We use cookies and similar technology on this website, which helps us to know a little bit about you and how you use our website. This improves the browsing experience for you and enables us to tailor better products and services to you and others. Cookies are stored locally on your computer or mobile device.

To accept cookies continue browsing as normal or go to the Cookies Notice for more information and to set your preferences.

You have to add this courses into your profile.

Data Science:Data Mining & Natural Language Processing in R

- |
- Lectures:
**104** - |
- Videos:
**13 hours** - |
- Level:
**All** - |
- Language:
**English** - |
- Last Updated:
**03/2018**

- Instructor : Eduonix Learning Solutions

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.

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

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, youâ€™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, youâ€™ll know it all: visualization, stats, machine learning, data mining, and neural networks!

**HERE IS WHAT YOU WILL GET:**

- 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.
- Equip you to use R to perform the different exploratory and visualization tasks for data modelling.
- 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.
- You will get a strong understanding of some of the most important data mining, text mining and natural language processing techniques.
- & 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.

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

1 | Introduction | ||

2 | Data and Scripts For the Course | ||

3 | Introduction to R and RStudio | ||

4 | Start with Rattle | ||

5 | Conclusion to Section 1 |

6 | Read in Data from CSV and Excel Files | ||

7 | Read Data from a Database | ||

8 | Read Data from JSON | ||

9 | Read in Data from Online CSVs | ||

10 | Read in Data from Online HTML Tables-Part 1 | ||

11 | Read in Data from Online HTML Tables-Part 2 | ||

12 | Read Data from Other Sources | ||

13 | Conclusions to Section 2 |

29 | What is Data Mining? | ||

30 | Association Mining with Apriori | ||

31 | Apriori with Real Data | ||

32 | Visualize the Rules | ||

33 | Association Mining with Eclat | ||

34 | Eclat with Real Data |

37 | K-means Clustering | ||

38 | Fuzzy K-Means Clustering | ||

39 | Weighted K-Means Clustering | ||

40 | Hierarchical Clustering in R | ||

41 | Expectation-Maximization (EM) in R | ||

42 | Use Rattle for Unsupervised Clustering | ||

43 | Conclusions to Section 6 |