Machine Learning in Practice | | Software Development
Machine Learning in Practice
Duration
24 hours
Location
Online
Language
English
Code
EAS-025
€ 600 *
Training for 7-8 or more people? Customize trainings for your specific needs
Description
We’ll look at the theory on classification, regression, predictions, and ensembles. The entire course is built around practical cases with datasets. For each case, we go through the entire life cycle of a machine learning project. Exploring, cleaning, and preparing data. Selecting a learning method to match the task (linear regression for regression, random forest for classification, K-average and DBSCAN for clustering). Learning with the use of the selected method. Outcome assessment. Model optimization. Representing the result to the customer. We will also devote time to discussing practical tasks that you might deal with, which can be solved by using the reviewed methods.
After completing the course, a certificate
is issued on the Luxoft Training form
is issued on the Luxoft Training form
Objectives
- Understand what tasks can be solved with the help of machine learning (and find out that Big Data is just a subsection, not a mandatory requirement)
- Learn how to utilize initial methods of machine learning, and by using fast prototyping tools learn how to answer the question “Can you evaluate an actual income from possible implementation?”
- Highlight data that should be collected and what can be required from it in near future. Why “we want to store petabytes” – it’s not always just a whim
- Get prepared for more complex subjects, particularly to complete solutions of real complex business problems
- See how exactly machine learning fits with classical analytics. In particular, make sure that it’s unnecessary (or even harmful) to dismiss all existing analysts for concept implementation
Target Audience
Primary:
Secondary:
- Analysts
- Project Managers who deal with data
- Technical Leads / Senior Developers in any data related projects
- Business Analysts
Secondary:
- Developers
- Data Engineers
- Architects, System Designers
Prerequisites
- Ability to read simple code in Python and to write in any script language.
Roadmap
-
Task overview
Which tasks are better solved by machine learning and which are being solved. What will happen if instead of a Data Scientist you hire a non-specialist in a given domain (just a developer/analyst/manager), expecting that they will learn everything in the process. -
Preparing, cleaning, and exploring data
How to gain insight into initial business data (and find whatever order in it at all). Processing sequence. What can and should be done by domain analysts, and what should better be done by a Data Scientist. Priorities in solving a specific task. -
Classifiers and Regressors
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Clustering
Where and how to do clustering: exploring data, task setting check, and validation of results. Which cases can be reduced to clustering. -
Model evaluation
Business metrics and technical metrics. Metrics for tasks of classification and regression, error matrix. Internal and external metrics of clustering quality. Cross validation. Overfitting. -
Optimization
What makes one model better than another: parameters, traits, and ensembles. Parameter management. Traits selection practice. Overview of tools for searching best parameters/traits/methods. -
Graphs, reports, dealing with real-life tasks
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Schedule and prices
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