R - Complete Machine Learning Solutions

Are you interested in understanding machine learning concepts and building real-time projects with R, but don’t know where to start? Then, this is the perfect course for you!

The aim of machine learning is to uncover hidden patterns, unknown correlations, and find useful information from data. In addition to this, through incorporation with data analysis, machine learning can be used to perform predictive analysis. With machine learning, the analysis of business operations and processes is not limited to human scale thinking; machine scale analysis enables businesses to capture hidden values in big data.

Machine learning has similarities to the human reasoning process. Unlike traditional analysis, the generated model cannot evolve as data is accumulated. Machine learning can learn from the data that is processed and analyzed. In other words, the more data that is processed, the more it can learn.

R, as a dialect of GNU-S, is a powerful statistical language that can be used to manipulate and analyze data. Additionally, R provides many machine learning packages and visualization functions, which enable users to analyze data on the fly. Most importantly, R is open source and free.

Using R greatly simplifies machine learning. All you need to know is how each algorithm can solve your problem, and then you can simply use a written package to quickly generate prediction models on data with a few command lines.

By taking this course, you will gain a detailed and practical knowledge of R and machine learning concepts to build complex machine learning models.

What details do you cover in this course?

We start off with basic R operations, reading data into R, manipulating data, forming simple statistics for visualizing data. We will then walk through the processes of transforming, analyzing, and visualizing the RMS Titanic data. You will also learn how to perform descriptive statistics.

This course will teach you to use regression models. We will then see how to fit data in tree-based classifier, Naive Bayes classifier, and so on.

We then move on to introducing powerful classification networks, neural networks, and support vector machines. During this journey, we will introduce the power of ensemble learners to produce better classification and regression results.

We will see how to apply the clustering technique to segment customers and further compare differences between each clustering method.

We will go through the process of compressing and restoring images, using the dimension reduction approach and R Hadoop, starting from setting up the environment to actual big data processing and machine learning on big data.

By the end of this course, we will build our own project in the e-commerce domain.

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Learning Outcomes

Create and inspect the transaction dataset and perform association analysis with the Apriori algorithm

Predict possible churn users with the classification approach

Implement the clustering method to segment customer data

Compress images with the dimension reduction method

Build a product recommendation system

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Pre-requisite

No prior knowledge of R is required

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Who is this course intended for?

If you are interested in understanding machine learning concepts and building real-time projects with R, then this is the perfect course for you!