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Course Skill Level:


Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Those with basic Linux and Apache knowledge

Who should attend & recommended skills

  • This course is geared for attendees with Apache knowledge who wish to know supervised and unsupervised machine learning made easy in Scala with this quick-start guide.
  • Skill-level: Foundation-level Machine Learning with Scala skills for Intermediate skilled team members. This is not a basic class.
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su

About this course

Scala is a highly scalable integration of object-oriented nature and functional programming concepts that make it easy to build scalable and complex big data applications. This course is a handy guide for machine learning developers and data scientists who want to develop and train effective machine learning models in Scala. The course starts with an introduction to machine learning, while covering deep learning and machine learning basics. It then explains how to use Scala-based ML libraries to solve classification and regression problems using linear regression, generalized linear regression, logistic regression, support vector machine, and Naive Bayes algorithms. It also covers tree-based ensemble techniques for solving both classification and regression problems. Moving ahead, it covers unsupervised learning techniques, such as dimensionality reduction, clustering, and recommender systems. Finally, it provides a brief overview of deep learning using a real-life example in Scala.

Skills acquired & topics covered

  • Construct and deploy machine learning systems that learn from your data and give accurate predictions
  • Unleash the power of Spark ML along with popular machine learning algorithms to solve complex tasks in Scala.
  • Solve hands-on problems by combining popular neural network architectures such as LSTM and CNN using Scala with DeepLearning4j library
  • Get acquainted with JVM-based machine learning libraries for Scala such as Spark ML and Deeplearning4j
  • RDDs, DataFrame, and Spark SQL for analyzing structured and unstructured data
  • Supervised and unsupervised learning techniques with best practices and pitfalls
  • Classification and regression analysis with linear regression, logistic regression, Nave Bayes, support vector machine, and tree-based ensemble techniques
  • Effective ways of clustering analysis with dimensionality reduction techniques
  • Recommender systems with collaborative filtering approach
  • Delve into deep learning and neural network architectures

Course breakdown / modules

  • Technical requirements
  • Overview of ML
  • ML tasks
  • Overview of Scala
  • ML libraries in Scala
  • Getting started learning

  • Technical requirements
  • An overview of regression analysis
  • Regression analysis algorithms
  • Learning regression analysis through examples
  • Linear regression
  • Generalized linear regression (GLR)
  • Hyperparameter tuning and cross-validation

  • Technical requirements
  • Overview of classification
  • Developing predictive models for churn
  • LR for churn prediction
  • NB for churn prediction
  • SVM for churn prediction

  • Technical requirements
  • Decision trees and tree ensembles
  • Decision trees for supervised learning
  • Gradient boosted trees for supervised learning
  • Random forest for supervised learning

  • Technical requirements
  • Overview of unsupervised learning
  • Clustering analysis through examples
  • Dimensionality reduction

  • Technical requirements
  • Overview of recommendation systems
  • Model-based course recommendation system

  • Technical requirements
  • DL versus ML
  • DL and ANNs
  • Neural network architectures
  • DL frameworks
  • Getting started with learning