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Machine Learning using R in New York-NY, United States

Learn about data classification using algorithms and clustering in R. Including the concepts of regression, forecasting, and visualizing the time series data.

  • 40 hours of Instructor-led sessions
  • Beginner to Advanced level
  • Hands-on training
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Modes of Delivery

Key Features

40 hours of Immersive Instructor-led training
Interactive hands-on learning
Understand the fundamental and advanced concepts of R environment
Learn how general predictive modeling and unsupervised learning are implemented in R
Grasp how the R language environment supports predictive modelling with machine learning
Our Machine Learning experts will guide students in implementing the technology for future projects

Description

If you want to make a career in predictive analysis or work with big data then you have to understand machine learning and R is the most popular platform for applied machine learning. This open source programming language is among the most widely used languages for statistics and data mining, gives quick results and is supported by a worldwide community of users and developers.

Zeolearn academy’s comprehensive Machine Learning using R course is a must for those who see themselves as future analysts. The aim of this coaching is to familiarise you with statistical models for supervised and unsupervised learning using R programming language and how the R language environment supports predictive modelling with machine learning. This Machine Learning using R training at our institute dives into the basics of R and Machine Learning. Enrol now for a complete hands-on familiarity with Machine Learning in R and get the Machine learning certification using R that increases your chances of getting hired.

Here’s what you will learn!

  • How general predictive modeling and unsupervised learning are implemented in R
  • How data is classified using classification algorithms and clustering in R
  • The concept of regression, forecasting and visualizing the time series data
  • Important models such as tree-based, support vector machines and other fundamentals

Prerequisites:

  • Basic knowledge of a programming language such as Python or Java
  • A background in Mathematics will be beneficial

Curriculum

  • What is machine learning
  • Explanation and prediction
  • Learning system model
  • Terminology
  • Training and testing
  • Performance
  • Algorithms
  • Structure
  • The Standard Linear Model
  • Logistic Regression
  • Expansions of Tools
    • Generalised Linear Models
    • Generalised Additive Models 
  • Correlation
  • Strength of linear association
  • Least-squares or regression line
  • Linear regression model
  • Correlation coefficient R
  • Multiple regression
  • Regression diagnostics
  • Assumptions in Regression Analysis
  • Continuous Outcomes
    • Squared Error
    • Absolute Error 
    • Negative Log-likelihood
    • R Example
  • Categorical Outcomes
    • Misclassification
    • Binomial log-likelihood
    • Exponential
    • Hinge Loss
  • Bias & Variance
  • The Tradeoff
  • Diagnosing Bias-Variance Issues & Possible Solutions
    • Worst Case Scenario
    • High Variance
    • High Bias 
  • Adding Another Validation Set
  • K-fold Cross-Validation
    • Leave-one-out Cross-Validation
  • Bootstrap 
  • Beyond Classification Accuracy: Other Measures of Performance 
  • Data Preparation
    • Define Data and Data Partitions
    • Feature Scaling
    • Feature Engineering
    • Discretization
  • Model Selection
  • Model Assessment 
  • The Data set
  • R Implementation
  • Feature Selection & The Data Partition
  • k-nearest Neighbours
    • Strengths & Weaknesses
  • Neural Nets
    • Strengths & Weaknesses
  • Trees & Forests
    • Strengths & Weaknesses
  • Support Vector Machines
    • Strengths & Weaknesses 
  • Unsupervised Learning
    • Clustering
    • Latent Variable Models
    • Graphical Structure
    • Imputation
  • Ensembles
    • Bagging
    • Boosting
    • Stacking
  • Feature Selection & Importance
  • Textual Analysis
  • Bayesian Approaches
  • Naive Bayes learning
  • Conditional probability
  • Bayesian theorem: basics
  • The Bayes classifier
  • Model parameters
  • Naive Bayes training
  • Types of errors
  • Sensitivity and specificity
  • ROC curve
  • Holdout estimation
  • Cross-validation
  • Key requirements
  • Decision tree as a rule set
  • How to create a decision tree
  • Choosing attributes
  • ID3 heuristic
  • Entropy
  • Pruning trees - Pre and post
  • Subtree Replacement
  • Raising
  • Tree induction
  • Splitting based on ordinal attributes
  • How to determine the best split
  • Measure of impurity: GINI
  • Splitting based on GINI
  • Attributes binary
  • Categorical -GINI
  • Strengths and weakness of decision trees
  • Cautionary Notes
  • Some Guidelines
  • Conclusion 

