Machine Learning for Development Leaders

Intro to machine learning, the work of a data scientist, and the inside and outside of a machine learning project

Référence de la formation




Nombre de jours

2 Days, 4H/day


688,50 € HT

Lieu de la formation

V: v-learning, virtual class


Basic IT Skills


High level Managers, Presale Managers, IT Managers, QA and Technical Support or those who wants to know better how to work on a data project.

Objectifs de la formation

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

The participants will gain knowledge on the right way to design, work through and deploy a machine learning project in their organization.

Finally, we will do some case studies and try to understand keys and failure points of data projects.

Contenu du cours

Chapter Description
High level intro to machine learning • Current achievements of Machine learning
− Image applications
− Text applications
− Voice application
− Generative application
− Personalization
− Failure detection
Specific intro to machine learning • Types of learning: supervised and unsupervised
• Algorithm overview and model selection
• Data management
• Metrics
Data project: how to plan, address and asses? • Converting business problems to ML language
• Methodology
• Key points
• Common failures
• Deploying ML to production
Machine learning projects: case studies • Classification tasks
• Regression tasks
• Personalization tasks
• Text tasks
• Using ML to improve customer experience


Chapter Description
Practical training

• Working out a real-world problem throughout developments steps, from design, collecting data, early versions, production, and optimizations

Summary including Q&A • Summary including Q&A



11 Août 2020 au 13 Août 2020


10% OFF if second course added

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