Data Science and AI

About the course

Výuka probíhá v angličtině.

The course will be held in English.

Building on the foundational knowledge gained in our Introduction to Data Science and Data Science Foundations in Python courses, this course provides advanced knowledge of machine learning and AI concepts and techniques – delving deeper into the intricacies of supervised and unsupervised learning algorithms, model evaluation and selection, and the fundamentals of deep learning and neural networks.

You'll explore different types of data, including numerical data, text (LLMs) and images (computer vision), and gain practical insights into implementing machine learning models using libraries such as scikit-learn. You'll also learn about reinforcement learning, understanding its applications and principles. Throughout the course, ethical considerations and best practices in AI are emphasized, ensuring that you're equipped to navigate the ethical complexities of the field responsibly.

This course is part of the Data & AI Scientist career learning path.

Who is this course for?

Prerequisite: Data Science Foundations in Python

• For women who are interested in advanced knowledge of ML and AI concepts and techniques.
• For women who seek to become a Data Scientist.
• For women who can communicate in English.
  
  

By the end of the course you will:

• Be able to work with unstructured data (text, image or audio),
• understand advanced concepts and methods used in AI, including deep learning and NNs,
• understand key MLOps tools such as version control systems (git).
  
  

Course content

• Supervised and unsupervised learning algorithms, model evaluation and selection, and fundamentals of deep learning, neural networks:
  • Numerical data, simple NNs
  • Text (LLMs)
  • Images (computer vision)
• Reinforcement Learning
• Practical implementation of ML models using libraries such as scikit-learn
• Ethical considerations and best practices in AI

Note: The course is still in preparation so there may be minor changes to the final content.

How do you finish the course?

You will receive a certificate, if you:

• meet the attendance requirements,
• submit the homework assignments.
  
  

Related courses

• Introduction to Data Science
• Data Science Foundations in Python

This course is part of the Data & AI Scientist career learning path.