DS214BKK
Practical Machine Learning
Faculty
Anna Aksenova
Senior Data Scientist at EPAM Systems
Course length
Duration
Total hours
Credits
Language
Course type
Fee for single course
Fee for degree students
Skills you’ll learn
Overview
The course is focused on applying machine learning techniques to real-world problems. We will discuss how to build an ML project from scratch and, more importantly, what can go wrong. The course starts with an overview of data-related issues and processes, then focuses on solving typical machine learning tasks in different domains, and ends with a discussion of various tools that help to improve model or presentation quality. At the end of the course, students are expected to present their own machine-learning project.
Learning highlights
- Learn how to collect and annotate the data for the machine learning project.
- Learn how to choose the particular approach for the business task.
- Learn to apply deep learning techniques to common problems.
- Learn how to use the tools for model development, versioning, and optimisation.
Course outline
15 classes
Session 1
Introduction. When do we need ML in business? Project discussion.
Session 2
Data collection and annotation. Annotation process and quality evaluation. Active learning.
Session 3
Data processing and EDA for different data types.
Session 4
Deep Learning recap. How to build embeddings and choose models?
Session 5
Classification. Problem setup and metrics. From LogReg to deep learning.
Session 6
Information Retrieval. Problem setup and metrics. Vector Search and databases.
Session 7
Information extraction. Named entity recognition. Question answering. Aspect based sentiment analysis.
Session 8
ML project beyond jupyter. DVC, W&B, Optuna
Session 9
From transformers to LLMs. LLM applications. LoRa, P-tuning
Session 10
LLMs continued. RAG, Langchain, Langfuse
Session 11
Project work.
Session 12
Project work.
Session 13
Project work.
Session 14
Project work.
Session 15
Project presentations.
Prerequisites
Machine learning, Python and Basics of deep learning
Methodology
Our sessions consist of two parts: a lecture session with slides and theoretical materials, followed by a practice session devoted to the discussed topic. The practical sessions will include programming tasks and interactive problem-solving based on real-life examples. The last part of the course will be dedicated to the project where the students will create their demo app that will try to solve a real-world problem.
Grading
Anna Aksenova is a Machine Learning and NLP specialist working on enterprise-scale agentic systems and Retrieval-Augmented Generation solutions, with a focus on sales and finance domains. Alongside her industry work, she has led applied research and development in healthcare-related Horizon Europe projects. Anna holds a Master’s degree in Data Science, Machine Learning, and AI from Aalto University, where her thesis focused on training a multilingual large language model for European languages. She teaches Machine Learning and NLP courses at both university and corporate levels and supervises graduate students’ research projects.
See full profileApply for this course
Practical Machine Learning
by Anna Aksenova
Total hours
45 Hours
Dates
Jun 17 - Jul 05, 2024
Fee for single course
€1500
Fee for degree students
€750
How to secure your spot
Complete the form below to kickstart your application
Schedule your Harbour.Space interview
If successful, get ready to join us on campus
FAQ
Will I receive a certificate after completion?
Yes. Upon completion of the course, you will receive a certificate signed by the director of the program your course belonged to.
Do I need a visa?
This depends on your case. Please check with the Spanish or Thai consulate in your country of residence about visa requirements. We will do our part to provide you with the necessary documents, such as the Certificate of Enrollment.
Can I get a discount?
Yes. The easiest way to enroll in a course at a discounted price is to register for multiple courses. Registering for multiple courses will reduce the cost per individual course. Please ask the Admissions Office for more information about the other kinds of discounts we offer and what you can do to receive one.