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Regression Analysis for Statistics & Machine Learning in R

By: Minerva Singh

  • 06:32:01
  • 47
  • 5
  • Language: English

Course Summary

With so many R Statistics & Machine Learning courses around, why  enroll for this ?<

Read More

Target Audience

  1. People with basic knowledge of R based statistical modelling
  2. People with knowledge of linear regression modelling
  3. People wanting to extend their knowledge of regression modelling for solving real world problems.
  4. People wanting to learn how to apply machine learning based regression models using R
  5. Undergraduates and postgraduates seeking to deepen their knowledge of statistical and machine learning analysis
  6. Academic researchers seeking to learn new techniques for data analysis
  7. Business data analysts who wish to use regression modelling for predictive analysis

Pre-Requisites

  1. Students should have prior experience of working with R and RStudio
  2. Students should have prior exposure to statistics
  3. Students should have prior experience of using simple linear regression modelling
  4. Students should have interest in building on the previous concepts to learn which regression models are applicable under different circumstances
  5. Students should have an interest in learning the machine learning based regression models in R

Curriculum

  • Introduction to the Course: What Will We Cover?
    06:59
  • What is the Difference Between Statistical Analysis & Machine Learning?
    05:36
  • Download & Install R & RStudio
    06:36
  • Read in Data from Different Sources in R
    15:29
  • Data Cleaning in R
    17:12
  • More Data Cleaning in R
    08:05
  • Exploratory Data Analysis (EDA)
    18:54
  • Conclusion to Section 1
    01:59
  • OLS Regression-Theory
    10:45
  • OLS-Implementation
    08:40
  • More on Results Interpretation
    07:46
  • Confidence Interval-Theory
    06:06
  • Confidence Interval Calculation in R
    04:54
  • CI and OLS Regression
    07:20
  • Linear Regression Without Intercept
    03:41
  • Implement ANOVA on OLS Regression
    03:38
  • Multiple Linear Regression
    06:27
  • Multiple linear Regression with Interaction Terms
    15:06
  • Some Basic Conditions that OLS Models Need to Satisfy
    12:57
  • Conclusions to Section 2
    02:56
  • Identify Multicollinearity
    16:43
  • Doing Regression Analyses with Correlated Predictor Variables
    05:37
  • Principal Component Regression
    10:39
  • Partial Least Square Regression
    07:33
  • Ridge Regression
    07:22
  • LASSO Regression
    04:25
  • Conclusion to Section 3
    02:00
  • Why Do Any Kind of Selection?
    04:41
  • Select the Most Suitable OLS Regression
    13:19
  • Select Model Subsets
    08:23
  • Machine Learning Perspective on Accuracy Evaluation
    07:10
  • Evaluate Regression Model Accuracy
    14:26
  • LASSO Regression for Variable Selection
    03:42
  • Identify the Contribution of Predictors in Explaining the Variation in Y
    08:39
  • Conclusions to Section 4
    01:36
  • Theory of Generalized Linear Models (GLMs)
    05:25
  • Logistic Regression
    16:19
  • Logistic Regression for Binary Response Variable
    09:10
  • Multinomial Regression
    06:12
  • Regression for Count Data
    06:20
  • Generalized Additive Model
    14:09
  • Boosted GAM
    06:16
  • MARS Regression
    08:07
  • CART-Regression Tree
    10:54
  • Conditional Inference Trees
    05:46
  • Random Forests (RF)
    11:52
  • Gradient Boosting Machine (GBM)
    04:11

About the Author

Minerva Singh, Data Scientist

Hello. I am a PhD graduate from Cambridge University where I specialized in Tropical Ecology. I am also a Data Scientist on the side. As a part of my research I have to carry out extensive data analysis, including spatial data analysis.or this purpose I prefer to use a combination of freeware tools- R, QGIS and Python.I do most of my spatial data analysis work using R and QGIS.

Apart from being free, these are very powerful tools for data visualization, processing and analysis. I also hold an MPhil degree in Geography and Environment from Oxford University. I have honed my statistical and data analysis skills through a number of MOOCs including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R based Machine Learning course offered by Standford online).

In addition to spatial data analysis, I am also proficient in statistical analysis, machine learning and data mining. I also enjoy general programming, data visualization and web development. In addition to being a scientist and number cruncher, I am an avid traveler.

More From Author

Regression Analysis for Statistics & Machine Learning in R

  • 06:32:01
  • 47
  • 5
  • Language: English
  • 15 days Money back Gurantee
  • Unlimited Access
  • Android, iPhone and iPad Access
  • Certificate of Completion

Course Summary

With so many R Statistics & Machine Learning courses around, why  enroll for this ?<

Read More

Target Audience

  1. People with basic knowledge of R based statistical modelling
  2. People with knowledge of linear regression modelling
  3. People wanting to extend their knowledge of regression modelling for solving real world problems.
  4. People wanting to learn how to apply machine learning based regression models using R
  5. Undergraduates and postgraduates seeking to deepen their knowledge of statistical and machine learning analysis
  6. Academic researchers seeking to learn new techniques for data analysis
  7. Business data analysts who wish to use regression modelling for predictive analysis

Pre-Requisites

  1. People with basic knowledge of R based statistical modelling
  2. People with knowledge of linear regression modelling
  3. People wanting to extend their knowledge of regression modelling for solving real world problems.
  4. People wanting to learn how to apply machine learning based regression models using R
  5. Undergraduates and postgraduates seeking to deepen their knowledge of statistical and machine learning analysis
  6. Academic researchers seeking to learn new techniques for data analysis
  7. Business data analysts who wish to use regression modelling for predictive analysis

About the Author

Minerva Singh, Data Scientist

Hello. I am a PhD graduate from Cambridge University where I specialized in Tropical Ecology. I am also a Data Scientist on the side. As a part of my research I have to carry out extensive data analysis, including spatial data analysis.or this purpose I prefer to use a combination of freeware tools- R, QGIS and Python.I do most of my spatial data analysis work using R and QGIS.

Apart from being free, these are very powerful tools for data visualization, processing and analysis. I also hold an MPhil degree in Geography and Environment from Oxford University. I have honed my statistical and data analysis skills through a number of MOOCs including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R based Machine Learning course offered by Standford online).

In addition to spatial data analysis, I am also proficient in statistical analysis, machine learning and data mining. I also enjoy general programming, data visualization and web development. In addition to being a scientist and number cruncher, I am an avid traveler.

More From Author