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Home » Technology » AI / Machine Learning » Machine Learning with R

Course Skill Level:

Foundational

Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:

    MALEWRL21E09

Who should attend & recommended skills:

Those with basic Linux and machine learning experience

Who should attend & recommended skills

  • This course is geared for those who wish to solve real-world data problems with R and machine learning.
  • Skill-level: Foundation-level Machine Learning with R skills for Intermediate skilled team members. This is not a basic class.
  • Machine Learning: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su

About this course

Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, This Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, course teaches you everything you need to uncover key insights, make new predictions, and visualize your findings. This updates the classic R data science course with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning with R expert instructor, students will learn about and explore:
  • Bestselling, widely acclaimed R machine learning course, updated and improved for R 3.5 and beyond
  • Harnessing the power of R to build flexible, effective, and transparent machine learning models
  • Learning quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner
  • The origins of machine learning and how exactly a computer learns by example
  • Preparing your data for machine learning work with the R programming language
  • Classifying important outcomes using nearest neighbor and Bayesian methods
  • Predicting future events using decision trees, rules, and support vector machines
  • Forecasting numeric data and estimate financial values using regression methods
  • Modeling complex processes with artificial neural networks – the basis of deep learning
  • Avoiding bias in machine learning models
  • Evaluating your models and improve their performance
  • Connecting R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlow

Course breakdown / modules

  • The origins of machine learning
  • Uses and abuses of machine learning
  • How machines learn
  • Machine learning in practice
  • Machine learning with R

  • R data structures
  • Managing data with R
  • Exploring and understanding data

  • Understanding nearest neighbor classification
  • Example – diagnosing breast cancer with the k-NN algorithm

  • Understanding Naive Bayes
  • Example – filtering mobile phone spam with the Naive Bayes algorithm

  • Understanding decision trees
  • Example – identifying risky bank loans using C5.0 decision trees
  • Understanding classification rules
  • Example – identifying poisonous mushrooms with rule learners

  • Understanding regression
  • Example – predicting medical expenses using linear regression
  • Understanding regression trees and model trees
  • Example – estimating the quality of wines with regression trees and model trees

  • Understanding neural networks
  • Example – modeling the strength of concrete with ANNs
  • Understanding support vector machines
  • Example – performing OCR with SVMs

  • Understanding association rules
  • Example – identifying frequently purchased groceries with association rules

  • Understanding clustering
  • Finding teen market segments using k-means clustering

  • Measuring performance for classification
  • Estimating future performance

  • Tuning stock models for better performance
  • Improving model performance with meta-learning

  • Managing and preparing real-world data
  • Working with online data and services
  • Working with domain-specific data
  • Improving the performance of R

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