The state of Machine Learning with R in 2022

The constantly evolving job hiring landscape requires you to be a step ahead and become familiar with leading technologies and platforms. If you are a wannabe data scientist or Machine Learning engineer who wants to improve your chances of getting hired, this becomes more important. And whether you want to make a career switch or improve your performance as a data scientist, learning software like R can be a feather in your cap.

The Machine Learning with R free course equips you with R knowledge while helping you be future-ready with a career in Machine Learning. It teaches you the basics of Machine Learning using an approachable programming language.

Machine Learning is a core skill in data science, and coupling the two in a single Bootcamp can launch your career in 2022.

What is Machine Learning and its relevance in 2022

Machine Learning (ML) is a type of artificial intelligence (AI) based on the hypothesis that systems learn on their own from data without human intervention. ML uses computer algorithms to improve automatically through iterations without being explicitly programmed. Algorithms and statistical models analyze and identify patterns, make decisions and predict outcomes. Historical data is used as input to predict output values.

Machine Learning is used in a wide range of applications. An instance is the recommendation engine that drives the Facebook news feed. Facebook uses Machine Learning to study patterns in user behaviour and personalize the member feed.

There are different types of Machine Learning based on how an algorithm learns. The four basic types are supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. The type of data to be predicted decides what algorithm is to be used.

Why is Machine Learning important?

Machine Learning unlocks the value of enterprise and customer data to predict outcomes with precisions and without human engagement. It learns iteratively from data to make predictions and discover insights.  Today, many leading companies, such as Facebook, Twitter, Google and Uber, use Machine Learning for their key operations to understand user behaviour and leverage this information for a competitive edge.

How do you use R for Machine Learning

R is used for exploration and building models in Machine Learning.

The R interactive environment offers the perfect way to explore and learn how to use packages and functions and discover the many packages used for Machine Learning purposes. The environment also allows you to explore a new problem and test what-if scenarios.

For any Machine Learning project, it is best to begin by developing scripts. Each task is described and documented in a new script for updates. The R platform is also ideal if you want to discover what model to use.

Why use R?

R is an open-source programming language with an extensive library of built-in packages, many of which are dedicated to Machine Learning functionalities. As a statistical language, it has all the features necessary for Machine Learning and data science tasks.

R is one of the most popular programming languages used by data scientists. It is not merely a programming language but also an environment for statistical analysis and modelling. Thus R allows the user to perform operations like data processing, data discovery, sampling, supervised learning, mathematical modelling and more.

There are more reasons to use R for Machine Learning instead of other programming languages.

R is used globally for all data science jobs and is the most popular Machine Learning tool. The breadth of techniques it offers makes R very powerful for modelling.

The R language is also used by academics and becoming a rage in 2022. New algorithms are developed in R and released as dedicated R packages, making R a simple-to-use tool for analysis. Algorithms in R, can be processed instantly.

Besides, R statistical language does not require you to be a programmer. The R syntax uses scripts that are easy to learn. So getting your way around R is not about being a good programmer but essentially knowing which packages to use and how to deploy them for each Machine Learning task in hand. As you have hundreds of Machine Learning packages, they facilitate Machine Learning tasks even for the newbie.

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What are the best R packages for Machine Learning tasks

As Machine Learning with R is about familiarity with R packages that lend themselves to Machine Learning, here are some leading R packages:

CARET: Classification and Regression Training (CARET) package is useful for all your Predictive Modelling needs.

MICE: Multivariate Imputation by Chained Sequences (MICE) package is used for inputting missing values.

RPART: The Recursive Partitioning And Regression Training (RPART) package is used to  partition your data and build classification or regression models.

PARTY: PARTY package in R is used for recursive partitioning of your data.

RANDOM FOREST: Random Forest algorithm is a widely used ML algorithm. The Random Forest package takes random samples and arranges observations into the decision tree.

E1071: The package is used for implementing Naïve Bayes, SVM, Fourier Transforms, Bagged Clustering, Fuzzy Clustering, and such algorithms. 

KERNLAB: It is a package for kernel-based Machine Learning algorithms like SVM, kernel feature analysis, ranking algorithm, dot product primitives, Gaussian process, and spectral clustering algorithm.

NNET: This is the most widely used and easy to understand neural network package,

DPLYR: provides feasible, fast, and stable functions for data handling.

TM: It is a Machine Learning package of R used for solving text mining tasks.

IGRAPH: It is one of the top Machine Learning R packages for network analysis. 

OCR: This R package helps in visualizing the performance of scoring classifiers.

DATA EXPLORER: The DataExplorer package serves in the field of exploratory data analysis (EDA), a predictive analytics task.

MLR: The mlr package provides encryption to various Machine Learning tasks.

ARULES: The arules package is a widely used R Machine Learning package for representation and transaction analysis of data and patterns and data manipulation, and also in Apriori and Eclat association mining algorithms.

MBOOST: It is a package that has an operative gradient descent algorithm for enhancing general risk functions.

If you are a fresh graduate who wants to learn the most relevant skills and programming language of 2022 or a data practitioner who wants to master R statistical software, register for the free course to upskill and make yourself job-ready.