Data Science:Data Mining & Natural Language Processing in R

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  • Study type: Online
  • Starts: Anytime
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Course Description

MASTER DATA SCIENCE, TEXT MINING AND NATURAL LANGUAGE PROCESSING IN R:

Learn to carry out pre-processing, visualization and machine learning tasks such as: clustering, classification and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge.

LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE:

My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).
I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning.
This gives students an incomplete knowledge of the subject. Unlike other courses out there, we are not going to stop at machine learning. We will also cover data mining, web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data.

NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED:

You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R.
My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use packages like caret, dplyr to work with real data in R. You will also learn to use the common NLP packages to extract insights from text data.
I will even introduce you to some very important practical case studies - such as detecting loan repayment and tumor detection using machine learning. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful All-In-One R Data Science course, you’ll know it all: visualization, stats, machine learning, data mining, and neural networks!

The underlying motivation for the course is to ensure you can apply R based data science on real data into practice today. Start analyzing data for your own projects, whatever your skill level and Impress your potential employers with actual examples of your data science projects.

HERE IS WHAT YOU WILL GET:

(a) This course will take you from a basic level to performing some of the most common advanced data science techniques using the powerful R based tools.

(b) Equip you to use R to perform the different exploratory and visualization tasks for data modelling.

(c) Introduce you to some of the most important machine learning concepts in a practical manner such that you can apply these concepts for practical data analysis and interpretation. (d) You will get a strong understanding of some of the most important data mining, text mining and natural language processing techniques.

(e) & You will be able to decide which data science techniques are best suited to answer your research questions and applicable to your data and interpret the results.

More Specifically, here's what's covered in the course:

  • Getting started with R, R Studio and Rattle for implementing different data science techniques

  • Data Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data.

  • How to Pre-Process and “Wrangle” your R data by removing NAs/No data, handling conditional data, grouping by attributes..etc

  • Creating data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, and MORE

  • Statistical analysis, statistical inference, and the relationships between variables.

  • Machine Learning, Supervised Learning, & Unsupervised Learning in R

  • Neural Networks for Classification and Regression

  • Web-Scraping using R

  • Extracting text data from Twitter and Facebook using APIs

  • Text mining

  • Common Natural Language Processing techniques such as sentiment analysis and topic modelling

We will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results.

After each video you will learn a new concept or technique which you may apply to your own projects.

All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE.

JOIN THE COURSE NOW!


Who this course is for:

  • Students wishing to learn practical data science and machine learning in R
  • Students wishing to learn the underlying theory and application of data mining in R
  • Students interested in obtaining/mining data from sources such as Twiter
  • Students interested in pre-processing and visualizing real life data
  • Students wishing to analyze and derive insights from text data
  • Students interested in learning basic text mining and Natural Language Processing (NLP) in R

Instructor

Bestselling Instructor & Data Scientist(Cambridge Uni)
  • 4.4 Instructor Rating
  • 14,036 Reviews
  • 73,733 Students
  • 42 Courses

I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics.

I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC).

