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Course Skill Level:

Intermediate to Advanced

Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Developers with basic Python experience

Who should attend & recommended skills

  • This course is designed for developers wanting to master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and more.
  • Skill-level: Foundation-level R Machine Learning Projects skills for Intermediate skilled team members. This is not a basic class.
  • Basics of Python: Basic (1-2 years’ experience)

About this course

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.

Skills acquired & topics covered

  • Mastering machine learning, deep learning, and predictive modeling concepts in R 3.5
  • Building intelligent end-to-end projects for finance, retail, social media, and a variety of domains
  • Implementing smart cognitive models with helpful tips and best practices
  • Exploring deep neural networks and various frameworks that can be used in R
  • Developing a joke recommendation engine to recommend jokes that match users’ tastes
  • Creating powerful ML models with ensembles to predict employee attrition
  • Building autoencoders for credit card fraud detection
  • Working with image recognition and convolutional neural networks
  • Making predictions for casino slot machine using reinforcement learning
  • Implementing NLP techniques for sentiment analysis and customer segmentation

Course breakdown / modules

  • ML versus software engineering
  • Types of ML methods
  • ML terminology – a quick review
  • ML project pipeline
  • Learning paradigm
  • Datasets

  • 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

  • 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

  • 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

  • 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

  • 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

  • Machine learning in credit card fraud detection
  • Autoencoders explained
  • The credit card fraud dataset
  • Building AEs with the H2O library in R

  • 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

  • Understanding RL
  • Multi-arm bandit – real-world use cases
  • Solving the MABP with UCB and Thompson sampling algorithms