Info: 2 day course

When: in curand

Where: Bucharest, Romania

Course Overview:

Integrating machine learning is paramount in organizations nowadays. machine learning engineers and data scientists are now an integral part of the development teams.

Machine Learning projects has some similarities to a standard software development project, so they may seem easy to work through at first. However, the data dependency, the research oriented work and the unknown nature of results imply different work strategies and project design.

This may lead to an expertise deficit which is not always easy to bridge.
Development leader from any level sometimes lack the knowledge about machine learning, and specifically about working through its related projects.

In this intense workshop, we will go through an intro to machine learning, the work of a data scientist, and the inside and outside of a machine learning project which will give the development manager a better understand of this field.

The seminar
In this seminar, we will go through a crash course in machine learning: we will first see an overview of today’s advances in machine learning.

Then, we will go through some of the basic of machine learning and data science work, and finally we will do some case studies and try to understand keys and failure points of data projects.

The Goal
This intense workshop is intended for development leaders, who want to know better how to work on a data project.
The workshop participants will gain knowledge on the right way to design, work through and deploy a machine learning project in their organization.

Course Content:

1. High level intro to machine learning
• Current achievements of Machine learning – Image applications
– Text applications
– Voice application
– Generative application – Personalization
– Failure detection

2. Specific intro to machine learning
• Types of learning: supervised and unsupervised • Algorithm overview and model selection
• Data management
• Metrics

3. Data project: how to plan, address and asses?
• Converting business problems to ML language • Methodology
• Key points
• Common failures

4. Machine learning projects: case studies
• Classification tasks
• Regression tasks
• Personalization tasks
• Text tasks
• Using ML to improve customer experience

5. Practical workshop
• Working out a real world problem throughout developments steps, from design, collecting data, early versions, production, and optimizations
• Deploying ML to production


Early Bird

cost total per participant

Full Price

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Groups larger than 10 participants



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