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Table of Contents

        1. Know Your Instructor
        2. Course Introduction
        1. Pre-requisites
        2. Course Outcomes
        1. System Requirements
        2. Installing R-Package & R-Studio
        3. Getting Familiar with R Environment
        1. What is Machine Learning?
        2. Applications of Machine Learning
        3. Machine Learning Steps
        4. Types of Machine Learning
        5. What is Supervised Machine Learning?
        6. Types of Supervised Machine Learning
        1. Introduction to Linear Regression
        2. Applications of Linear Regression Algor...
        3. Understanding Equation and Formula of L...
        4. Calculating Parameters of Linear Regres...
        5. What Does 'Y is Regressed on X' Means?
        6. Understanding Unstandardized and Standa...
        7. Understanding Error Term
        8. Understanding Intercept
        9. Understanding R Squared or Coefficient ...
        10. Understanding Multiple R
        1. Manual Calculation of Model Parameters
        2. Calculating Model Parameters in Excel -...
        3. Calculating Model Parameters in Excel -...
        4. Calculating Model Parameters in Excel -...
        5. Implementing Linear Regression Algorith...
        1. What is KNN Algorithm?
        2. Applications of KNN Algorithm
        3. Concept of Euclidean Distance
        4. How to Calculate Euclidean Distance?
        5. Understanding KNN Function in R
        6. Understanding Confusion Matrix
        7. Understanding True Positives
        8. Understanding True Negatives
        9. Understanding False Positives
        10. Understanding False Negatives
        11. Estimating Accuracy of KNN Model
        12. Kappa Coefficient as an Estimate of KNN...
        13. Other Measures of KNN Model Accuracy
        1. Implementing KNN Algorithm in R

Contents

Machine Learning for Social Scientists: Learn ML Using R

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    • Statistics & Data Science

    Course Description

    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.

    Learning Outcomes

    On completion of this course you will be able to understand:

    • 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

    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

    Certification

    You will receive a course completion certificate after completion of the course.

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    Instructor

    Ritesh Chouhan
    Ritesh Chouhan

    Scholarsight is a Knowledge Organization. Our mission is to humanize scientific methods through technology-enabled learning. We are building Galaxy's biggest library of courses for scientists, inventors, and professionals.

    Dr. Amit Kumar Mishra

    Dr. Amit Kumar Mishra is M.Tech and Ph.D. with specialization in the area of Social Network Analysis. He is co-author of a book on Machine Learning. He holds national and international patents and his articles have appeared in reputed journals published by Elsevier, Springer Nature, etc.

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