R Applied Data Science: Statistics and Machine Learning

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Data Science in R: Statistics and Machine Learning Training is Waiting for You!

In this training, prepared in detail for programming, statistics and machine learning techniques for data science in R, a lot of information you will need in your business life and school life is waiting for you. This training, which consists of 50 hours and more than 300 course content, in which all techniques are explained in detail, is prepared on R Studio for those who want to get training from beginner to advanced level. In our training, we will learn many statistics and machine learning techniques both theoretically and practically using different data sets and different R packages!

Topics covered in this tutorial;

  • Statistical techniques in R are both theoretical and applied

  • Both theoretical and applied machine learning techniques in R

  • R Programming for Data Science

  • Different data structures and data types defined in R

  • Mathematics and descriptive statistics

  • Data manipulation

  • Algorithm and functional programming

  • Data visualization techniques

  • Probability distributions

  • Parametric and nonparametric statistics tests

  • Analysis of variance

  • Correlation tests

  • Loss and outlier checks

  • Missing observation filling techniques

  • Conversion actions on data

  • Normalization and standardization processes

  • Regularization processes

  • Model verification and model tuning processes

  • Different Machine Learning techniques for Regression, Classification and Cluster problems

At the end of this training, in which almost all subjects were discussed within the scope of statistics and machine learning; You will be able to master data science, statistics and machine learning. In addition, you will be able to follow the solutions of the problems you have experienced in the subjects described within the scope of the training in the Questions From You section.

We hope it will be a successful, effective and useful training process…

Instructors: Sergen Cansiz

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