# Complete course for Data Science and Machine Learning with R

Complete course for Data Science and Machine Learning with R

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*Complete course for Data Science and Machine Learning with R From beginner to expert in Data Science techniques with R: machine learning, neural networks, text mining*

What you'll learn

- Review of the bases of R and its data structures
- Programming environments for Data Science
- Importing datasets into R
- Graph creation and dataset exploration
- Manipulation and management of datasets
- Preprocessing and cleaning of data for analysis
- Introduction to machine learning with R
- Machine learning theory and algorithms, supervised and unsupervised methods
- Ensemble methods: bagging, boosting
- Validation and evaluation of models
- Text cleaning and analysis
- Methods for Sentiment Analysis

Requirements

- Basic knowledge of R

Description

This course on Data Science with R was created to be a complete path on how data analysis has evolved in recent years starting from classical algebra and statistics. The goal is to accompany a student who has some basic R on a journey through the various souls of Data Science.

We will start with a review of the basics of R, starting with downloading and installing, setting up the work environment, going through structures, creating functions, using operators and some important functions.

We will then move on to see how to manipulate and manage a dataset, extract cases or variables, generate random datasets, calculate basic statistical measures, create graphs with the Matplotlib and Seaborn packages.

In the following sections we begin to enter the heart of Data Science with R, starting with preprocessing: we see how to clean up and normalize a dataset, and how to manage missing data.

The next section allows us to start setting up machine learning models with Python: we will see all the most common algorithms, both supervised and unsupervised, such as regression, simple, multiple and logistic, the k-nearest neighbors, the Support Vector Machines , Naive Bayes, decision trees and clustering.

We will then move on to the most common ensemble methods, such as Random Forest, Bagging and Boosting, and to the analysis of natural language and its use in machine learning for cataloging texts.

In the last sections we will see some rudiments of temporal analysis, recommendation systems and social media mining.

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