Start Date: 14/03/2022

Price 2,094 ILS


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.

Expectations and Goals

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 Outline:

1. High level intro to machine learning
• Current achievements of Machine learning

– Image applications
– Text applications
– Voice application
– Genereative 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
• Deploying ML to production

4. Machine learning projects: case studies
• Classification tasks
• Regression tasks
• Personlaization tasks
• Text tasks
• Using ML to improve customer experience5. Practical workshop
• Working out a real world problem throughout developments steps, from design, collecting data, early versions, production, and optimizations