Data Science:Data Mining & Natural Language Processing in R course will teach students how to use R for machine learning tasks like clustering, classification, and regression as well as pre-processing and visualisation. Students will get a competitive advantage for themselves and to their business, Students will also be able to extract insights from text data and Twitter.
The course gives an in-depth knowledge to Text Mining, Natural Language Processing, and Data Science in R. Students are thus given a limited understanding of the subject. In contrast to other courses, the course doesn’t stop at machine learning but also teaches about text data mining on social media platforms like Twitter and Facebook, The course also talk about data mining, web scraping, text mining, and natural language processing. Students doesn’t require any previous knowledge to statistics or machine learning. The courses are usually available at INR 3,499 on Udemy but you can click now to get 87% off and get Data Science:Data Mining & Natural Language Processing in R for INR 449.
Who all can opt for this course?
- Students looking to get experience using R for practical data science and machine learning
- Students who want to master the fundamental principles and practical uses of data mining in R
- Students who are interested in collecting or mining data from Twitter, for example
- Students who are interested in preparing and displaying actual data
- students who want to examine and draw conclusions from textual information
- Students who want to understand the fundamentals of R’s Natural Language Processing (NLP)
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 13 Hours |
| Rating | 5.0/5 |
| Student Enrollment | 3,932 students |
| Instructor | Minerva Singh https://www.linkedin.com/in/minervasingh |
| Topics Covered | Text Mining, Data Minining, Machine Learning, Natural Language Processing, R Programming, and Data Science |
| Course Level | Beginner |
| Total Student Reviews | 367 |
Learning Outcomes
- Run R to complete the most crucial pre-processing tasks required before machine learning
- Apply R to data visualisation
- For unsupervised classification in R, use machine learning
- Create R models for classification and regression to do supervised learning
- Analyze and contrast the performance of supervised machine learning algorithms in R
- Use R to perform sentiment analysis on text data
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools (19 minutes) | Introduction |
| Data and Scripts For the Course | ||
| Introduction to R and RStudio | ||
| Start with Rattle | ||
| Troubleshooting For Rattle | ||
| Conclusion to Section 1 | ||
| 2. | Reading in Data from Different Sources in R (43 minutes) | Read in Data from CSV and Excel Files |
| Read Data from a Database | ||
| Read Data from JSON | ||
| Read in Data from Online CSVs | ||
| Read in Data from Online HTML Tables-Part 1 | ||
| Read in Data from Online HTML Tables-Part 2 | ||
| Read Data from Other Sources | ||
| Conclusions to Section 2 | ||
| 3. | Exploratory Data Analysis and Data Visualization in R (02 hours 16 minutes) | Remove NAs |
| More Data Cleaning | ||
| Exploratory Data Analysis(EDA): Basic Visualizations with R | ||
| More Exploratory Data Analysis with xda | ||
| Introduction to dplyr for Data Summarizing-Part 1 | ||
| Introduction to dplyr for Data Summarizing-Part 2 | ||
| Data Exploration & Visualization With dplyr & ggplot2 | ||
| Pre-Processing Dates-Part 1 | ||
| Pre-Processing Dates-Part 2 | ||
| Plotting Temporal Data in R | ||
| Twist in the (Temporal) Data | ||
| Associations Between Quantitative Variables- Theory | ||
| Testing for Correlation | ||
| Evaluate the Relation Between Nominal Variables | ||
| Cramer’s V for Examining the Strength of Association Between Nominal Variable | ||
| Section 3 Quiz | ||
| 4. | Data Mining for Patterns and Relationships (37 minutes) | What is Data Mining? |
| Association Mining with Apriori | ||
| Apriori with Real Data | ||
| Visualize the Rules | ||
| Association Mining with Eclat | ||
| Eclat with Real Data | ||
| 5. | Machine Learning for Data Science (11 minutes) | How is Machine Learning Different from Statistical Data Analysis? |
| What is Machine Learning (ML) About? Some Theoretical Pointers | ||
| 6. | Unsupervised Classification- R (01 hour 04 minutes) | K-means Clustering |
| Fuzzy K-Means Clustering | ||
| Weighted K-Means Clustering | ||
| Hierarchical Clustering in R | ||
| Expectation-Maximization (EM) in R | ||
| Use Rattle for Unsupervised Clustering | ||
| Conclusions to Section 6 | ||
| Section 6 Quiz | ||
| 7. | Dimension Reduction (56 minutes) | Dimensionality Reduction-theory |
| PCA | ||
| Removing Highly Correlated Predictor Variables | ||
| Variable Selection Using LASSO Regression | ||
| Variable Selection With FSelector | ||
| Boruta Analysis for Feature Selection | ||
| Conclusions to Section 7 | ||
| Section 7 Quiz | ||
| 8. | Supervised Learning Theory (13 minutes) | Some Basic Supervised Learning Concepts |
| Pre-processing for Supervised Learning | ||
| 9. | Supervised Learning: Classification (01 hour 57 minutes) | Binary Classification |
| What are GLMs? | ||
| Logistic Regression Models as Binary Classifiers | ||
| Linear Discriminant Analysis (LDA) | ||
| Binary Classifier with PCA | ||
| Obtain Binary Classification Accuracy Metrics | ||
| Multi-class Classification Models | ||
| Our Multi-class Classification Problem | ||
| Classification Trees | ||
| More on Classification Tree Visualization | ||
| Decision Trees | ||
| Random Forest (RF) classification | ||
| Examine Individual Variable Importance for Random Forests | ||
| GBM Classification | ||
| Support Vector Machines (SVM) for Classification | ||
| More SVM for Classification | ||
| Conclusions to Section 9 | ||
| Section 9 Quiz | ||
| 10. | Supervised Learning: Regression (01 hour 14 minutes) | Ridge Regression in R |
| LASSO Regression in R | ||
