculation of Parameters of Linear Regression Model manually, using Excel and R

· K Nearest Neighbor (KNN) Analysis

· Understanding Mathematics behind K Nearest Neighbor Analysis

· Estimating sensitivity and specificity of the model

· Implementing KNN Algorithm in R

· Many more

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. To join the course, click on the Sign Up button and start your journey in Machine Learning from today.

Course Curriculum

Chapter 1: Prerequisites and Learning Outcomes

Lecture 1: Prerequisites

Lecture 2: Learning Outcomes

Lecture 3: Know Your Instructor

Chapter 2: Downloading and Installing the R Software

Lecture 1: Understanding System Requirements for Installing R and R Studio

Lecture 2: Installing R and R Studio in Your Computer

Lecture 3: Getting Started with R

Chapter 3: Introduction to Machine Learning: Core Concepts

Lecture 1: What is Machine Learning?

Lecture 2: Applications of Machine Learning

Lecture 3: Machine Learning Steps

Lecture 4: Types of Machine Learning

Lecture 5: What is Supervised Machine Learning?

Lecture 6: Types of Supervised Machine Learning

Chapter 4: Supervised Machine Learning Using Linear Regression Algorithm

Lecture 1: Introduction to Linear Regression

Lecture 2: Applications of Linear Regression Algorithm

Lecture 3: Understanding Equation and Formula of Linear Regression

Lecture 4: Calculating Parameters of Linear Regression Model

Lecture 5: What Does 'Y is Regressed on X' Means?

Lecture 6: Understanding Unstandardized and Standardized Beta Values

Lecture 7: Understanding Error Term

Lecture 8: Understanding Intercept

Lecture 9: Understanding R Squared or Coefficient of Variation

Lecture 10: Understanding Multiple R

Lecture 11: Manual Calculation of Model Parameters

Lecture 12: Calculating Model Parameters in Excel – Part 1

Lecture 13: Calculating Model Parameters in Excel – Part 2

Lecture 14: Calculating Model Parameters in Excel – Part 3 (Model Summary)

Lecture 15: Implementing Linear Regression Algorithm in R

Chapter 5: K- Nearest Neighbour Algorithm (KNN)

Lecture 1: What is KNN Algorithm?

Lecture 2: Applications of KNN Algorithm

Lecture 3: Concept of Euclidean Distance

Lecture 4: How to Calculate Euclidean Distance?

Lecture 5: Understanding KNN Function in R

Lecture 6: Understanding Confusion Matrix

Lecture 7: Understanding True Positive

Lecture 8: Understanding True Negative

Lecture 9: Understanding False Positive

Lecture 10: Understanding False Negative

Lecture 11: Estimating Accuracy of KNN Model

Lecture 12: Kappa Coefficient as an Estimate of KNN Model Accuracy

Lecture 13: Other Measures of KNN Model Accuracy

Lecture 14: Implementing KNN Algorithm in R

Chapter 6: References

Lecture 1: Reference Books

Lecture 2: Foundation Research Papers for Learning ML

Chapter 7: Next Step

Lecture 1: Bonus Lecture

Instructors

Scholarsight Learning
Courses in High Impact Research & Technology

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 0 votes
4 stars: 0 votes
5 stars: 3 votes

Frequently Asked Questions

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