Expected Outcomes

  1. Perform the most important pre-processing tasks needed prior to machine learning in R Carry out data visualization in R Use machine learning for unsupervised classification in R Carry out supervised learning by building classification and regression models in R Evaluate the accuracy of supervised machine learning algorithms and compare their performance in R Carry out sentiment analysis using text data in R Curated for the Udemy Business collection Course content 15 sections • 110 lectures • 13h 6m total length Expand all sections INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools 6 lectures • 20min Introduction Preview 04:58 Data and Scripts For the Course 00:04 Introduction to R and RStudio 06:36 Start with Rattle Preview 06:30 Troubleshooting For Rattle 00:10 Conclusion to Section 1 Preview 01:34 Reading in Data from Different Sources in R 8 lectures • 43min Read in Data from CSV and Excel Files 09:56 Read Data from a Database Preview 08:23 Read Data from JSON 05:28 Read in Data from Online CSVs 04:04 Read in Data from Online HTML Tables-Part 1 04:13 Read in Data from Online HTML Tables-Part 2 06:24 Read Data from Other Sources 02:13 Conclusions to Section 2 Preview 02:20 Exploratory Data Analysis and Data Visualization in R 15 lectures • 2hr 16min Remove NAs 17:12 More Data Cleaning 08:05 Exploratory Data Analysis(EDA): Basic Visualizations with R 18:53 More Exploratory Data Analysis with xda 04:16 Introduction to dplyr for Data Summarizing-Part 1 06:11 Introduction to dplyr for Data Summarizing-Part 2 04:44 Data Exploration & Visualization With dplyr & ggplot2 06:07 Pre-Processing Dates-Part 1 07:33 Pre-Processing Dates-Part 2 08:28 Plotting Temporal Data in R 12:35 Twist in the (Temporal) Data 08:56 Associations Between Quantitative Variables- Theory 03:43 Testing for Correlation 19:50 Evaluate the Relation Between Nominal Variables 06:14 Cramer's V for Examining the Strength of Association Between Nominal Variable 03:35 Section 3 Quiz 2 questions Data Mining for Patterns and Relationships 6 lectures • 37min What is Data Mining? Preview 04:09 Association Mining with Apriori 12:20 Apriori with Real Data 05:34 Visualize the Rules 04:55 Association Mining with Eclat 06:11 Eclat with Real Data 03:57 Machine Learning for Data Science 2 lectures • 11min How is Machine Learning Different from Statistical Data Analysis? Preview 05:36 What is Machine Learning (ML) About? Some Theoretical Pointers Preview 05:32 Unsupervised Classification- R 7 lectures • 1hr 4min K-means Clustering 14:31 Fuzzy K-Means Clustering 18:14 Weighted K-Means Clustering 06:04 Hierarchical Clustering in R 14:13 Expectation-Maximization (EM) in R 05:50 Use Rattle for Unsupervised Clustering 03:48 Conclusions to Section 6 Preview 01:45 Section 6 Quiz 3 questions Dimension Reduction 7 lectures • 57min Dimensionality Reduction-theory Preview 03:17 PCA 13:10 Removing Highly Correlated Predictor Variables 16:42 Variable Selection Using LASSO Regression 03:42 Variable Selection With FSelector 13:35 Boruta Analysis for Feature Selection 04:51 Conclusions to Section 7 Preview 01:39 Section 7 Quiz 3 questions Supervised Learning Theory 2 lectures • 14min Some Basic Supervised Learning Concepts Preview 10:10 Pre-processing for Supervised Learning 03:31 Supervised Learning: Classification 17 lectures • 1hr 58min Binary Classification Preview 00:09 What are GLMs? 05:25 Logistic Regression Models as Binary Classifiers 09:10 Linear Discriminant Analysis (LDA) 12:55 Binary Classifier with PCA 15:44 Obtain Binary Classification Accuracy Metrics 08:18 Multi-class Classification Models Preview 00:08 Our Multi-class Classification Problem 06:13 Classification Trees 11:55 More on Classification Tree Visualization 09:20 Decision Trees 08:39 Random Forest (RF) classification 08:15 Examine Individual Variable Importance for Random Forests 03:53 GBM Classification 07:50 Support Vector Machines (SVM) for Classification 03:55 More SVM for Classification 03:42 Conclusions to Section 9 Preview 01:59 Section 9 Quiz 3 questions Supervised Learning: Regression 10 lectures • 1hr 14min Ridge Regression in R 07:22 LASSO Regression in R 04:24 Generalized Additive Models (GAMs) in R 14:09 Boosted GAMs 06:15 MARS Regression 08:06 CART-Regression Trees in R 10:54 Random Forest (RF) Regression 11:52 GBM Regression 04:10 Compare Models 05:31 Conclusions to Section 10 Preview 01:45 5 more sections Requirements Keen interest in learning about data science and data mining Keen interest in mining and deriving insights from text data Should have prior experience of using R and RStudio Should be able to install and read in packages in R Prior exposure to the principles of statistical data analysis , data visualization and summarizing in R will be beneficial but not necessary Description MASTER DATA SCIENCE, TEXT MINING AND NATURAL LANGUAGE PROCESSING IN R: Learn to carry out pre-processing, visualization and machine learning tasks such as: clustering, classification and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge. LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE: My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning. This gives students an incomplete knowledge of the subject. Unlike other courses out there, we are not going to stop at machine learning. We will also cover data mining, web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data. NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED: You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use packages like caret, dplyr to work with real data in R. You will also learn to use the common NLP packages to extract insights from text data. I will even introduce you to some very important practical case studies - such as detecting loan repayment and tumor detection using machine learning. