PRODUCT DESCRIPTION

Machine Learning with Scala generally includes the techniques for most innovative and also cutting edge for Machine Learning with Scala Implementation. Scala is generally deep dive into Data Management for creating and working with sophisticated algorithms.

OBJECTIVES

  • Understanding the implementation of classification, regression and clustering
  • Discovering the concept of key Scala ML Libraries
  • Understanding the advanced techniques for analytics in predicting situations

ADVANTAGES

  • Ability for performing ML Techniques for large datasets in technological world
  • Scala Programming Language scope to highly advanced level
  • Several implementation of ML Algorithms for securing Application
Duration: 2 Days

Course Content:
  1. Basic Overview of Scala

    • Introduction to Scala
    • Overview of Functional Combinators in Scala
    • Understanding Scala Traits, Classes and Objects
    • Using IntelliJ IDEA as an IDE
    • Using Breeze Library for Linear Algebra
    • Working with WISP for Plotting
  2. Understanding Exploratory Data Analysis with Scala

    • Data Analysis Exploratory Concepts
    • Using DataFrames with Scala
    • Plotting scenario with Breeze
  3. Basics of Supervised Learning

    • Basic Formulation for Supervised Learning Problem
    • Understanding two basic Regression Algorithms
    • Implementation of K-Nearest Neighbors in Scala
    • Implementation of Naive Bayes in Scala
    • Selection of Model concept
    • Overview of Unsupervised Learning

      • Basic Formulation for Unsupervised Learning
      • Implementation of K-means Algorithm in Scala
      • Mixture of Gaussian Clustering
      • Implementation of Mixture of Gaussian Clustering in Scala
      • Understanding Dimensionality Reduction with Principle Component Analysis (PCA)
      • Implementation of PCA in Scala
    • Understanding the concept of Neural Networks

      • Basic Overview of Feed-Forward Neural Networks
      • Implementation of Feed-Forward Neural Networks in Scala
      • Overview of Restricted Boltzmann Machines (RBMs)
      • Implementation of RBM in Scala
    • Additional Scala Frameworks for Machine Learning

      • Understanding Akka Actor Model for Concurrency
      • Using Multi-Threaded K-Nearest neighbor Implementation with Akka
      • Basic Overview of Apache Spark
      • Running Linear Regression on Spark with MLib