Understanding Machine Learning Models and Analyzing Data Professionally

In this hands-on seminar, participants learn the mathematical foundations and methods required for Machine Learning. Building on existing Python skills, the course covers essential ML algorithms, data analysis and visualization techniques, as well as model optimization. After completing the seminar, participants will be able to independently implement simple ML models and analyze data effectively.

Your Benefits at a Glance

• Build a solid mathematical foundation for Machine Learning
• Analyze and visualize data using Python, Pandas, NumPy, Matplotlib, and Seaborn
• Apply and evaluate classical ML algorithms
• Optimize models and avoid common pitfalls such as overfitting
• Practice-oriented exercises with your own datasets
• Step-by-step implementation of ML workflows in Python

Seminar Content

Part 1: 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 2: 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

Note

• This course is the second part of the 5‑day seminar “Introduction to Python and Machine Learning.”
• The preceding course “Introduction to Python” can be booked separately; the combined package is available at a reduced rate.

Prerequisites

• Basic understanding of mathematics (school level)
• Python skills equivalent to the “Introduction to Python” course
• Personal laptop with a pre-installed Python environment (setup instructions provided)

Target Audience

• Technically oriented professionals (engineers, analysts, developers)
• Students in technical or scientific fields
• Career changers entering Data Science
• Anyone who wants to understand and apply Machine Learning models and analyze data professionally