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful All-In-One R Data Science course, you’ll know it all: visualization, stats, machine learning, data mining, and neural networks! The underlying motivation for the course is to ensure you can apply R based data science on real data into practice today. Start analyzing data for your own projects, whatever your skill level and Impress your potential employers with actual examples of your data science projects. HERE IS WHAT YOU WILL GET: (a) This course will take you from a basic level to performing some of the most common advanced data science techniques using the powerful R based tools. (b) Equip you to use R to perform the different exploratory and visualization tasks for data modelling. (c) Introduce you to some of the most important machine learning concepts in a practical manner such that you can apply these concepts for practical data analysis and interpretation. (d) You will get a strong understanding of some of the most important data mining, text mining and natural language processing techniques. (e) & You will be able to decide which data science techniques are best suited to answer your research questions and applicable to your data and interpret the results. More Specifically, here's what's covered in the course: Getting started with R, R Studio and Rattle for implementing different data science techniques Data Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data. How to Pre-Process and “Wrangle” your R data by removing NAs/No data, handling conditional data, grouping by attributes..etc Creating data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, and MORE Statistical analysis, statistical inference, and the relationships between variables. Machine Learning, Supervised Learning, & Unsupervised Learning in R Neural Networks for Classification and Regression Web-Scraping using R Extracting text data from Twitter and Facebook using APIs Text mining Common Natural Language Processing techniques such as sentiment analysis and topic modelling We will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects. All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE. JOIN THE COURSE NOW! Who this course is for: Students wishing to learn practical data science and machine learning in R Students wishing to learn the underlying theory and application of data mining in R Students interested in obtaining/mining data from sources such as Twiter Students interested in pre-processing and visualizing real life data Students wishing to analyze and derive insights from text data Students interested in learning basic text mining and Natural Language Processing (NLP) in R Show more Show less Instructor Minerva Singh Bestselling Instructor & Data Scientist(Cambridge Uni) 4.4 Instructor Rating 14,036 Reviews 73,733 Students 42 Courses I completed a PhD ( University of Cambridge, UK ) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC). Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'6777c86ecf5a541b',m:'169e9f1a503e6e47ba74cb998fa8e208c1b6f9d8-1627744536-1800-AW3Hupvf4JrtVWV7MFWxUFSNqMtqSDNnKnPdwGPF6v0aC5Fuh+NbRxQC63XX1QyIhRPRamRR02tRdOW0BfiuHHy6lwAJi5WGI8PD6q0bRfN6Dtq8Wwsv7Q8uGHNxtbrAXUdKIkYzRZ5CovzYgfO+w5oBVfp9n7LESzquhQsByNOB3CnARkd0+R8Q4TTUr3wo4H2D0CgQc5HgQzDIKYSMDfw=',s:[0xbc2e598cb7,0xf9c3210951],}})();
  2. Carry out data visualization in R Use machine learning for unsupervised classification in R Carry out supervised learning by building classification and regression models in R Evaluate the accuracy of supervised machine learning algorithms and compare their performance in R Carry out sentiment analysis using text data in R Curated for the Udemy Business collection Course content 15 sections • 110 lectures • 13h 6m total length Expand all sections INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools 6 lectures • 20min Introduction Preview 04:58 Data and Scripts For the Course 00:04 Introduction to R and RStudio 06:36 Start with Rattle Preview 06:30 Troubleshooting For Rattle 00:10 Conclusion to Section 1 Preview 01:34 Reading in Data from Different Sources in R 8 lectures • 43min Read in Data from CSV and Excel Files 09:56 Read Data from a Database Preview 08:23 Read Data from JSON 05:28 Read in Data from Online CSVs 04:04 Read in Data from Online HTML Tables-Part 1 04:13 Read in Data from Online HTML Tables-Part 2 06:24 Read Data from Other Sources 02:13 Conclusions to Section 2 Preview 02:20 Exploratory Data Analysis and Data Visualization in R 15 lectures • 2hr 16min Remove NAs 17:12 More Data Cleaning 08:05 Exploratory Data Analysis(EDA): Basic Visualizations with R 18:53 More Exploratory Data Analysis with xda 04:16 Introduction to dplyr for Data Summarizing-Part 1 06:11 Introduction to dplyr for Data Summarizing-Part 2 04:44 Data Exploration & Visualization With dplyr & ggplot2 06:07 Pre-Processing Dates-Part 1 07:33 Pre-Processing Dates-Part 2 08:28 Plotting Temporal Data in R 12:35 Twist in the (Temporal) Data 08:56 Associations Between Quantitative Variables- Theory 03:43 Testing for Correlation 19:50 Evaluate the Relation Between Nominal Variables 06:14 Cramer's V for Examining the Strength of Association Between Nominal Variable 03:35 Section 3 Quiz 2 questions Data Mining for Patterns and Relationships 6 lectures • 37min What is Data Mining? Preview 04:09 Association Mining with Apriori 12:20 Apriori with Real Data 05:34 Visualize the Rules 04:55 Association Mining with Eclat 06:11 Eclat with Real Data 03:57 Machine Learning for Data Science 2 lectures • 11min How is Machine Learning Different from Statistical Data Analysis? Preview 05:36 What is Machine Learning (ML) About? Some Theoretical Pointers Preview 05:32 Unsupervised Classification- R 7 lectures • 1hr 4min K-means Clustering 14:31 Fuzzy K-Means Clustering 18:14 Weighted K-Means Clustering 06:04 Hierarchical Clustering in R 14:13 Expectation-Maximization (EM) in R 05:50 Use Rattle for Unsupervised Clustering 03:48 Conclusions to Section 6 Preview 01:45 Section 6 Quiz 3 questions Dimension Reduction 7 lectures • 57min Dimensionality Reduction-theory Preview 03:17 PCA 13:10 Removing Highly Correlated Predictor Variables 16:42 Variable Selection Using LASSO Regression 03:42 Variable Selection With FSelector 13:35 Boruta Analysis for Feature Selection 04:51 Conclusions to Section 7 Preview 01:39 Section 7 Quiz 3 questions Supervised Learning Theory 2 lectures • 14min Some Basic Supervised Learning Concepts Preview 10:10 Pre-processing for Supervised Learning 03:31 Supervised Learning: Classification 17 lectures • 1hr 58min Binary Classification Preview 00:09 What are GLMs? 05:25 Logistic Regression Models as Binary Classifiers 09:10 Linear Discriminant Analysis (LDA) 12:55 Binary Classifier with PCA 15:44 Obtain Binary Classification Accuracy Metrics 08:18 Multi-class Classification Models Preview 00:08 Our Multi-class Classification Problem 06:13 Classification Trees 11:55 More on Classification Tree Visualization 09:20 Decision Trees 08:39 Random Forest (RF) classification 08:15 Examine Individual Variable Importance for Random Forests 03:53 GBM Classification 07:50 Support Vector Machines (SVM) for Classification 03:55 More SVM for Classification 03:42 Conclusions to Section 9 Preview 01:59 Section 9 Quiz 3 questions Supervised Learning: Regression 10 lectures • 1hr 14min Ridge Regression in R 07:22 LASSO Regression in R 04:24 Generalized Additive Models (GAMs) in R 14:09 Boosted GAMs 06:15 MARS Regression 08:06 CART-Regression Trees in R 10:54 Random Forest (RF) Regression 11:52 GBM Regression 04:10 Compare Models 05:31 Conclusions to Section 10 Preview 01:45 5 more sections Requirements Keen interest in learning about data science and data mining Keen interest in mining and deriving insights from text data Should have prior experience of using R and RStudio Should be able to install and read in packages in R Prior exposure to the principles of statistical data analysis , data visualization and summarizing in R will be beneficial but not necessary Description MASTER DATA SCIENCE, TEXT MINING AND NATURAL LANGUAGE PROCESSING IN R: Learn to carry out pre-processing, visualization and machine learning tasks such as: clustering, classification and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge. LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE: My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning. This gives students an incomplete knowledge of the subject. Unlike other courses out there, we are not going to stop at machine learning. We will also cover data mining, web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data. NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED: You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use packages like caret, dplyr to work with real data in R. You will also learn to use the common NLP packages to extract insights from text data. I will even introduce you to some very important practical case studies - such as detecting loan repayment and tumor detection using machine learning. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful All-In-One R Data Science course, you’ll know it all: visualization, stats, machine learning, data mining, and neural networks! The underlying motivation for the course is to ensure you can apply R based data science on real data into practice today. Start analyzing data for your own projects, whatever your skill level and Impress your potential employers with actual examples of your data science projects. HERE IS WHAT YOU WILL GET: (a) This course will take you from a basic level to performing some of the most common advanced data science techniques using the powerful R based tools. (b) Equip you to use R to perform the different exploratory and visualization tasks for data modelling. (c) Introduce you to some of the most important machine learning concepts in a practical manner such that you can apply these concepts for practical data analysis and interpretation. (d) You will get a strong understanding of some of the most important data mining, text mining and natural language processing techniques. (e) & You will be able to decide which data science techniques are best suited to answer your research questions and applicable to your data and interpret the results. More Specifically, here's what's covered in the course: Getting started with R, R Studio and Rattle for implementing different data science techniques Data Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data. How to Pre-Process and “Wrangle” your R data by removing NAs/No data, handling conditional data, grouping by attributes..etc Creating data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, and MORE Statistical analysis, statistical inference, and the relationships between variables. Machine Learning, Supervised Learning, & Unsupervised Learning in R Neural Networks for Classification and Regression Web-Scraping using R Extracting text data from Twitter and Facebook using APIs Text mining Common Natural Language Processing techniques such as sentiment analysis and topic modelling We will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects. All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE. JOIN THE COURSE NOW! Who this course is for: Students wishing to learn practical data science and machine learning in R Students wishing to learn the underlying theory and application of data mining in R Students interested in obtaining/mining data from sources such as Twiter Students interested in pre-processing and visualizing real life data Students wishing to analyze and derive insights from text data Students interested in learning basic text mining and Natural Language Processing (NLP) in R Show more Show less Instructor Minerva Singh Bestselling Instructor & Data Scientist(Cambridge Uni) 4.4 Instructor Rating 14,036 Reviews 73,733 Students 42 Courses I completed a PhD ( University of Cambridge, UK ) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC). Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'6777c86ecf5a541b',m:'169e9f1a503e6e47ba74cb998fa8e208c1b6f9d8-1627744536-1800-AW3Hupvf4JrtVWV7MFWxUFSNqMtqSDNnKnPdwGPF6v0aC5Fuh+NbRxQC63XX1QyIhRPRamRR02tRdOW0BfiuHHy6lwAJi5WGI8PD6q0bRfN6Dtq8Wwsv7Q8uGHNxtbrAXUdKIkYzRZ5CovzYgfO+w5oBVfp9n7LESzquhQsByNOB3CnARkd0+R8Q4TTUr3wo4H2D0CgQc5HgQzDIKYSMDfw=',s:[0xbc2e598cb7,0xf9c3210951],}})();
  3. Use machine learning for unsupervised classification in R Carry out supervised learning by building classification and regression models in R Evaluate the accuracy of supervised machine learning algorithms and compare their performance in R Carry out sentiment analysis using text data in R Curated for the Udemy Business collection Course content 15 sections • 110 lectures • 13h 6m total length Expand all sections INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools 6 lectures • 20min Introduction Preview 04:58 Data and Scripts For the Course 00:04 Introduction to R and RStudio 06:36 Start with Rattle Preview 06:30 Troubleshooting For Rattle 00:10 Conclusion to Section 1 Preview 01:34 Reading in Data from Different Sources in R 8 lectures • 43min Read in Data from CSV and Excel Files 09:56 Read Data from a Database Preview 08:23 Read Data from JSON 05:28 Read in Data from Online CSVs 04:04 Read in Data from Online HTML Tables-Part 1 04:13 Read in Data from Online HTML Tables-Part 2 06:24 Read Data from Other Sources 02:13 Conclusions to Section 2 Preview 