Enhance your competence as a machine & deep learning professional! Study 30 ECTS in English online and obtain a Diploma in Machine & Deep learning based on the courses completed.
In this 30 credits diploma, you will gain profound knowledge and understanding of machine & deep learning topics and learn to use and apply this knowledge in practice using Python programming language. In addition, you will learn how to use common cloud platforms such Azure and Amazon Web Services for machine learning.
The studies are fully online allowing you to become a student regardless of where you live. This Diploma corresponds to level 7 on the EQF (European Qualifications Framework) of reference.
30 credits study module contents
30 credits study module contains following topics:
1 ECTS credit = 27 hours of workload to a participant
Introduction to Python for Data Science 4 ECTS
This course is designed to introduce the students to the basics of the Python programming environment, including fundamental Python programming techniques used in data science. The course aims to teach students various data visualization, manipulation, and cleaning techniques using the popular Python data science libraries by exploring different types of data. This course provides a unique opportunity for the student to get hands-on experience with popular Python libraries such as NumPy, Pandas, and Matplotlib. By the end of this course, the student will understand the data science workflow, the basics of Python programming, and learns how to take tabular data, as well as clean, manipulate and visualize it, and run basic analyses.
Exploratory Data Analysis with Python, 6 ECTS
Exploratory Data Analysis (EDA) is a combination of multiple techniques that extract valuable insights and meaningful information from the data. The main aim of EDA is to investigate datasets to reveal the underlying structures, challenges, and opportunities of data without attempting to apply any machine learning model. This course will introduce the student to the practical knowledge and the main pillars of EDA, including data exploration, data preparation, data visualization, data relationships, and data clustering using Python programming language. Apart from the intuitions, the student will get familiar with how EDA steps are performed by various Python libraries such as NumPy, Pandas, and Matplotlib.
Fundamentals of Machine Learning, 2 ECTS
Machine Learning has found its way into many of the services we use daily, e.g., Google Search, YouTube, Netflix, and Spotify. It is an application of AI that deals with the challenge of computers performing tasks without being explicitly programmed. This course will introduce the student to the basic principles and concepts of machine learning. Apart from the intuitions, the student will get familiar with the most popular machine learning algorithms, their applications, and their intuitions. After passing this course, the student will be prepared to enter the fantastic world of machine learning towards amazing job positions in the industry.
Machine Learning with Python, 6 ECTS
This course dives into practical machine learning using an approachable and well-known programming language, Python. It provides a unique opportunity for the student to get hands-on experience with popular Python libraries for machine learning such as Numpy, Matplotlib, Pandas, Seaborn, and Scikit-learn. After passing this course, the student will be able to implement his/her own machine learning models (supervised and unsupervised) from scratch, get them to work, and evaluate their performance. Furthermore, common practices and tricks used by data scientists and machine learning experts are also described throughout the course to prepare the student for future job opportunities.
Fundamentals of Deep Learning, 2 ECTS
Deep learning is a new area of machine learning that is concerned with algorithms inspired by the brain's structure and functionality. Deep learning is evolving as one of the crucial practices in industries like manufacturing, hospitality, digital assistants, automotive, etc. This is an introductory course that provides a unique opportunity for the student to get familiar with the basic concepts of deep learning. After passing this course, the student will be familiar with different types of deep learning architectures and models and the intuitions behind them. In fact, the student gets acquainted with variations of the neural network algorithm, which are used for various types of data. Furthermore, the most critical concepts and techniques of deep learning in today's industry have been discussed.
TensorFlow or PyTorch, 5 ECTS
In the first course, you learned the basics of PyTorch; in this course, you will learn how to build deep neural networks in PyTorch. Also, you will learn how to train these models using state of the art methods. You will first review multiclass classification, learning how to build and train a multiclass linear classifier in PyTorch. This will be followed by an in-depth introduction on how to construct Feed-forward neural networks in PyTorch, learning how to train these models, how to adjust hyperparameters such as activation functions and the number of neurons. You will then learn how to build and train deep neural networks such as CNNs—learning how to apply methods such as dropout, initialization, different types of optimizers and batch normalization.
Azure Machine Learning or AWS Machine Learning, 5 ECTS
Azure Machine Learning (ML) is Microsoft's cloud-based service for machine learning implementations, which runs on top of Microsoft Azure cloud and allows for building, deploying, tracking machine learning and deep learning models with lots of capabilities and customizations. This course introduces the primary machine learning tools available on the Azure ML studio and focuses on standardized approaches to data analytics and machine learning implementation (e.g., predictive modeling) based on them. After passing this course, the student will learn to build, train, deploy, automate, manage, and track enterprise-grade machine learning models from scratch in a simplified way using powerful Azure workspaces.
An appropriate foreign higher education degree in Bachelor of Engineering (Information Technology, or near study field).
The Diploma enhances your professional growth in machine learning, deep learning and programming skills with Python. If you continue from Diploma to Master's Degree in Information Technology, your Diploma studies will be recognized as part of the degree studies, and only thing left is the Master’s thesis. Two years relevant work experience (in addition to 240 ECTS degree in Bachelor of Engineering, Information Technology or near study field) will be required for Master’s degree studies. The Master’s degree qualifies for team and project leadership in an IT organization and advancement towards management positions in private or public sector, e.g. IT manager, Team Leader, senior software designer.
Nonstop throughout the year.
Link to the registration will be provided upon request
Manager of Education Export Aija Ahokas
aija.ahokas [at] metropolia.fi