Introduction to Machine Learning for Data Analysts & Engineers ★★ Practitioner Level
Learn how you can create and implement machine learning algorithms within your business. Join us and discover the ‘six steps of the machine learning process’ in which you will gain the skills required to execute all necessary steps to develop a model while avoiding the most common pitfalls.
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Introduction to Machine Learning for Data Analysts & Engineers
- How to define the research question and conceptual model
- Data selection and preparation
- How to analyze and improve the concept model
- Model selection and optimization
- How to validate, test, and improve the model to draw conclusions
- How to implement it as a system
- The most common pitfalls and how to avoid them
- Overview of machine learning – Understand the importance of modeling. Have an overview of different machine learning techniques and evaluation metrics
- Building the modeling team – Identify which knowledge and experience should be present in a modeling team to be successful
- Structuring a modeling project – Create business impact and having sufficient knowledge about each step to have an overview
- Roles within the modeling process – Distinguish what the role of the analyst is versus the business stakeholder
- Dealing with pitfalls – Recognize which major modeling pitfalls and challenges there are to avoid stepping into them