02:20 Exploratory Data Analysis and Data Visualization in R 15 lectures • 2hr 16min Remove NAs 17:12 More Data Cleaning 08:05 Exploratory Data Analysis(EDA): Basic Visualizations with R 18:53 More Exploratory Data Analysis with xda 04:16 Introduction to dplyr for Data Summarizing-Part 1 06:11 Introduction to dplyr for Data Summarizing-Part 2 04:44 Data Exploration & Visualization With dplyr & ggplot2 06:07 Pre-Processing Dates-Part 1 07:33 Pre-Processing Dates-Part 2 08:28 Plotting Temporal Data in R 12:35 Twist in the (Temporal) Data 08:56 Associations Between Quantitative Variables- Theory 03:43 Testing for Correlation 19:50 Evaluate the Relation Between Nominal Variables 06:14 Cramer's V for Examining the Strength of Association Between Nominal Variable 03:35 Section 3 Quiz 2 questions Data Mining for Patterns and Relationships 6 lectures • 37min What is Data Mining? Preview 04:09 Association Mining with Apriori 12:20 Apriori with Real Data 05:34 Visualize the Rules 04:55 Association Mining with Eclat 06:11 Eclat with Real Data 03:57 Machine Learning for Data Science 2 lectures • 11min How is Machine Learning Different from Statistical Data Analysis? Preview 05:36 What is Machine Learning (ML) About? Some Theoretical Pointers Preview 05:32 Unsupervised Classification- R 7 lectures • 1hr 4min K-means Clustering 14:31 Fuzzy K-Means Clustering 18:14 Weighted K-Means Clustering 06:04 Hierarchical Clustering in R 14:13 Expectation-Maximization (EM) in R 05:50 Use Rattle for Unsupervised Clustering 03:48 Conclusions to Section 6 Preview 01:45 Section 6 Quiz 3 questions Dimension Reduction 7 lectures • 57min Dimensionality Reduction-theory Preview 03:17 PCA 13:10 Removing Highly Correlated Predictor Variables 16:42 Variable Selection Using LASSO Regression 03:42 Variable Selection With FSelector 13:35 Boruta Analysis for Feature Selection 04:51 Conclusions to Section 7 Preview 01:39 Section 7 Quiz 3 questions Supervised Learning Theory 2 lectures • 14min Some Basic Supervised Learning Concepts Preview 10:10 Pre-processing for Supervised Learning 03:31 Supervised Learning: Classification 17 lectures • 1hr 58min Binary Classification Preview 00:09 What are GLMs? 05:25 Logistic Regression Models as Binary Classifiers 09:10 Linear Discriminant Analysis (LDA) 12:55 Binary Classifier with PCA 15:44 Obtain Binary Classification Accuracy Metrics 08:18 Multi-class Classification Models Preview 00:08 Our Multi-class Classification Problem 06:13 Classification Trees 11:55 More on Classification Tree Visualization 09:20 Decision Trees 08:39 Random Forest (RF) classification 08:15 Examine Individual Variable Importance for Random Forests 03:53 GBM Classification 07:50 Support Vector Machines (SVM) for Classification 03:55 More SVM for Classification 03:42 Conclusions to Section 9 Preview 01:59 Section 9 Quiz 3 questions Supervised Learning: Regression 10 lectures • 1hr 14min Ridge Regression in R 07:22 LASSO Regression in R 04:24 Generalized Additive Models (GAMs) in R 14:09 Boosted GAMs 06:15 MARS Regression 08:06 CART-Regression Trees in R 10:54 Random Forest (RF) Regression 11:52 GBM Regression 04:10 Compare Models 05:31 Conclusions to Section 10 Preview 01:45 5 more sections Requirements Keen interest in learning about data science and data mining Keen interest in mining and deriving insights from text data Should have prior experience of using R and RStudio Should be able to install and read in packages in R Prior exposure to the principles of statistical data analysis , data visualization and summarizing in R will be beneficial but not necessary Description MASTER DATA SCIENCE, TEXT MINING AND NATURAL LANGUAGE PROCESSING IN R: Learn to carry out pre-processing, visualization and machine learning tasks such as: clustering, classification and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge. LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE: My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning. This gives students an incomplete knowledge of the subject. Unlike other courses out there, we are not going to stop at machine learning. We will also cover data mining, web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data. NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED: You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use packages like caret, dplyr to work with real data in R. You will also learn to use the common NLP packages to extract insights from text data. I will even introduce you to some very important practical case studies - such as detecting loan repayment and tumor detection using machine learning. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful All-In-One R Data Science course, you’ll know it all: visualization, stats, machine learning, data mining, and neural networks! The underlying motivation for the course is to ensure you can apply R based data science on real data into practice today. Start analyzing data for your own projects, whatever your skill level and Impress your potential employers with actual examples of your data science projects. HERE IS WHAT YOU WILL GET: (a) This course will take you from a basic level to performing some of the most common advanced data science techniques using the powerful R based tools. (b) Equip you to use R to perform the different exploratory and visualization tasks for data modelling. (c) Introduce you to some of the most important machine learning concepts in a practical manner such that you can apply these concepts for practical data analysis and interpretation. (d) You will get a strong understanding of some of the most important data mining, text mining and natural language processing techniques. (e) & You will be able to decide which data science techniques are best suited to answer your research questions and applicable to your data and interpret the results. More Specifically, here's what's covered in the course: Getting started with R, R Studio and Rattle for implementing different data science techniques Data Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data. How to Pre-Process and “Wrangle” your R data by removing NAs/No data, handling conditional data, grouping by attributes..etc Creating data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, and MORE Statistical analysis, statistical inference, and the relationships between variables. Machine Learning, Supervised Learning, & Unsupervised Learning in R Neural