Program Duration

##### Duration - 56 Hours

Accredited by

##### NSDC

Fexible Learning

##### In-center and online

Learn from

##### Industry Experts

#### Objective

Machine learning using R is to use the R programming language and its powerful libraries to build and deploy machine learning models that can analyze and make predictions on large datasets. With machine learning using R, you can gain insights into complex data, automate tasks, and make data-driven decisions that can help businesses gain a competitive advantage.

** Understanding machine learning algorithms : **To build effective machine learning models using R, you need to have a good understanding of different algorithms, such as regression, decision trees, clustering, and neural networks.

** Data preprocessing :** Before building machine learning models, you need to preprocess data by cleaning, transforming, and normalizing it to ensure accuracy and consistency.

** Feature selection : **To create efficient and effective machine learning models, you need to select relevant features from the dataset that can help in making predictions.

** Model building and evaluation :** Using R, you can build machine learning models and evaluate their performance based on different metrics, such as accuracy, precision, recall, and F1 score.

** Model deployment :** Once the machine learning model is built and evaluated, you can deploy it into production and use it to make predictions on new data.

#### Who Can Learn :

Machine learning using R is suitable for anyone who wants to gain insights into complex data, automate tasks, and make data-driven decisions using machine learning techniques. Here are some groups of people who can benefit from learning machine learning using R:

** Data analysts : **If you are a data analyst, learning machine learning using R can help you gain deeper insights into your data and make more accurate predictions.

** Data scientists :** Data scientists can use R to build and deploy powerful machine learning models to solve complex data problems.

** Business analysts : **Business analysts can use machine learning using R to analyze customer behavior, optimize marketing campaigns, and make data-driven decisions that can help their organizations gain a competitive advantage.

** Software developers :** If you are a software developer, learning machine learning using R can help you build intelligent systems and automate tasks.

** Students and researchers : **Students and researchers who are interested in data science and machine learning can learn R and use it to conduct data analysis and build machine learning models.

#### Job Opportunities :

** Data Scientist :** A data scientist is responsible for building and deploying machine learning models to analyze data and make predictions. Machine learning using R is a critical skill for this job role.

** Machine Learning Engineer : **A machine learning engineer is responsible for designing, building, and deploying machine learning models. Expertise in R programming language and its libraries is essential for this job role.

** Business Analyst :** Business analysts use machine learning models to analyze customer behavior, optimize marketing campaigns, and make data-driven decisions. Machine learning using R is an essential skill for this job role.

** Data Analyst :** Data analysts use machine learning models to gain insights into complex data and make more accurate predictions. Machine learning using R is a valuable skill for this job role.

** Research Scientist : **Research scientists use machine learning to analyze data and gain insights into various fields, such as biology, medicine, and social sciences. Machine learning using R is a critical skill for this job role.

#### Frequently Asked Questions:

** 1. What is R and why is it used for machine learning?**

R is a programming language and software environment for statistical computing and graphics. It is widely used in the field of data science and machine learning due to its powerful statistical analysis capabilities, extensive collection of libraries for data manipulation and analysis, and easy-to-learn syntax.

** 2. What are the most commonly used R libraries for machine learning?**

Some of the most commonly used R libraries for machine learning include caret, MLR, randomForest, xgboost, and glmnet.

** 3. What are the different types of machine learning models that can be built using R?**

R can be used to build a wide range of machine learning models, including linear regression, logistic regression, decision trees, random forests, support vector machines, neural networks, and deep learning models.

** 4. How can I learn machine learning using R?**

You can start by learning the basics of R programming language and then move on to learning machine learning concepts and techniques. There are many online courses, tutorials, and books available that can help you learn machine learning using R.

** 5. Can R be used for big data processing and machine learning?**

Yes, R can be used for big data processing and machine learning using libraries such as bigmemory, ff, and sparklyr. These libraries allow R to handle large datasets and perform distributed computing.

#### Course Curriculum

» Preliminaries of statistics

» R programming for machine learning

» Statistic programming using Python

» Pre-processing using Python

» Supervised learning

» ClassifiCation

• Decision tree

• GINI

• Entropy

• Support vector machine

• Bayesian network (BNB,GNBtMNB)

• KNN

• Random Forest

» Regression

• Logistic regression

• Linear regression

• Random Forest

• Multinomial regression

• Poisson regression

• Negative binomial regression

• Ridge regression

• Lasso Regression

» Unsupervised learning

» Clustering

» Hierarchical

» K-means

» K-medoids

» CLARA

» DBSCAN

» Dimension Reduction

» Text mining using NLP

» PCA

» WordCloud

» Survival Analysis

» Deep learning (Neural Network)

• ANN for classification problem

• ANN for regression problem

• RNN for time and sequence related problems

#### API & Libraries

Im, glm, caTools, scale, fast_ dummies, gmodels, qnom, binom, skew, kurtosis, magrittr, dpIyr, e1071, class, caret, ts, forecast, wordcloud, corpus, neural net, etc.