Machine Learning With R

The R Programming Language

Machine Learning With R

First of all, The Machine Learning with R Course  dives into the basics of machine learning using an approachable, and well-known, programming language. Also, it will look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!

Therefore, The Machine Learning with R 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 Contents

R is a powerful language for data analysis, data visualization, machine learning and statistics. Originally developed for statistical programming, it is now one of the most popular languages in data science. 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. You’re not just learning about R fundamentals, you’ll be using R to solve problems related to movies data.

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.

Session 1 – R basics

  • Math, Variables, and Strings
  • Vectors and Factors
  • Vector operations

Session 2 – Data structures in R

  • Arrays & Matrices
  • Lists
  • Dataframes

Session 3 – R programming fundamentals

  • Conditions and loops
  • Functions in R
  • Objects and Classes
  • Debugging

Session 4 – Working with data in R

  • Reading CSV and Excel Files
  • Reading text files
  • Writing and saving data objects to file in R

Session 5 – Strings and Dates in R

  • String operations in R
  • Regular Expressions
  • Dates in R

Part-II Machine Learning

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

  • Machine Learning Languages, Types, and Examples
  • Machine Learning vs Statistical Modelling
  • Supervised vs Unsupervised Learning
  • Supervised Learning Classification
  • Unsupervised Learning

Session 2 – Supervised Learning I

  • K-Nearest Neighbors
  • Decision Trees
  • Random Forests
  • Reliability of Random Forests
  • Advantages & Disadvantages of Decision Trees

Session 3 – Supervised Learning II

  • Regression Algorithms
  • Model Evaluation
  • Model Evaluation: Overfitting & Underfitting
  • Understanding Different Evaluation Models

Session 4 – Unsupervised Learning

  • K-Means Clustering plus Advantages & Disadvantages
  • Hierarchical Clustering plus Advantages & Disadvantages
  • Measuring the Distances Between Clusters – Single Linkage Clustering
  • Measuring the Distances Between Clusters – Algorithms for Hierarchy Clustering
  • Density-Based Clustering

Session 5 – Dimensionality Reduction & Collaborative Filtering

  • Dimensionality Reduction: Feature Extraction & Selection
  • Collaborative Filtering & Its Challenges

 

November 14, 2018

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