- This event has passed.
ONLINE COURSE – Machine Learning using Python (MLUP01) This course will be delivered live
10 February 2025 - 14 February 2025
£480.00
Event Date
Monday, February 10th, 2025
Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Course Program
TIME ZONE – Ireland local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.
Course Details
This course comprehensively introduces Machine Learning, covering theoretical foundations and practical applications. It focuses on crucial machine learning techniques such as supervised and unsupervised learning algorithms, using Python and popular libraries like Scikit-learn, TensorFlow, and Keras. The course emphasises hands-on projects to apply learned concepts to real-world ecological problems. By the end of the course, participants should:
- Understand fundamental concepts in machine learning, including supervised and unsupervised learning.
- Be able to preprocess data for machine learning tasks.
- Understand key algorithms for regression, classification, clustering, and dimensionality reduction.
- Gain proficiency in building neural networks and deep learning models.
- Be familiar with model selection techniques and hyperparameter tuning.
- Have confidence in deploying machine learning models in production environments.
- Be able to apply machine learning techniques to solve real-world problems through hands-on projects.
Intended Audiences
- Academics and post-graduate students working on machine learning projects.
- Data scientists and applied researchers in public or private sectors who need to implement machine learning solutions.
- Professionals looking to integrate machine learning into their workflows or enhance their understanding of AI technologies.
- Ecologists looking to understand the basic principles of Machine learning and implement them in their research.
Venue
Course Information
Availability – TBC
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English
Teaching Format
Introductory and Intermediate-level lectures interspersed with hands-on projects. The instructors will provide datasets, but participants are welcome to bring their data. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session.
All sessions will be video recorded and made available to all attendees as soon as possible. If some sessions are not at a convenient time due to different time zones, attendees are encouraged to join as many of the live broadcasts as possible.
At the start of the first day, we will ensure that everyone is comfortable with how Zoom works, and we’ll discuss the procedure for asking questions and raising comments.
Assumed quantitative knowledge
A basic understanding of statistical and mathematical concepts, such as linear algebra.
Assumed computer background
Day one will cover the basics of Python for the module. However, some familiarity with any other programming language is welcome.
Equipment and software requirements
Although not absolutely necessary, a large monitor and a second screen could improve the learning experience. Participants are also encouraged to keep their webcams active to increase their interaction with the instructor and other students.
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.
COURSE PROGRAMME
Monday 10th
Day 1: A Short Course in Python Basics (9:30 – 17:30)
This day provides participants with the foundational Python skills required for machine learning tasks. This day is designed for beginners or those needing a refresher in Python programming.
- Section 1 (Python Essentials for Machine Learning): This section focuses on Python syntax, variables, data types, conditionals (`if`, `else`, `elif`), loops (`for`, `while`), and writing reusable code using functions.
- Section 2 (Data Structures and File Handling in Python): Focuses on lists, dictionaries, tuples, sets, and reading/writing files (e.g., CSVs) for data manipulation.
Tuesday 11th
Day 2: Fundamentals of Machine Learning (9:30 – 17:30)
This day focuses on the theoretical foundations of machine learning, detailing the application of learning algorithms in preparation for the practical examples in Python.
- Section 3 (Introduction to Machine Learning): This section covers the definition of Machine learning, types of Learning (Supervised, Unsupervised, Reinforcement, Semi-Supervised), applications of Machine Learning and an overview of Python libraries for ML (NumPy, scikit-learn)
- Section 4 (Fundamental learning algorithms): This section explores the available learning algorithms and focuses on their applications. We will also discuss the application of different algorithms with practical examples in Ecology.
Wednesday 12th
Day 3: Statistical Learning Theory (9:30 – 17:30)
This day focuses on the theoretical foundations of Statistical Learning Theory (SLT) and illustrates their practical implications.
- Section 5 (Important Definitions on SLT): In this section, we will explore the concept of Statistical Learning Theory and its implications for classification tasks in supervised learning settings, highlighting its importance for machine learning practitioners.
- Section 6 (Practical implications of the SLT): This section provides a detailed explanation of the practical consequences of statistical learning theory based on Vapniks’ findings and using Support Vector Machines as a helpful example in Python
Thursday 13th
Day 4: Classification boundaries and the power of Deep Neural networks (9:30 – 17:30)
This day introduces participants to the core libraries used in machine learning tasks. scikit-learn is used to implement machine learning algorithms, and TensorFlow is used to build deep learning models.
- Section 7 (Classification with various learning algorithms): Offers a step-by-step guide to building learning algorithms using scikit-learn.
- Section 8 (Building Deep Learning Models with TensorFlow/Keras): Offers a step-by-step guide to building CNN models for image classification using TensorFlow/Keras.
Friday 14th
Day 5: The Machine Learning Pipeline (9:30 – 17:30)
Participants will learn about the end-to-end workflow of a typical machine learning project using ecological datasets as an illustration.
Section 9 (Preprocessing data and selecting algorithms): This section focuses on preprocessing techniques in OpenCV before feeding images into TensorFlow models for training. An entomological example illustrating the Machine Learning Pipeline will be used.
Section 10 (The Complete Machine Learning Pipeline: From Classification to Evaluating Learning): Covers the end-to-end machine learning workflow, including using the data preprocessed data and creating scikit-learn pipelines to automate critical aspects of the workflow.
Course Instructor
Dr. Gabriel Palma
Gabriel R. Palma obtained a B.Sc. in Biology from the University of São Paulo, Brazil in 2021. He is currently a PhD researcher at the Hamilton Institute at Maynooth University, Ireland, funded by the Science Foundation Ireland’s Centre for Research Training in Foundations of Data Science. His research interests include statistical and mathematical modelling, machine vision, machine learning, and applications to ecology and entomology. His personal webpage can be found here