Artificial Intelligence may be our generation's most exciting pursuit, and, in order for a machine to be intelligent, it must be able to learn. Machine learning is the process by which computer systems access data, run experiments, and learn from experience, much the same way we do. In this course, you will learn how to set up these systems and how they might benefit your business.
This dynamic course covers both theoretical explanations and practical activities, which comprise over 60% of the course. The main goal is that participants face “real” problems, experience consequential challenges and learn to come up with a solution – guided by the instructor at all stages of the process.
This course is intended for technical analysts or mid-level managers who are willing to do their first steps in Machine Learning.
Analysts/technical managers with at least 1 year of programming experience (Ideally, also experience in Python)
About Python/Jupyter
Overview of main Python libraries to be used
Exploratory Data Analysis: in theory
Exploratory Data Analysis: in practice
Regression
From lab to production: challenges and common problems
The importance of distributed computing in Machine Learning
Deep Learning: What is it and where can it be applied?
Machine Learning in my organization: How can I implement ML considering the current problems we face?
Final summary, questions, suggestions, etc.