| Generalized Additive Models (GAMs) in R | ||
| Boosted GAMs | ||
| MARS Regression | ||
| CART-Regression Trees in R | ||
| Random Forest (RF) Regression | ||
| GBM Regression | ||
| Compare Models | ||
| Conclusions to Section 10 | ||
| 11. | Introduction to Artificial Neural Networks (ANN) (43 minutes) | What are Artificial Neural Networks? |
| Neural Network for Binary Classifications | ||
| Neural Network with PCA for Binary Classifications | ||
| Neural Network for Regression | ||
| More on Neural Networks- with neuralnet | ||
| Identify Variable Importance in Neural Networks | ||
| 12. | More Web-scraping and Text Data Mining (45 minutes) | Read in Text Data from an HTML Page |
| Explore Amazon with R | ||
| More Webscraping With rvest-IMDB Webpage | ||
| Prior to Mining Data from Twitter | ||
| Extract Tweets Using R | ||
| More Twitter Data Extraction Using R | ||
| Get Data from Facebook Using R | ||
| Conclusions to Section 12 | ||
| 13. | Gaining Insights from Text Data- Text Mining and Natural Language Processing (NL (01 hour 28 minutes) | Explore Tweet Data |
| Visualize Tweet Sentiment Wordcloud- India’s Demonetization Policy | ||
| More Wordclouds: Amazon Review Data | ||
| Word Frequency in Text Data | ||
| Tweet Sentiments- India’s Demonetization Policy | ||
| Sentiment Analysis of Mugabe Tweets | ||
| Tweet Sentiments- Mugabe’s Ouster | ||
| Examine the Polarity of Text | ||
| Polarity of Individual Tweets | ||
| Topic Modelling a Document | ||
| Topic Modelling Multiple Documents | ||
| Conclusions to Section 13 | ||
| 14. | Text Data and Machine Learning (24 minutes) | EDA With Text Data |
| Identify Deceptive Reviews With Supervised Classification | ||
| Identify Spam Emails with Supervised Classification | ||
| 15. | Miscellaneous Lectures (19 minutes) | 3D Scatterplots |
| Getting Acquainted with Github Desktop | ||
| Data Editing Within R | ||
| Group By Time |
Resources Required
- A strong desire to learn more about data mining and data science
- Keen interest in extracting insights from text data and text mining
- Should have previous R and RStudio experience
- In R, packages ought to be able to be installed and read
- It will be advantageous but not required to have prior knowledge of the fundamentals of statistical data analysis, data visualisation, and summarization in R
Featured Review
Anonymized U. (5/5) : The course is beyond my expectations. It will enhance the quality of my work several notches.
Pros
- Kitara Beily (5/5) : Exclusive one! This guy is best presenter of tech topics I’ve ever seen online.
- Anonymized User (5/5) : The concept has been brilliantly explained by the instructor with practical examples.
- Salma Rabiia (5/5) : It was a great course where I learned a lot about data science.
- Sita Kimic (5/5) : This course is great if you want to grasp the concept of data science .
Cons
- O. L. (1.5/5) : The software does not install, outdated info
- Mauricio L. (1.5/5): The instructutor does not provide the necesaary guides as other courses about how to install correctly the proper material for the course
- Taskyn R. (1.5/5) : Beginning was very good. Bit when it comes to supervised learning it become mess.Also Author dosenot writes code online.Somecases not executes functions,which was written.
- Prashant B. (1/5) : It is not taught properly. what kind of method are used, why it is used,no proper explanation. disappointing
About the Author
The instructor of this course is Minerva Singh who is a Bestselling Instructor & Data Scientist(Cambridge Uni). With 4.4 Instructor Rating and 18,466 Reviews on Udemy, he/she offers 50 Courses and has taught 93,130 Students so far.
- The instructor finished his Doctorate in 2017 at the University of Cambridge in the UK, where he concentrated on using data science tools to calculate the effects of forest loss on tropical ecosystems
- Instructor graduated from Oxford University with an MPhil in the School of Geography and Environment and an MSc in the Department of Engineering
- Instructor have more than ten years of experience conducting academic research (published in prestigious international peer-reviewed scientific journals like PLOS One) and offering advice to non-governmental and business stakeholders on matters relating to data science, deep learning, and earth observation (EO)
- Instructor have a proven track record of using R and Python to accomplish machine learning, data visualisation, spatial data analysis, deep learning, and NLP applications
- Instructor have developed my statistical and data analysis skills through a variety of MOOCs, including The Analytics Edge (an R-based statistics and machine learning course offered by EdX), Statistical Learning (an R-based Machine Learning course offered by Stanford online), and the IBM Data Science Professional certificate Track
- Instructor have also received his education from some of the best universities in the world
- Instructor have a wide range of specialties, including deep learning (Tensorflow, Keras), machine learning, spatial data analysis (including processing EO data), data visualisation, natural language processing, and financial analysis, among others
- Instructor have served as a peer reviewer for esteemed academic publications like Remote Sensing and delivered guest talks at illustrious gatherings like the Open Data Science Conference (ODSC).
Comparison Table
| Parameters | Data Science:Data Mining & Natural Language Processing in R | Regression Analysis for Statistics & Machine Learning in R | Practical Neural Networks & Deep Learning In R |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 13.5 hours | 7.5 hours | 5.5 hours |
| Rating | 5.0 /5 | 4.9 /5 | 4.4 /5 |
| Student Enrollments | 3,932 | 4,408 | 1,770 |
| Instructors | Minerva Singh | Minerva Singh | Minerva Singh |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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