Introduction to Python and Machine Learning – Fundamentals of Programming, Data Analysis, and AI

Learn Python in a Hands-On Way and Build Your First Machine Learning Models

In this practical seminar, participants learn the Python programming language from the basics to more advanced techniques and additionally acquire solid foundational knowledge in Machine Learning. By the end of the course, participants will be able to develop Python programs for data analysis, visualization, and initial Machine Learning models.

Your Benefits at a Glance

• Confident use of Python for data processing
• Analyze, prepare, and visualize data
• Understand fundamental Machine Learning concepts
• Apply and interpret classical ML algorithms
• Introduction to Matplotlib, Seaborn, NumPy, Pandas, and scikit-learn
• Practice-oriented exercises with your own datasets
• Step-by-step implementation from Python code to ML model

Seminar Content

Part 1: Python Fundamentals

• Introduction: Python overview, installation & setup (Anaconda, Jupyter), comparison with other languages
• Python basics: Variables, data types, conditions & loops, functions & modules
• Advanced Python techniques: Lists, dictionaries, sets, error handling, list comprehensions, introduction to classes & objects

Part 2: Mathematical Foundations for Machine Learning

• Math essentials: Linear algebra, statistics (mean, variance, correlation), probability
• Functions & visualization: Data analysis and visualization with Pandas, NumPy, Matplotlib & Seaborn

Part 3: Machine Learning Fundamentals

• Introduction to ML: Key concepts and typical application areas
• Classical ML algorithms: K-Nearest Neighbors (KNN), linear and polynomial regression, decision trees
• Model optimization: Understanding and avoiding errors, overfitting, and underfitting
• Working with scikit-learn: Creating pipelines, evaluating models, practical implementation

Prerequisites

• Basic understanding of mathematics (school level)
• No or only minimal Python knowledge required
• Personal laptop with a pre-installed Python environment (setup instructions provided)

Target Audience

• Technically oriented professionals (engineers, analysts, developers)
• Students in technical and scientific fields
• Career changers entering Data Science
• Anyone who wants to learn Python in a practical way and implement their first Machine Learning models