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Machine Learning with R

  • Course Code: Artificial Intelligence - Machine Learning with R
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 3 Days Audience: This course is geared for those who wants to Solve real-world data problems with R and machine learning

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Machine Learning with R skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Solve real-world data problems with R and machine learning 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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.. 

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 
  • Harness the power of R to build flexible, effective, and transparent machine learning models 
  • Learn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Discover the origins of machine learning and how exactly a computer learns by example 
  • Prepare your data for machine learning work with the R programming language 
  • Classify important outcomes using nearest neighbor and Bayesian methods 
  • Predict future events using decision trees, rules, and support vector machines 
  • Forecast numeric data and estimate financial values using regression methods 
  • Model complex processes with artificial neural networks — the basis of deep learning 
  • Avoid bias in machine learning models 
  • Evaluate your models and improve their performance 
  • Connect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlow 

Audience & Pre-Requisites 

This course is geared for attendees who wish to Solve real-world data problems with R and machine learning 

Pre-Requisites:  Students should have  

  • Basic to ML Skills. 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Introducing Machine Learning 
  • Introducing Machine Learning 
  • The origins of machine learning 
  • Uses and abuses of machine learning 
  • How machines learn 
  • Machine learning in practice 
  • Machine learning with R 
  1. Managing and Understanding Data 
  • Managing and Understanding Data 
  • R data structures 
  • Managing data with R 
  • Exploring and understanding data 
  1. Lazy Learning – Classification Using Nearest Neighbors 
  • Lazy Learning – Classification Using Nearest Neighbors 
  • Understanding nearest neighbor classification 
  • Example – diagnosing breast cancer with the k-NN algorithm 
  1. Probabilistic Learning – Classification Using Naive Bayes 
  • Probabilistic Learning – Classification Using Naive Bayes 
  • Understanding Naive Bayes 
  • Example – filtering mobile phone spam with the Naive Bayes algorithm 
  1. Divide and Conquer – Classification Using Decision Trees and Rules 
  • Divide and Conquer – Classification Using Decision Trees and Rules 
  • Understanding decision trees 
  • Example – identifying risky bank loans using C5.0 decision trees 
  • Understanding classification rules 
  • Example – identifying poisonous mushrooms with rule learners 
  1. Forecasting Numeric Data – Regression Methods 
  • Forecasting Numeric Data – Regression Methods 
  • 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 
  1. Black Box Methods – Neural Networks and Support Vector Machines 
  • Black Box Methods – Neural Networks and Support Vector Machines 
  • Understanding neural networks 
  • Example – modeling the strength of concrete with ANNs 
  • Understanding support vector machines 
  • Example – performing OCR with SVMs 
  1. Finding Patterns – Market Basket Analysis Using Association Rules 
  • Finding Patterns – Market Basket Analysis Using Association Rules 
  • Understanding association rules 
  • Example – identifying frequently purchased groceries with association rules 
  1. Finding Groups of Data – Clustering with k-means 
  • Finding Groups of Data – Clustering with k-means 
  • Understanding clustering 
  • Finding teen market segments using k-means clustering 
  1. Evaluating Model Performance 
  • Evaluating Model Performance 
  • Measuring performance for classification 
  • Estimating future performance 
  1. Improving Model Performance 
  • Improving Model Performance 
  • Tuning stock models for better performance 
  • Improving model performance with meta-learning 
  1. Specialized Machine Learning Topics 
  • Specialized Machine Learning Topics 
  • 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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