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Predictive Analytics with R

  • Course Code: Artificial Intelligence - Predictive Analytics with R
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 4 Days Audience: This course is geared for Python experienced developers, analysts or others who are intending to master the craft of predictive modeling in R by developing strategy, intuition, and a solid foundation in essential concepts

Course Snapshot 

  • Duration: 4 days 
  • Skill-level: Foundation-level R skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who are intending to master the craft of predictive modeling in R by developing strategy, intuition, and a solid foundation in essential concepts 
  • 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, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems. 

The course begins with a dedicated chapter on the language of models and the predictive modeling process. You will understand the learning curve and the process of tidying data. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real-world datasets. How do you train models that can handle really large datasets? This course will also show you just that. Finally, you will tackle the really important topic of deep learning by implementing applications on word embedding and recurrent neural networks. By the end of this course, you will have explored and tested the most popular modeling techniques in use on real- world datasets and mastered a diverse range of techniques in predictive analytics using R. 

Working in a hands-on learning environment, led by our R instructor, students will learn about and explore: 

  • Grasping the major methods of predictive modeling and moving beyond black box thinking to a deeper level of understanding 
  • Leveraging the flexibility and modularity of R to experiment with a range of different techniques and data types 
  • Packed with practical advice and tips explaining important concepts and best practices to help you understand quickly and easily 

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

  • Master the steps involved in the predictive modeling process 
  • Grow your expertise in using R and its diverse range of packages 
  • Learn how to classify predictive models and distinguish which models are suitable for a particular problem 
  • Understand steps for tidying data and improving the performing metrics 
  • Recognize the assumptions, strengths, and weaknesses of a predictive model 
  • Understand how and why each predictive model works in R 
  • Select appropriate metrics to assess the performance of different types of predictive model 
  • Explore word embedding and recurrent neural networks in R 
  • Train models in R that can work on very large datasets 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to master the craft of predictive modeling in R by developing strategy, intuition, and a solid foundation in essential concepts 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills. Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them. 
  • 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. Gearing Up for Predictive Modeling 
  • Gearing Up for Predictive Modeling 
  • Models 
  • Types of model 
  • The process of predictive modeling 
  1. Tidying Data and Measuring Performance 
  • Tidying Data and Measuring Performance 
  • Getting started 
  • Tidying data 
  • Categorizing data quality 
  • Performance metrics 
  • Cross-validation 
  • Learning curves 
  1. Linear Regression 
  • Linear Regression 
  • Introduction to linear regression 
  • Simple linear regression 
  • Multiple linear regression 
  • Assessing linear regression models 
  • Problems with linear regression 
  • Feature selection 
  • Regularization 
  • Polynomial regression 
  1. Generalized Linear Models 
  • Generalized Linear Models 
  • Classifying with linear regression 
  • Introduction to logistic regression 
  • Predicting heart disease 
  • Assessing logistic regression models 
  • Regularization with the lasso 
  • Classification metrics 
  • Extensions of the binary logistic classifier 
  • Poisson regression 
  • Negative Binomial regression 
  1. Neural Networks 
  • Neural Networks 
  • The biological neuron 
  • The artificial neuron 
  • Stochastic gradient descent 
  • Multilayer perceptron networks 
  • The back propagation algorithm 
  • Predicting the energy efficiency of buildings 
  • Predicting glass type revisited 
  • Predicting handwritten digits 
  • Radial basis function networks 
  1. Support Vector Machines 
  • Support Vector Machines 
  • Maximal margin classification 
  • Support vector classification 
  • Kernels and support vector machines 
  • Predicting chemical biodegration 
  • Predicting credit scores 
  • Multiclass classification with support vector machines 
  1. Tree-Based Methods 
  • Tree-Based Methods 
  • The intuition for tree models 
  • Algorithms for training decision trees 
  • Predicting class membership on synthetic 2D data 
  • Predicting the authenticity of banknotes 
  • Predicting complex skill learning 
  • Improvements to the M5 model 
  1. Dimensionality Reduction 
  • Dimensionality Reduction 
  • Defining DR 
  1. Ensemble Methods 
  • Ensemble Methods 
  • Bagging 
  • Boosting 
  • Predicting atmospheric gamma ray radiation 
  • Predicting complex skill learning with boosting 
  1. Probabilistic Graphical Models 
  • Probabilistic Graphical Models 
  • A little graph theory 
  • Bayes’ theorem 
  • Conditional independence 
  • Bayesian networks 
  • The Naïve Bayes classifier 
  1. Topic Modeling 
  • Topic Modeling 
  • An overview of topic modeling 
  • Latent Dirichlet Allocation 
  • Modeling the topics of online news stories 
  • Modeling tweet topics 
  1. Recommendation Systems 
  • Recommendation Systems 
  • Rating matrix 
  • Collaborative filtering 
  • Singular value decomposition 
  • Predicting recommendations for movies and jokes 
  • Loading and pre-processing the data 
  • Exploring the data 
  • Other approaches to recommendation systems 
  1. Scaling Up 
  • Scaling Up 
  • Starting the project 
  • Characteristics of big data 
  • Training models at scale 
  • A path forward 
  • Alternatives 
  1. Deep Learning 
  • Deep Learning 
  • Machine learning or deep learning 
  • What is deep learning? 
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