Frequently Asked Questions

Machine learning is often used to make informed preventive or proactive decisions with its powerful predictive capabilities. With the help of algorithms, machine learning helps find insights and patterns in data. This automatic application of complex mathematical calculations to big data is revolutionising the world. R is a programming language that is the most popular platform for applied machine learning and is widely used to statistically explore data sets. Studying R in machine learning will help you become a proficient data analyst.

After completing our course, you will know:

  • About R, its basic data structures, objects and operations
  • Basic plots and lattice plots
  • Basic statistics and machine learning fundamentals
  • Classification of data using Naïve-Bayes classifier
  • Support vector machine and its implementation in R
  • Common classifier algorithms such as Decision Tree

Towards the end of the course, all participants will be required to work on a project to get hands on familiarity with the concepts learnt. You will use R programming language and perform statistical data analysis with support from your mentors. This project, which can also be a live industry project, will be reviewed by our instructors and industry experts. On successful completion, you will be awarded a certificate.

Classes are held on weekdays and weekends. You can check available schedules and choose the batch timings which are convenient for you.

You may be required to put in 10 to 12 hours of effort every week, including the live class, self study and assignments.

We offer classes in classroom and online format. While classroom sessions are held in specific venues in your city, for online sessions all you need is a Windows computer with good internet connection and you can access the class anywhere, at anytime. A headset with microphone is also recommended.

You may also attend these classes from your smart phone or tablet.

Don’t worry, you can always access your class recording or opt to attend the missed session again in any other live batch.

Statisticians, Data Analysts, Business Analysts and professionals keen to learn more about the science and practice of Machine Learning using R, will benefit from this course.

One must have:

  • Operating system such as Mac OS X, Windows or Linux
  • Good Text / JavaScript Editor (Notepad++ / SublimeText / Brackets / Atom )
  • A modern web browser such as Chrome
  • High speed Internet Connection

Machine Learning using R Course in New York-NY

Machine Learning using R

Nowadays, when the corporates want something more, and above the ordinary skill prowess on a recruit’s resume, when it comes to statistics and data analysis, r programming is a big deal, it always has been and will remain so for a long time to come in the future. The Zeolearn institute offers the machine learning using R course in New York. It is a 40 hours course that covers basic to advance level concepts. As this course covers end to end concepts and provides enough opportunities for practical experience, you will get lucrative career opportunities with this machine learning using R training in New York. Any professional who would involve in dealing with machine learning with R can attend the training. Especially, Business Analysts, Data Analysts and Statisticians who want to gain a deeper understanding of the subject will find the course suitable. To understand this machine learning classes using R in New York better, you must have basic knowledge of Java or Python programming language. Having a good mathematical base will be an added advantage.

Unlike similar courses, this course stands out unique because it is driven by the trainer. The tutor will walk you through the course starting with classes and project work to till the end of the machine learning using R course in New York and even after the course, for your future projects.

Two modes of delivery:

This course is available in two formats, i.e. classroom and online mode. For online training, you need a computer or smartphone or tablet. You need to connect your device to the good internet to have smooth access to the virtual machine learning using R training in New York. If you are a person who is comfortable taking classroom sessions, you can opt for classroom mode. These classes are available at limited locations in the city. You can pick the most convenient schedule from all the available slots. Both weekdays and weekend batches and also 2 hour and 4 hours per day slots are available for the participants. You can register early to utilise the fee concession opportunity being provided by Zeolearn academy.

Here, you will learn how the R programming language environment supports predictive analysis with machine learning. To work with big data, you must understand machine learning. This course will greatly help you in understanding R language and machine learning. The machine learning certification using R in New York which you receive at the end of the course will improve your chances of getting recruited.

 

 

 

 

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