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Data Sciences

R Machine Learning Projects

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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.

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Course Access

Unlimited Duration

Last Updated

July 29, 2021

Students Enrolled

20

Total Reviews

Posted by

Ernesto Lee

Certification

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.

Course Curriculum

    • Exploring the Machine Learning Landscape 00:00:00
    • ML versus software engineering 00:00:00
    • Types of ML methods 00:00:00
    • ML terminology – a quick review 00:00:00
    • ML project pipeline 00:00:00
    • Learning paradigm 00:00:00
    • Datasets 00:00:00
    • Predicting Employee Attrition Using Ensemble Models 00:00:00
    • Philosophy behind ensembling 00:00:00
    • Getting started 00:00:00
    • Understanding the attrition problem and the dataset 00:00:00
    • K-nearest neighbors model for benchmarking the performance 00:00:00
    • Bagging 00:00:00
    • Randomization with random forests 00:00:00
    • Boosting 00:00:00
    • Stacking 00:00:00
    • Implementing a Jokes Recommendation Engine 00:00:00
    • Fundamental aspects of recommendation engines 00:00:00
    • Getting started 00:00:00
    • Understanding the Jokes recommendation problem and the dataset 00:00:00
    • Building a recommendation system with an item-based collaborative filtering technique 00:00:00
    • Building a recommendation system with a user-based collaborative filtering technique 00:00:00
    • Building a recommendation system based on an association-rule mining technique 00:00:00
    • Content-based recommendation engine 00:00:00
    • Building a hybrid recommendation system for Jokes recommendations 00:00:00
    • Sentiment Analysis of Amazon Reviews with NLP 00:00:00
    • The sentiment analysis problem 00:00:00
    • Getting started 00:00:00
    • Understanding the Amazon reviews dataset 00:00:00
    • Building a text sentiment classifier with the BoW approach 00:00:00
    • Understanding word embedding 00:00:00
    • Building a text sentiment classifier with pretrained word2vec word embedding based on Reuters news corpus 00:00:00
    • Building a text sentiment classifier with GloVe word embedding 00:00:00
    • Building a text sentiment classifier with fastText 00:00:00
    • Customer Segmentation Using Wholesale Data 00:00:00
    • Understanding customer segmentation 00:00:00
    • Understanding the wholesale customer dataset and the segmentation problem 00:00:00
    • Identifying the customer segments in wholesale customer data using k-means clustering 00:00:00
    • Identifying the customer segments in the wholesale customer data using DIANA 00:00:00
    • Identifying the customer segments in the wholesale customers data using AGNES 00:00:00
    • Image Recognition Using Deep Neural Networks 00:00:00
    • Technical requirements 00:00:00
    • Understanding computer vision 00:00:00
    • Achieving computer vision with deep learning 00:00:00
    • Introduction to the MXNet framework 00:00:00
    • Understanding the MNIST dataset 00:00:00
    • Implementing a deep learning network for handwritten digit recognition 00:00:00
    • Implementing computer vision with pretrained models 00:00:00
    • Credit Card Fraud Detection Using Autoencoders 00:00:00
    • Machine learning in credit card fraud detection 00:00:00
    • Autoencoders explained 00:00:00
    • The credit card fraud dataset 00:00:00
    • Building AEs with the H2O library in R 00:00:00
    • Automatic Prose Generation with Recurrent Neural Networks 00:00:00
    • Understanding language models 00:00:00
    • Exploring recurrent neural networks 00:00:00
    • Backpropagation through time 00:00:00
    • Problems and solutions to gradients in RNN 00:00:00
    • Building an automated prose generator with an RNN 00:00:00
    • Winning the Casino Slot Machines with Reinforcement Learning 00:00:00
    • Understanding RL 00:00:00
    • Multi-arm bandit – real-world use cases 00:00:00
    • Solving the MABP with UCB and Thompson sampling algorithms 00:00:00

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