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:

Machine learning Basics
Apache Spark Usage
Deep Learning
Decision Trees
Naive Bayes
Logic Regression
Neural Nets
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