Networks for Classification and Regression Web-Scraping using R Extracting text data from Twitter and Facebook using APIs Text mining Common Natural Language Processing techniques such as sentiment analysis and topic modelling We will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects. All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE. JOIN THE COURSE NOW! Who this course is for: Students wishing to learn practical data science and machine learning in R Students wishing to learn the underlying theory and application of data mining in R Students interested in obtaining/mining data from sources such as Twiter Students interested in pre-processing and visualizing real life data Students wishing to analyze and derive insights from text data Students interested in learning basic text mining and Natural Language Processing (NLP) in R Show more Show less Instructor Minerva Singh Bestselling Instructor & Data Scientist(Cambridge Uni) 4.4 Instructor Rating 14,036 Reviews 73,733 Students 42 Courses I completed a PhD ( University of Cambridge, UK ) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC). Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? 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  4. Carry out supervised learning by building classification and regression models in R Evaluate the accuracy of supervised machine learning algorithms and compare their performance in R Carry out sentiment analysis using text data in R Curated for the Udemy Business collection Course content 15 sections • 110 lectures • 13h 6m total length Expand all sections INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools 6 lectures • 20min Introduction Preview 04:58 Data and Scripts For the Course 00:04 Introduction to R and RStudio 06:36 Start with Rattle Preview 06:30 Troubleshooting For Rattle 00:10 Conclusion to Section 1 Preview 01:34 Reading in Data from Different Sources in R 8 lectures • 43min Read in Data from CSV and Excel Files 09:56 Read Data from a Database Preview 08:23 Read Data from JSON 05:28 Read in Data from Online CSVs 04:04 Read in Data from Online HTML Tables-Part 1 04:13 Read in Data from Online HTML Tables-Part 2 06:24 Read Data from Other Sources 02:13 Conclusions to Section 2 Preview 02:20 Exploratory Data Analysis and Data Visualization in R 15 lectures • 2hr 16min Remove NAs 17:12 More Data Cleaning 08:05 Exploratory Data Analysis(EDA): Basic Visualizations with R 18:53 More Exploratory Data Analysis with xda 04:16 Introduction to dplyr for Data Summarizing-Part 1 06:11 Introduction to dplyr for Data Summarizing-Part 2 04:44 Data Exploration & Visualization With dplyr & ggplot2 06:07 Pre-Processing Dates-Part 1 07:33 Pre-Processing Dates-Part 2 08:28 Plotting Temporal Data in R 12:35 Twist in the (Temporal) Data 08:56 Associations Between Quantitative Variables- Theory 03:43 Testing for Correlation 19:50 Evaluate the Relation Between Nominal Variables 06:14 Cramer's V for Examining the Strength of Association Between Nominal Variable 03:35 Section 3 Quiz 2 questions Data Mining for Patterns and Relationships 6 lectures • 37min What is Data Mining? Preview 04:09 Association Mining with Apriori 12:20 Apriori with Real Data 05:34 Visualize the Rules 04:55 Association Mining with Eclat 06:11 Eclat with Real Data 03:57 Machine Learning for Data Science 2 lectures • 11min How is Machine Learning Different from Statistical Data Analysis? Preview 05:36 What is Machine Learning (ML) About? Some Theoretical Pointers Preview 05:32 Unsupervised Classification- R 7 lectures • 1hr 4min K-means Clustering 14:31 Fuzzy K-Means Clustering 18:14 Weighted K-Means Clustering 06:04 Hierarchical Clustering in R 14:13 Expectation-Maximization (EM) in R 05:50 Use Rattle for Unsupervised Clustering 03:48 Conclusions to Section 6 Preview 01:45 Section 6 Quiz 3 questions Dimension Reduction 7 lectures • 57min Dimensionality Reduction-theory Preview 03:17 PCA 13:10 Removing Highly Correlated Predictor Variables 16:42 Variable Selection Using LASSO Regression 03:42 Variable Selection With FSelector 13:35 Boruta Analysis for Feature Selection 04:51 Conclusions to Section 7 Preview 01:39 Section 7 Quiz 3 questions Supervised Learning Theory 2 lectures • 14min Some Basic Supervised Learning Concepts Preview 10:10 Pre-processing for Supervised Learning 03:31 Supervised Learning: Classification 17 lectures • 1hr 58min Binary Classification Preview 00:09 What are GLMs? 05:25 Logistic Regression Models as Binary Classifiers 09:10 Linear Discriminant Analysis (LDA) 12:55 Binary Classifier with PCA 15:44 Obtain Binary Classification Accuracy Metrics 08:18 Multi-class Classification Models Preview 00:08 Our Multi-class Classification Problem 06:13 Classification Trees 11:55 More on Classification Tree Visualization 09:20 Decision Trees 08:39 Random Forest (RF) classification 08:15 Examine Individual Variable Importance for Random Forests 03:53 GBM Classification 07:50 Support Vector Machines (SVM) for Classification 03:55 More SVM for Classification 03:42 Conclusions to Section 9 Preview 01:59 Section 9 Quiz 3 questions Supervised Learning: Regression 10 lectures • 1hr 14min Ridge Regression in R 07:22 LASSO Regression in R 04:24 Generalized Additive Models (GAMs) in R 14:09 Boosted GAMs 06:15 MARS Regression 08:06 CART-Regression Trees in R 10:54 Random Forest (RF) Regression 11:52 GBM Regression 04:10 Compare Models 05:31 Conclusions to Section 10 Preview 01:45 5 more sections Requirements Keen interest in learning about data science and data mining Keen interest in mining and deriving insights from text data Should have prior experience of using R and RStudio Should be able to install and read in packages in R Prior exposure to the principles of statistical data analysis , data visualization and summarizing in R will be beneficial but not necessary Description MASTER DATA SCIENCE, TEXT MINING AND NATURAL LANGUAGE PROCESSING IN R: Learn to carry out pre-processing, visualization and machine learning tasks such as: clustering, classification and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge. LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE: My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning. This gives students an incomplete knowledge of the subject. Unlike other courses out there, we are not going to stop at machine learning. We will also cover data mining, web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data. NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED: You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use packages like caret, dplyr to work with real data in R. You will also learn to use the common NLP packages to extract insights from text data. I will even introduce you to some very important practical case studies - such as detecting loan repayment and tumor detection using machine learning. