Machine Learning Using R

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    Machine Learning Using R

    This course,  Machine Learning using R, dives into the basics of machine learning using an approachable, and well-known, programming language. Therefore, it looks at real-life examples of Machine learning. And, how it affects society in ways you may not have guessed!

    Explore many algorithms and models:

    • Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
    • Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.

    R Course Contents

    Probably, R is the most  powerful language for data analysis, data visualization, machine learning and statistics. It was originally developed for statistical programming.  Furthermore, it is now one of the most popular languages in data science.

    Therefore, you’ll be learning about the basics of R, and you’ll end with the confidence to start writing your own R scripts. But this isn’t your typical textbook introduction to R. While you’re not just learning about R fundamentals, you’ll be using R to solve problems related to movies data as well. Using a concrete example makes the learning painless. You will learn about the fundamentals of R syntax, including assigning variables and doing simple operations with one of R’s most important data structures — vectors! From vectors, you’ll then learn about lists, matrix, arrays and data frames. Then you’ll jump into conditional statements, functions, classes and debugging. Once you’ve covered the basics – you’ll learn about reading and writing data in R, whether it’s a table format (CSV, Excel) or a text file (.txt). Finally, you’ll end with some important functions for character strings and dates in R.

    1 – R basics

    2 – Data structures in R

    3 – R programming fundamentals

    4 – Working with data in R

    5 – Strings and Dates in R

    Part-II Machine Learning

    1 – Machine Learning vs Statistical Modeling & Supervised vs Unsupervised Learning

    2 – Supervised Learning I

    3 – Supervised Learning II

    4 – Unsupervised Learning

    5 – Dimensionality Reduction & Collaborative Filtering

     

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