Machine Learning in Practice
A basic practical training in machine learning that covers the entire cycle of building a solution from initial data capture (.xlsx file), through building a model, to explaining data and outcomes specifics to the end customer.
24 hours
Online
English
EAS-025
Machine Learning in Practice
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Duration
24 hours
Location
Online
Language
English
Code
EAS-025
Schedule and prices
14.06.2022 - 21.06.2022
€ 410
Training for 7-8 or more people? Customize trainings for your specific needs
Machine Learning in Practice
Sign Up
Duration
24 hours
Location
Online
Language
English
Code
EAS-025
Schedule and prices
14.06.2022 - 21.06.2022
€ 410
Training for 7-8 or more people? Customize trainings for your specific needs

Description

Well 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

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 its 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 its unnecessary (or even harmful) to dismiss all existing analysts for concept implementation

Target Audience

  • Analysts
  • Project Managers who deal with data
  • Technical Leads / Senior Developers in any data related projects
  • Business Analysts
  • 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

    Practice well formalized tasks with prepared data. Differences between tasks (binary/nonbinary/probabilistic classification, regression), redistribution of tasks across classes. Examples of practical tasks classification.
  • 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

    How to visualize and present results. Semi-automated tests, process control points. From real-life tasks to complete R&D process (R&D in practice) reviewing and analyzing tasks from the audience.
  • Show Entire Program
Schedule and prices
View:
14.06.2022 - 21.06.2022
09:30-13:30
Location:Online
Duration:24 hours
Language:English
Time:09:30-13:30
Timezone:UTC +2
Trainer: Millea Adrian
Trainer Millea Adrian
€ 410
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Trainers
Millea Adrian
Machine Learning Consultant
Adrian is working as a Reinforcement Learning scientist, and is finishing his PhD at Imperial College London. He has worked with various topics over the years, ranging from Echo State Networks to Variational Inference and Information Geometry.

He has a deep understanding of ML principles and the mathematics behind it. Some of the current practical tools he uses are Pytorch, Tensorflow, Ray, Scikit, Pandas. Adrian also works as a teaching assistant at University of Groningen.

His professional and teaching experience make Adrian the perfect choice for our Python and Machine Learning courses.
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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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