Artificial Intelligence (AI) and Machine Learning (ML) are integral parts of today’s IT world. Their potential is vast, but sometimes unrealistic expectations are placed on them. For those who want to explore and implement machine learning in their projects, an introductory course in Machine Learning with Python offers an overview of both the fundamentals and limitations of AI.
The course combines programming exercises in Python with theory, demonstrations, and hands-on experiments. Across five sessions, an expert provides clear explanations, application examples, interesting stories, and impressive demonstrations, even beyond the Python context.
This course is aimed at individuals in the software field with some programming experience (preferably in Python) who want to take their first steps in machine learning and gain an overview. After completing all sessions, participants will understand different types of machine learning algorithms, know how to realistically apply AI solutions, and master data preparation, classical statistical methods, and artificial neural networks in Python.
The sessions are scheduled as follows:
- April 9, 2025: Python basics and data preparation with NumPy and pandas
- April 16, 2025: Data preparation and visualization with pandas and Matplotlib
- May 7, 2025: Fundamentals and supervised learning with scikit-learn
- May 14, 2025: Decision trees, unsupervised learning, and reinforcement learning
- May 21, 2025: Deep Learning: Artificial neural networks with Keras and TensorFlow
Each session lasts four hours, from 9 AM to 1 PM. Participants can look forward to a lot of practice and interaction. They also have the opportunity to review and deepen what they have learned with all recordings and materials afterward.
Questions are answered directly in the live chat, and participants can also discuss topics with each other. Access to videos and exercise materials is included. For more information and tickets, interested parties can visit the course’s website.
Machine learning involves using algorithms to find patterns in data and make predictions or decisions based on that data. It is a subset of AI and focuses on building systems that learn from data. Python is a popular language for machine learning due to its simplicity and the availability of numerous libraries and frameworks.
NumPy and pandas are libraries in Python used for data manipulation and analysis. NumPy provides support for large, multi-dimensional arrays and matrices, along with mathematical functions to operate on them. Pandas is used for data manipulation and analysis, offering data structures and operations for manipulating numerical tables and time series.
Data visualization is crucial in machine learning to understand data patterns and trends. Matplotlib is a plotting library in Python that provides an object-oriented API for embedding plots into applications.
Scikit-learn is a machine learning library for Python that provides simple and efficient tools for data mining and data analysis. It supports various supervised and unsupervised learning algorithms.
Supervised learning involves training a model on a labeled dataset, which means the model learns from input-output pairs. Unsupervised learning, on the other hand, deals with unlabeled data and tries to find hidden patterns or intrinsic structures in the data.
Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward.
Deep learning is a subset of machine learning that uses neural networks with many layers (hence “deep”) to model complex patterns in data. Keras and TensorFlow are popular deep learning frameworks in Python.
By understanding these concepts and tools, individuals can harness the power of machine learning to solve real-world problems efficiently and effectively. The course aims to equip participants with the necessary skills to apply machine learning techniques in their projects and explore the vast possibilities AI offers.