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R Machine Learning Projects

  • Course Code: Data Science - R Machine Learning Projects
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 2 Days Audience: This course is geared for those who wants to Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and more.

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level R Machine Learning Projects skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and more. 
  • 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. 

R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this course, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. This course will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you’ll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You’ll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the course will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine. By the end of the course, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations. 

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

  • Master machine learning, deep learning, and predictive modeling concepts in R 3.5 
  • Build intelligent end-to-end projects for finance, retail, social media, and a variety of domains 
  • Implement smart cognitive models with helpful tips and best practices 

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

  • Explore deep neural networks and various frameworks that can be used in R 
  • Develop a joke recommendation engine to recommend jokes that match users’ tastes 
  • Create powerful ML models with ensembles to predict employee attrition 
  • Build autoencoders for credit card fraud detection 
  • Work with image recognition and convolutional neural networks  
  • Make predictions for casino slot machine using reinforcement learning 
  • Implement NLP techniques for sentiment analysis and customer segmentation 

Audience & Pre-Requisites 

This course is designed for developers wants to Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and more 

Pre-Requisites:  Students should have familiar with  

  • Basics of Python  
  • Knowledge of Python is assumed. 

Course Agenda / Topics 

  1. Exploring the Machine Learning Landscape 
  • Exploring the Machine Learning Landscape 
  • ML versus software engineering 
  • Types of ML methods 
  • ML terminology – a quick review 
  • ML project pipeline 
  • Learning paradigm 
  • Datasets 
  1. Predicting Employee Attrition Using Ensemble Models 
  • Predicting Employee Attrition Using Ensemble Models 
  • Philosophy behind ensembling  
  • Getting started 
  • Understanding the attrition problem and the dataset  
  • K-nearest neighbors model for benchmarking the performance 
  • Bagging 
  • Randomization with random forests 
  • Boosting  
  • Stacking  
  1. Implementing a Jokes Recommendation Engine 
  • Implementing a Jokes Recommendation Engine 
  • Fundamental aspects of recommendation engines 
  • Getting started 
  • Understanding the Jokes recommendation problem and the dataset 
  • Building a recommendation system with an item-based collaborative filtering technique 
  • Building a recommendation system with a user-based collaborative filtering technique 
  • Building a recommendation system based on an association-rule mining technique 
  • Content-based recommendation engine 
  • Building a hybrid recommendation system for Jokes recommendations 
  1. Sentiment Analysis of Amazon Reviews with NLP 
  • Sentiment Analysis of Amazon Reviews with NLP 
  • The sentiment analysis problem 
  • Getting started 
  • Understanding the Amazon reviews dataset 
  • Building a text sentiment classifier with the BoW approach 
  • Understanding word embedding 
  • Building a text sentiment classifier with pretrained word2vec word embedding based on Reuters news corpus 
  • Building a text sentiment classifier with GloVe word embedding 
  • Building a text sentiment classifier with fastText 
  1. Customer Segmentation Using Wholesale Data 
  • Customer Segmentation Using Wholesale Data 
  • Understanding customer segmentation 
  • Understanding the wholesale customer dataset and the segmentation problem 
  • Identifying the customer segments in wholesale customer data using k-means clustering 
  • Identifying the customer segments in the wholesale customer data using DIANA 
  • Identifying the customer segments in the wholesale customers data using AGNES 
  1. Image Recognition Using Deep Neural Networks 
  • Image Recognition Using Deep Neural Networks 
  • Technical requirements 
  • Understanding computer vision 
  • Achieving computer vision with deep learning 
  • Introduction to the MXNet framework 
  • Understanding the MNIST dataset 
  • Implementing a deep learning network for handwritten digit recognition 
  • Implementing computer vision with pretrained models 
  1. Credit Card Fraud Detection Using Autoencoders 
  • Credit Card Fraud Detection Using Autoencoders 
  • Machine learning in credit card fraud detection 
  • Autoencoders explained 
  • The credit card fraud dataset 
  • Building AEs with the H2O library in R 
  1. Automatic Prose Generation with Recurrent Neural Networks 
  • Automatic Prose Generation with Recurrent Neural Networks 
  • Understanding language models 
  • Exploring recurrent neural networks 
  • Backpropagation through time 
  • Problems and solutions to gradients in RNN 
  • Building an automated prose generator with an RNN 
  1. Winning the Casino Slot Machines with Reinforcement Learning 
  • Winning the Casino Slot Machines with Reinforcement Learning 
  • Understanding RL 
  • Multi-arm bandit – real-world use cases 
  • Solving the MABP with UCB and Thompson sampling algorithms 

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