Introduction to Machine Learning for Data Analysts & Engineers

About the course

Having a clearly defined research question is just the first step to building an effective model with us! This rich and thorough course is designed to inspire you on the possibilities of machine learning and link your specific business question to an appropriate model type. Through a structured approach, we will arm you with an in-depth understanding of the machine learning process. This badge will follow a detailed two-day course walking you through the six steps needed to create a valuable model; from defining your research question to creating the model and model validation. With the use of two case studies, we will teach you how to execute a machine learning task step-by-step, providing you with the appropriate code. With an extensive focus on the basics of modeling which will enable you to correctly execute and supervise a machine learning process and manage a modeling project successfully.

 

Why this is for you

All analysts aim to get the most business impact from their models. Have you ever experienced a model that didn’t perform well or wasn’t adopted by the business to the extent you envisioned? Chances are that you didn’t follow all the steps of the machine learning process. In this module, you’ll learn a structured approach with tips and tricks to ensure your models achieve their maximum business impact.

 

For whom

This course is aimed at all professionals, working hands-on with data and AI, from a data science or data engineering perspective. It requires no pre-requisite to participate and is perfect for anyone looking to improve their models. Our training is valuable not only for new but also experienced Data Scientists and Engineers who still struggle with the process or want to refine their skills.

 

What you’ll learn

Throughout this two-day badge, we will take you through the six key steps of the machine learning process. In each step, we will discuss best practices based on many years of experience in modeling projects.

1. How to define the research question and conceptual model
2. Data selection and preparation
3. How to analyze and improve the concept model
4. Model selection and optimization
5. How to validate, test, and improve the model to draw conclusions
6. How to implement it as a system
7. The most common pitfalls and how to avoid them

Learning Goals

• 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

Theory and practical use

All trainings in the GAIn portfolio combine high-quality standardized training material with theory sessions from experts and hands-on experience where you directly apply the material to real-life cases. Each training is developed by top of the field practitioners which means they are full of industry examples along with practical challenges and know-how, fueling the interactive discussions during training. We believe this multi-level approach creates the ideal learning environment for participants to thrive.