Practical course of Machine Learning with R

Practical course of Machine Learning with R
.MP4 | Video: 1280×720, 30 fps(r) | Audio: AAC, 44100 Hz, 2ch | 5.94 GB
Duration: 11.5 hours | Genre: eLearning Video | Language: English
Start your career as a Data Scientist by learning to build regression, classification and clustering models.

What you’ll learn
You will be able to realize Machine Learning models in total autonomy, both for classification and regression and clustering problems, both in supervised and unsupervised cases
You will know how to use Linear Regression algorithms (simple, multiple and non-linear), Logistic Regression, LDA (Linear Discriminant Analysis), QDA (Discriminant Quadratic Analysis), KNN (K-Nearest Neighbors) and K-Means
You will learn to evaluate the results of a Machine Learning model
You will know how to choose the most appropriate Machine Learning model for the case in question
You will learn to use the R programming language in RStudio

requirements

A computer on which to install R and RStudio (free)
Propensity to algorithmic thinking
Basic knowledge of descriptive statistics (concepts of average, standard deviation, …) and mathematical notation (summation, use of indices, …)
A minimum programming experience (in any language) will help you progress more quickly

Description

This course has only one focus: to enable the use of Machine Learning in R.

Everything therefore orbits around the goal of allowing students to realize their Machine Learning models independently, using R. To achieve this result many tutorials have been included, where all the steps are performed one at a time. At the same time there are theoretical sessions that allow us to understand the principles behind the various algorithms or strategies.

With this course you will learn the principles behind Machine Learning, the most common algorithms and R commands to be able to create models both for Regression problems, Classification problems, and Clustering problems.

What often distinguishes a mediocre Data Scientist from an excellent one is his ability to evaluate and choose the best models. For this reason specific techniques will be taught and put into practice during the course to do this.

Overall we will present and use 8 different algorithms, you can follow more than 11 hours of video divided into over 120 lessons. You will also have almost 300 pages of slides available in pdf format that you can download by section and consult at any time. Also the source code of the R scripts that we will carry out during the course will be at your disposal, and you can download it and use it in your R console. Finally, to facilitate learning, I have created some special Quizzes at the end of the Section. Thanks to the Quizzes you will be able to remember more easily what you have studied during the Section and therefore learn more and better;)

With this course you will learn the concrete skills you need to apply Machine Learning to real problems.

I hope to see you soon in the course!

Luca-

Who this course is for:

Who wants to become a Data Scientist, and needs to develop the Machine Learning part
Junior Data Scientists who want to strengthen themselves in Machine Learning and R
Anyone who wants to create Machine Learning systems, even without becoming a Data Scientist
Who wants to understand the principles of Machine Learning in a practical and concrete way
Who wants to learn how to use R to do Machine Learning

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