Machine Learning in Practice

Training 2 days

Dates and Booking

-- Description

Procedure Models, Roles, Applications, and Systems

As a result of the latest developments in the field of deep learning, machine learning has become one of the key topics of artificial intelligence. Although projects in the field of machine learning have much in common with traditional software projects, the procedure, the processes, the architecture, the design, and even the roles are different.

-- Agenda

  • Understand the different types of machine learning: Supervised, unsupervised, and reinforcement learning. Which form of machine learning is suitable for your project?

  • Find applications: Achieve business benefits

  • Data analysis: Data often contain treasure

  • Experiments not iterations: Procedures in a machine learning project

  • Roles in machine learning: New processes necessitate new roles

  • Services and frameworks: Build it yourself or use what’s already there

  • Development life cycle: From experiment to operation

  • Machine learning in production: Monitoring and metrics

  • Architectural goals in machine learning: Machine learning also has quality criteria

  • Basic knowledge of Python, scikit-learn, Jupyter Notebook: Technical basics

-- Your Benefits

Learn about the different phases of a machine learning project by means of practical examples

Become acquainted with the unique capabilities of machine learning

Methods and concepts of machine learning and exercises in Python code

Discover how you can use machine learning for your business

-- Audience

Architects, product owners, developers, and managers. Knowledge of the processes and challenges of software projects is advised. Basic knowledge of the programming is helpful but not essential.

-- Training Objectives

Learn about the different types of machine learning and classify them

Achieve business benefits

Data analysis

Different roles in machine learning

Build services and frameworks yourself or use what’s already there

Development life cycle

Monitoring and metrics

Architectural goals in machine learning

Technical basics: Basic knowledge of Python, scikit-learn, Jupyter Notebook

-- Your Trainers

Oliver Zeigermann

Machine Learning und Frontend-Architektur

-- Technical Information and Books

Machine Learning – kurz & gut: Einführung mit Python, Pandas und Scikit-Learn

Der kompakte Schnelleinstieg in Machine Learning und Deep Learning O’Reilly, 2. Auflage, April 2021, zusammen mit Chi Nhan Nguyen ISBN: 978-3960091615

Machine Learning Lösungen entwerfen (Architektur-Spicker Nr. 10)

Machine Learning (kurz ML) wird häufig mystifiziert. Tatsächlich eröffnet es ganz neue Möglichkeiten. Dabei unterscheiden sich Herangehensweise und Werkzeuge deutlich von klassischer Softwareentwicklung. Dieser Spicker führt unaufgeregt in das Thema ML ein und weist den Weg in eigene Experimente. Download & Infos

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