# Machine Learning and Statistical Modeling with R Examples

Machine Learning and Statistical Modeling with R Examples

Machine Learning and Statistical Modeling with R Examples An educational resource for those seeking knowledge related to machine learning and statistical computing in R. Here, you will find quality articles, with working R code and examples, where, the goal is to make the rstats concepts clear and as simple as possible.

What you'll learn

- understand the most common principles of machine learning and statistical modeling
- perform machine learning tasks in R
- understand which machine learning tool is suitable for a given problem
- know what machine learning can do
- implement machine learning and statistical modeling in your work

Requirements

- You need a solid foundation in R
- You need a good understanding of general statistics
- You should be interested in machine learning and modeling

Description

**See things in your data that no one else can see – and make the right decisions!**

Due
to modern technology and the internet, the amount of available data
grows substantially from day to day. Successful companies know that. And
they also know that seeing the patterns in the data gives them an edge
on increasingly competitive markets. Proper understanding and training
in **Machine Learning** and **Statistical Modeling** will give you the power to identify those patterns. This can make you an invaluable asset for your company/institution and can* boost your career*!

*Marketing companies* use Machine Learning to identify potential customers and how to best present products.

*Scientists*
use Machine Learning to capture new insights in nearly any given field
ranging from psychology to physics and computer sciences.

*IT companies* use Machine Learning to create new search tools or cutting edge mobile apps.

*Insurance companies, banks and investment funds* use Machine Learning to make the right financial decisions or even use it for algorithmic trading.

*Consulting companies* use Machine Learning to help their customers on decision making.

Artificial intelligence would not be possible without those modeling tools.

Basically we already live in a world that is heavily influenced by Machine Learning algorithms.

1. But what exactly is Machine Learning?

Machine
learning is a collection of modern statistical methods for various
applications. Those methods have one thing in common: they try to create
a model based on underlying (training) data to predict outcomes on new
data you feed into the model. A test dataset is used to see how accurate
the model works. Basically Machine learning is the same as **Statistical Modeling**.

2. Is it hard to understand and learn those methods?

Unfortunately the learning materials about Machine Learning tend to be quite technical and need tons of prior knowledge to be understood.

With this course it is my main goal to make understanding those tools as** intuitive** and **simple** as possible.

While you need some knowledge in statistics and statistical programming, the course is meant for people without a major in a quantitative field like math or statistics. Basically anybody dealing with data on a regular basis can benefit from this course.

3. How is the course structured?

For a better learning success, each section has a* theory part*, a* practice part* where I will show you an example in R and at last every section is enforced with ** exercises.** You can download the

**code pdf**of every section to try the presented code on your own.

4. So how do I prepare best to benefit from that course?

It depends on your prior knowledge. But as a rule of thumb you should know how to handle standard tasks in R (courses *R Basics* and *R Level 1*). You should also know the basics of modeling and statistics and how to implement that in R (*Statistics in R* course).

For special offers and combinations just check out the **r-tutorials webpage** which you can find below the instructor profile.

What R you waiting for?

Martin

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