Data Science:Data Mining & Natural Language Processing in R

Data Science:Data Mining & Natural Language Processing in R, available at $79.99, has an average rating of 4.5, with 112 lectures, 5 quizzes, based on 409 reviews, and has 4456 subscribers.

You will learn about 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 This course is ideal for individuals who are Students wishing to learn practical data science and machine learning in R or Students wishing to learn the underlying theory and application of data mining in R or Students interested in obtaining/mining data from sources such as Twiter or Students interested in pre-processing and visualizing real life data or Students wishing to analyze and derive insights from text data or Students interested in learning basic text mining and Natural Language Processing (NLP) in R It is particularly useful for Students wishing to learn practical data science and machine learning in R or Students wishing to learn the underlying theory and application of data mining in R or Students interested in obtaining/mining data from sources such as Twiter or Students interested in pre-processing and visualizing real life data or Students wishing to analyze and derive insights from text data or Students interested in learning basic text mining and Natural Language Processing (NLP) in R.

Enroll now: Data Science:Data Mining & Natural Language Processing in R

Summary

Title: Data Science:Data Mining & Natural Language Processing in R

Price: $79.99

Average Rating: 4.5

Number of Lectures: 112

Number of Quizzes: 5

Number of Published Lectures: 112

Number of Published Quizzes: 5

Number of Curriculum Items: 117

Number of Published Curriculum Objects: 117

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

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

Target Audiences

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

�

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!

Course Curriculum

Chapter 1: INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools

Lecture 1: Introduction

Lecture 2: Data and Scripts For the Course

Lecture 3: Introduction to R and RStudio

Lecture 4: Start with Rattle

Lecture 5: Troubleshooting For Rattle

Lecture 6: Conclusion to Section 1

Chapter 2: Reading in Data from Different Sources in R

Lecture 1: Read in Data from CSV and Excel Files

Lecture 2: Read Data from a Database

Lecture 3: Read Data from JSON

Lecture 4: Read in Data from Online CSVs

Lecture 5: Read in Data from Online HTML Tables-Part 1

Lecture 6: Read in Data from Online HTML Tables-Part 2

Lecture 7: Read Data from Other Sources

Lecture 8: Conclusions to Section 2

Chapter 3: Exploratory Data Analysis and Data Visualization in R

Lecture 1: Remove NAs

Lecture 2: More Data Cleaning

Lecture 3: Exploratory Data Analysis(EDA): Basic Visualizations with R

Lecture 4: More Exploratory Data Analysis with xda

Lecture 5: Introduction to dplyr for Data Summarizing-Part 1

Lecture 6: Introduction to dplyr for Data Summarizing-Part 2

Lecture 7: Data Exploration & Visualization With dplyr & ggplot2

Lecture 8: Pre-Processing Dates-Part 1

Lecture 9: Pre-Processing Dates-Part 2

Lecture 10: Plotting Temporal Data in R

Lecture 11: Twist in the (Temporal) Data

Lecture 12: Associations Between Quantitative Variables- Theory

Lecture 13: Testing for Correlation

Lecture 14: Evaluate the Relation Between Nominal Variables

Lecture 15: Cramer's V for Examining the Strength of Association Between Nominal Variable

Chapter 4: Data Mining for Patterns and Relationships

Lecture 1: What is Data Mining?

Lecture 2: Association Mining with Apriori

Lecture 3: Apriori with Real Data

Lecture 4: Visualize the Rules

Lecture 5: Association Mining with Eclat

Lecture 6: Eclat with Real Data

Chapter 5: Machine Learning for Data Science

Lecture 1: How is Machine Learning Different from Statistical Data Analysis?

Lecture 2: What is Machine Learning (ML) About? Some Theoretical Pointers

Chapter 6: Unsupervised Classification- R

Lecture 1: K-means Clustering

Lecture 2: Fuzzy K-Means Clustering

Lecture 3: Weighted K-Means Clustering

Lecture 4: Hierarchical Clustering in R

Lecture 5: Expectation-Maximization (EM) in R

Lecture 6: Use Rattle for Unsupervised Clustering

Lecture 7: Conclusions to Section 6

Chapter 7: Dimension Reduction

Lecture 1: Dimensionality Reduction-theory

Lecture 2: PCA

Lecture 3: Removing Highly Correlated Predictor Variables

Lecture 4: Variable Selection Using LASSO Regression

Lecture 5: Variable Selection With FSelector

Lecture 6: Boruta Analysis for Feature Selection

Lecture 7: Conclusions to Section 7

Chapter 8: Supervised Learning Theory

Lecture 1: Some Basic Supervised Learning Concepts

Lecture 2: Pre-processing for Supervised Learning

Chapter 9: Supervised Learning: Classification

Lecture 1: Binary Classification

Lecture 2: What are GLMs?

Lecture 3: Logistic Regression Models as Binary Classifiers

Lecture 4: Linear Discriminant Analysis (LDA)

Lecture 5: Binary Classifier with PCA

Lecture 6: Obtain Binary Classification Accuracy Metrics

Lecture 7: Multi-class Classification Models

Lecture 8: Our Multi-class Classification Problem

Lecture 9: Classification Trees

Lecture 10: More on Classification Tree Visualization

Lecture 11: Decision Trees

Lecture 12: Random Forest (RF) classification

Lecture 13: Examine Individual Variable Importance for Random Forests

Lecture 14: GBM Classification

Lecture 15: Support Vector Machines (SVM) for Classification

Lecture 16: More SVM for Classification

Lecture 17: Conclusions to Section 9

Chapter 10: Supervised Learning: Regression

Lecture 1: Ridge Regression in R

Lecture 2: LASSO Regression in R

Lecture 3: Generalized Additive Models (GAMs) in R

Lecture 4: Boosted GAMs

Lecture 5: MARS Regression

Lecture 6: CART-Regression Trees in R

Lecture 7: Random Forest (RF) Regression

Lecture 8: GBM Regression

Lecture 9: Compare Models

Lecture 10: Conclusions to Section 10

Chapter 11: Introduction to Artificial Neural Networks (ANN)

Lecture 1: What are Artificial Neural Networks?

Lecture 2: Neural Network for Binary Classifications

Lecture 3: Neural Network with PCA for Binary Classifications

Lecture 4: Neural Network for Regression

Lecture 5: More on Neural Networks- with neuralnet

Instructors

Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)

Rating Distribution

1 stars: 19 votes
2 stars: 17 votes
3 stars: 42 votes
4 stars: 82 votes
5 stars: 249 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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