EAS-020 Machine Learning Fundamentals

EAS-020 Machine Learning Fundamentals
24 hours
Online
English
EAS-020
EAS-020 Machine Learning Fundamentals
Sign Up
Duration
24 hours
Location
Online
Language
English
Code
EAS-020
Schedule and prices
600.00 *
Training for 7-8 or more people? Customize trainings for your specific needs
EAS-020 Machine Learning Fundamentals
Sign Up
Duration
24 hours
Location
Online
Language
English
Code
EAS-020
Schedule and prices
600.00 *
Training for 7-8 or more people? Customize trainings for your specific needs

Description

This course provides an introduction to the fundamentals of Machine Learning, featuring key topics such as Basics, Spark for distributed data processing, Neural Networks, and Deep Learning. We will study how to utilize essential algorithms like Decision Trees, Naive Bayes, Logistic Regression, and Clustering techniques to unlock insightful patterns from data.
After completing the course, a certificate
is issued on the Luxoft Training form

Objectives

After completion of the course, students will better understand:

  1. Machine learning Basics
  2. Apache Spark Usage
  3. Deep Learning
  4. Decision Trees
  5. Naive Bayes
  6. Logic Regression
  7. Neural Nets
  8. Clustering

Target Audience

ML developers, architects & testers that need to automate a part of their activity.

Prerequisites

  • Understand principles of object-oriented programming
  • At least one year’s experience of working with object-oriented languages
  • Advisable to know Java

Roadmap

  • [Theory – 2,5h: Practice – 1.5h] Introduction to Machine Learning

    • The fundamentals of ML
    • Tasks formulation 
    • ML algorithms 
    • Data manipulation methods 
    • Model evaluation
    • Clustering algorithms

  • [Theory – 2h: Practice – 2h] Spark MLLib and Spark ML

    • MLLIB 
    • ML Pipelines
  • [Theory – 1.5h: Practice – 2.5h] Spark MLLib and Spark ML

    • MLLIB 
    • ML Pipelines
  • [Theory – 1.5h: Practice – 2.5h] Algorithms

    • Decision Trees
    • Naïve Bayes
  • [Theory – 1.5h: Practice – 2.5h] Algorithms

    • Logic Regression
    • Neural Nets
  • [Theory – 3h: Practice – 1h] Clustering

    • Clustering basics
    • Hierarchical clustering
    • Gaussian mixture model
    • Hard EM
    • Stanford EM
  • Show Entire Program
Schedule and prices
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Your benefits
Expertise
Our trainers are industry experts, involved in software development project
Live training
Facilitated online so that you can interact with the trainer and other participants
Practice
A focus on helping you practice your new skills
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