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Machine Learning with Scala

  • Course Code: Artificial Intelligence - Machine Learning with Scala
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 2 Days Audience: This course is geared for those who wants to know Supervised and unsupervised machine learning made easy in Scala with this quick-start guide.

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Machine Learning with Scala skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to know Supervised and unsupervised machine learning made easy in Scala with this quick-start guide. 
  • 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. 

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 Naïve 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. 

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

  • 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 

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

  • Get acquainted with JVM-based machine learning libraries for Scala such as Spark ML and Deeplearning4j 
  • Learn RDDs, DataFrame, and Spark SQL for analyzing structured and unstructured data 
  • Understand supervised and unsupervised learning techniques with best practices and pitfalls 
  • Learn classification and regression analysis with linear regression, logistic regression, Naïve Bayes, support vector machine, and tree-based ensemble techniques 
  • Learn effective ways of clustering analysis with dimensionality reduction techniques 
  • Learn recommender systems with collaborative filtering approach 
  • Delve into deep learning and neural network architectures 

Audience & Pre-Requisites 

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. 

Pre-Requisites:  Students should have  

  • 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. Introduction to Machine Learning with Scala 
  • Introduction to Machine Learning with Scala 
  • Technical requirements 
  • Overview of ML 
  • ML tasks 
  • Overview of Scala 
  • ML libraries in Scala 
  • Getting started learning 
  1. Scala for Regression Analysis 
  • Scala for Regression Analysis 
  • 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 
  1. Scala for Learning Classification 
  • Scala for Learning Classification 
  • Technical requirements 
  • Overview of classification 
  • Developing predictive models for churn 
  • LR for churn prediction 
  • NB for churn prediction 
  • SVM for churn prediction 
  1. Scala for Tree-Based Ensemble Techniques 
  • Scala for Tree-Based Ensemble Techniques 
  • Technical requirements 
  • Decision trees and tree ensembles 
  • Decision trees for supervised learning 
  • Gradient boosted trees for supervised learning 
  • Random forest for supervised learning 
  1. Scala for Dimensionality Reduction and Clustering 
  • Scala for Dimensionality Reduction and Clustering 
  • Technical requirements 
  • Overview of unsupervised learning 
  • Clustering analysis through examples 
  • Dimensionality reduction 
  1. Scala for Recommender System 
  • Scala for Recommender System 
  • Technical requirements 
  • Overview of recommendation systems 
  • Model-based course recommendation system 
  1. Introduction to Deep Learning with Scala 
  • Introduction to Deep Learning with Scala 
  • Technical requirements 
  • DL versus ML 
  • DL and ANNs 
  • Neural network architectures 
  • DL frameworks 
  • Getting started with learning 
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