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful All-In-One R Data Science course, you’ll know it all: visualization, stats, machine learning, data mining, and neural networks! The underlying motivation for the course is to ensure you can apply R based data science on real data into practice today. Start analyzing data for your own projects, whatever your skill level and Impress your potential employers with actual examples of your data science projects. HERE IS WHAT YOU WILL GET: (a) This course will take you from a basic level to performing some of the most common advanced data science techniques using the powerful R based tools. (b) Equip you to use R to perform the different exploratory and visualization tasks for data modelling. (c) Introduce you to some of the most important machine learning concepts in a practical manner such that you can apply these concepts for practical data analysis and interpretation. (d) You will get a strong understanding of some of the most important data mining, text mining and natural language processing techniques. (e) & You will be able to decide which data science techniques are best suited to answer your research questions and applicable to your data and interpret the results. More Specifically, here's what's covered in the course: Getting started with R, R Studio and Rattle for implementing different data science techniques Data Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data. How to Pre-Process and “Wrangle” your R data by removing NAs/No data, handling conditional data, grouping by attributes..etc Creating data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, and MORE Statistical analysis, statistical inference, and the relationships between variables. Machine Learning, Supervised Learning, & Unsupervised Learning in R Neural Networks for Classification and Regression Web-Scraping using R Extracting text data from Twitter and Facebook using APIs Text mining Common Natural Language Processing techniques such as sentiment analysis and topic modelling We will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects. All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE. JOIN THE COURSE NOW! Who this course is for: Students wishing to learn practical data science and machine learning in R Students wishing to learn the underlying theory and application of data mining in R Students interested in obtaining/mining data from sources such as Twiter Students interested in pre-processing and visualizing real life data Students wishing to analyze and derive insights from text data Students interested in learning basic text mining and Natural Language Processing (NLP) in R Show more Show less Instructor Minerva Singh Bestselling Instructor & Data Scientist(Cambridge Uni) 4.4 Instructor Rating 14,036 Reviews 73,733 Students 42 Courses I completed a PhD ( University of Cambridge, UK ) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC). Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'6777c86ecf5a541b',m:'169e9f1a503e6e47ba74cb998fa8e208c1b6f9d8-1627744536-1800-AW3Hupvf4JrtVWV7MFWxUFSNqMtqSDNnKnPdwGPF6v0aC5Fuh+NbRxQC63XX1QyIhRPRamRR02tRdOW0BfiuHHy6lwAJi5WGI8PD6q0bRfN6Dtq8Wwsv7Q8uGHNxtbrAXUdKIkYzRZ5CovzYgfO+w5oBVfp9n7LESzquhQsByNOB3CnARkd0+R8Q4TTUr3wo4H2D0CgQc5HgQzDIKYSMDfw=',s:[0xbc2e598cb7,0xf9c3210951],}})();
  5. Evaluate the accuracy of supervised machine learning algorithms and compare their performance in R Carry out sentiment analysis using text data in R Curated for the Udemy Business collection Course content 15 sections • 110 lectures • 13h 6m total length Expand all sections INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools 6 lectures • 20min Introduction Preview 04:58 Data and Scripts For the Course 00:04 Introduction to R and RStudio 06:36 Start with Rattle Preview 06:30 Troubleshooting For Rattle 00:10 Conclusion to Section 1 Preview 01:34 Reading in Data from Different Sources in R 8 lectures • 43min Read in Data from CSV and Excel Files 09:56 Read Data from a Database Preview 08:23 Read Data from JSON 05:28 Read in Data from Online CSVs 04:04 Read in Data from Online HTML Tables-Part 1 04:13 Read in Data from Online HTML Tables-Part 2 06:24 Read Data from Other Sources 02:13 Conclusions to Section 2 Preview 02:20 Exploratory Data Analysis and Data Visualization in R 15 lectures • 2hr 16min Remove NAs 17:12 More Data Cleaning 08:05 Exploratory Data Analysis(EDA): Basic Visualizations with R 18:53 More Exploratory Data Analysis with xda 04:16 Introduction to dplyr for Data Summarizing-Part 1 06:11 Introduction to dplyr for Data Summarizing-Part 2 04:44 Data Exploration & Visualization With dplyr & ggplot2 06:07 Pre-Processing Dates-Part 1 07:33 Pre-Processing Dates-Part 2 08:28 Plotting Temporal Data in R 12:35 Twist in the (Temporal) Data 08:56 Associations Between Quantitative Variables- Theory 03:43 Testing for Correlation 19:50 Evaluate the Relation Between Nominal Variables 06:14 Cramer's V for Examining the Strength of Association Between Nominal Variable 03:35 Section 3 Quiz 2 questions Data Mining for Patterns and Relationships 6 lectures • 37min What is Data Mining? Preview 04:09 Association Mining with Apriori 12:20 Apriori with Real Data 05:34 Visualize the Rules 04:55 Association Mining with Eclat 06:11 Eclat with Real Data 03:57 Machine Learning for Data Science 2 lectures • 11min How is Machine Learning Different from Statistical Data Analysis? Preview 05:36 What is Machine Learning (ML) About? Some Theoretical Pointers Preview 05:32 Unsupervised Classification- R 7 lectures • 1hr 4min K-means Clustering 14:31 Fuzzy K-Means Clustering 18:14 Weighted K-Means Clustering 06:04 Hierarchical Clustering in R 14:13 Expectation-Maximization (EM) in R 05:50 Use Rattle for Unsupervised Clustering 03:48 Conclusions to Section 6 Preview 01:45 Section 6 Quiz 3 questions Dimension Reduction 7 lectures • 57min Dimensionality Reduction-theory Preview 03:17 PCA 13:10 Removing Highly Correlated Predictor Variables 