Welcome, AI/ML enthusiast

STRV is pleased to offer this 7-week intensive course as part of our STRV Academy. During the course, you will learn to apply plenty of practical and theoretical skills needed for developing and completing your own End-to-End Machine Learning Projects. You will gain practical knowledge and hands-on experience with applied ML techniques and an intuitive understanding of what can be achieved through ML and what its limitations are.

Hearty thanks to our authors: Jan Maly, Niek Mereu , Lukas Koucky, and Jaroslav Bezdek.

✏️ This is the very first run of our DS Academy. We want to make the Academy tremendous, so we will ask for a lot of feedback. If there is a bug or mistake in the implementation, please create a report on GitHub.

The Format

The Academy consists of two equally important parts. The first part aims to teach you the necessary practical and theoretical skills to complete the second part. In the second part, you will work on your personal project.

Lectures

There will be six lectures (not counting the last one) dedicated to introducing you to crucial basic Machine Learning concepts. Each class is divided into two sessions, separated by a break. We will cover a necessary theory for you to understand the practical part involving live coding.

The handbook should contain all materials used during lectures. We will extend materials with recordings of each session once we capture them.

End-to-End Machine Learning Projects

Each student is supposed to work on their personal project during the Academy. A personal project is a great way to apply and practice knowledge from lessons on some real word problems. It is also an excellent opportunity to explore specific areas more deeply. Students will be supported by mentors. Results of projects will be presented during Demo Day.

Getting Started

Before the start of the Academy, we expect you to finish the preliminary lecture, so you have everything ready for the first lecture. It is also essential to review the initial information for personal projects. In the first lecture, you will be matched with a mentor to help you with the project. It is crucial to have a project in mind, so we can find you an appropriate mentor.

The Plan

Number	Lecture	Date	Description	Speaker
0.	Getting Started	before the curse	Guide to prepare you for the course	NA
1.	Getting Your Hands Dirty with Basics	19th of September	Basics of Python and end-to-end motivational ML example	Jan Maly
2.	Understanding, Extracting, Sourcing, and Processing Data	26th of September	Start exploring and processing datasets for your ML projects	Prepared by Jan Maly, delivered by Jozef Reginac
3.	Basic concepts in AI/ML	3rd of October	Supervised and Unsupervised Learning	Niek Mereu
4.	(Zoo of) ML algorithms & Improving ML models	10th of October	ML algorithms and data pipelines	Niek Mereu
5.	Evaluating, comparing, and selecting ML models	17th of October	Model evaluation and selection	Lukas Koucky
6.	Deploying & Managing an AI solution in production	24th of October	Creating web app and API for ML model inference	Jaroslav Bezdek
7.	Demo Day	31st of October	Presenting your project to others	NA

Mentors

Name	Role	Bio
Jan Maly	Machine Learning Engineer	Jan Maly is passionate about AI and its potential to transform business. He has been in charge of multiple data-driven projects involving Machine Learning and Advanced Data Analytics.
Niek Mereu	Machine Learning Engineer	Niek Mereu is a statistician that tries to find the precarious balance between theory and practice. He enjoys working in projects where the solution requires statistical rigour.
Jaroslav Bezdek	Machine Learning Engineer	Even though Jaroslav is a statistician in heart, he is interested in DevOps, too. He loves clean code and conventions so much that he defined git ones for his team mates.
Lukas Koucky	Machine Learning Engineer	Lukas’s main interests are computer vision, deep learning, and recently he digs more into the whole lifecycle of ML solutions from an idea to deployment in production.
Martina Zapletalova	Data Scientist	Martina is data scientist who, among other things, is also interested in data engineering. Because of that they started to call her full-stack data scientist at STRV.
Jozef Reginac	Data Engineer	Jozef was the first data engineer at STRV. Once a long-time data analyst, he got pissed and became an analytics engineer thanks to dbt. Jozef likes good filter coffee, open source projects and filmography.
Pavel Jezek	Data Engineer	Pavel is the newest member of the team and the second data engineer. He enjoys problem solving and finding efficient solutions.