16:42 Variable Selection Using LASSO Regression 03:42 Variable Selection With FSelector 13:35 Boruta Analysis for Feature Selection 04:51 Conclusions to Section 7 Preview 01:39 Section 7 Quiz 3 questions Supervised Learning Theory 2 lectures • 14min Some Basic Supervised Learning Concepts Preview 10:10 Pre-processing for Supervised Learning 03:31 Supervised Learning: Classification 17 lectures • 1hr 58min Binary Classification Preview 00:09 What are GLMs? 05:25 Logistic Regression Models as Binary Classifiers 09:10 Linear Discriminant Analysis (LDA) 12:55 Binary Classifier with PCA 15:44 Obtain Binary Classification Accuracy Metrics 08:18 Multi-class Classification Models Preview 00:08 Our Multi-class Classification Problem 06:13 Classification Trees 11:55 More on Classification Tree Visualization 09:20 Decision Trees 08:39 Random Forest (RF) classification 08:15 Examine Individual Variable Importance for Random Forests 03:53 GBM Classification 07:50 Support Vector Machines (SVM) for Classification 03:55 More SVM for Classification 03:42 Conclusions to Section 9 Preview 01:59 Section 9 Quiz 3 questions Supervised Learning: Regression 10 lectures • 1hr 14min Ridge Regression in R 07:22 LASSO Regression in R 04:24 Generalized Additive Models (GAMs) in R 14:09 Boosted GAMs 06:15 MARS Regression 08:06 CART-Regression Trees in R 10:54 Random Forest (RF) Regression 11:52 GBM Regression 04:10 Compare Models 05:31 Conclusions to Section 10 Preview 01:45 5 more sections Requirements Keen interest in learning about data science and data mining Keen interest in mining and deriving insights from text data Should have prior experience of using R and RStudio Should be able to install and read in packages in R Prior exposure to the principles of statistical data analysis , data visualization and summarizing in R will be beneficial but not necessary Description MASTER DATA SCIENCE, TEXT MINING AND NATURAL LANGUAGE PROCESSING IN R: Learn to carry out pre-processing, visualization and machine learning tasks such as: clustering, classification and regression in R. You will be able to mine insights from text data and Twitter to give yourself & your company a competitive edge. LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE: My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably with machine learning. This gives students an incomplete knowledge of the subject. Unlike other courses out there, we are not going to stop at machine learning. We will also cover data mining, web-scraping, text mining and natural language processing along with mining social media sites like Twitter and Facebook for text data. NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED: You’ll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real life. After taking this course, you’ll easily use packages like caret, dplyr to work with real data in R. You will also learn to use the common NLP packages to extract insights from text data. I will even introduce you to some very important practical case studies - such as detecting loan repayment and tumor detection using machine learning. You will also extract tweets pertaining to trending topics and analyze their underlying sentiments and identify topics with Latent Dirichlet allocation. With this Powerful All-In-One R Data Science course, you’ll know it all: visualization, stats, machine learning, data mining, and neural networks! The underlying motivation for the course is to ensure you can apply R based data science on real data into practice today. Start analyzing data for your own projects, whatever your skill level and Impress your potential employers with actual examples of your data science projects. HERE IS WHAT YOU WILL GET: (a) This course will take you from a basic level to performing some of the most common advanced data science techniques using the powerful R based tools. (b) Equip you to use R to perform the different exploratory and visualization tasks for data modelling. (c) Introduce you to some of the most important machine learning concepts in a practical manner such that you can apply these concepts for practical data analysis and interpretation. (d) You will get a strong understanding of some of the most important data mining, text mining and natural language processing techniques. (e) & You will be able to decide which data science techniques are best suited to answer your research questions and applicable to your data and interpret the results. More Specifically, here's what's covered in the course: Getting started with R, R Studio and Rattle for implementing different data science techniques Data Structures and Reading in Pandas, including CSV, Excel, JSON, HTML data. How to Pre-Process and “Wrangle” your R data by removing NAs/No data, handling conditional data, grouping by attributes..etc Creating data visualizations like histograms, boxplots, scatterplots, barplots, pie/line charts, and MORE Statistical analysis, statistical inference, and the relationships between variables. Machine Learning, Supervised Learning, & Unsupervised Learning in R Neural Networks for Classification and Regression Web-Scraping using R Extracting text data from Twitter and Facebook using APIs Text mining Common Natural Language Processing techniques such as sentiment analysis and topic modelling We will spend some time dealing with some of the theoretical concepts related to data science. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects. All the data and code used in the course has been made available free of charge and you can use it as you like. You will also have access to additional lectures that are added in the future for FREE. JOIN THE COURSE NOW! Who this course is for: Students wishing to learn practical data science and machine learning in R Students wishing to learn the underlying theory and application of data mining in R Students interested in obtaining/mining data from sources such as Twiter Students interested in pre-processing and visualizing real life data Students wishing to analyze and derive insights from text data Students interested in learning basic text mining and Natural Language Processing (NLP) in R Show more Show less Instructor Minerva Singh Bestselling Instructor & Data Scientist(Cambridge Uni) 4.4 Instructor Rating 14,036 Reviews 73,733 Students 42 Courses I completed a PhD ( University of Cambridge, UK ) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning co