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Intermediate Level5 Gün

Applied Machine Learning with Python

Learn to develop algorithms that automate prediction, classification, and decision-making tasks by learning from data. Build, optimize, and deploy models into production using Python libraries.

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About This Course

Machine Learning (ML) is a system of algorithms and models that automate tasks such as prediction, classification, decision-making, and pattern recognition by learning from data. This training covers the fundamental principles of machine learning, from supervised and unsupervised learning to modeling, evaluation, optimization, advanced techniques, and deployment processes. Through theoretical knowledge and practical applications, participants will gain the competence to develop powerful models capable of solving real-world problems across various sectors.

Learning Outcomes

Competence to develop robust, end-to-end machine learning models capable of solving real-world problems across various sectors.
Ability to successfully manage complex Feature Engineering processes by handling missing data, using dimensionality reduction, and applying domain knowledge.
The skill to select the most appropriate supervised or unsupervised algorithms (Linear models, SVM, K-Means, etc.) for a business problem and maximize success rates through hyperparameter optimization.

Requirements

Basic knowledge of the Python programming language (Variables, loops, data structures).
Familiarity with foundational mathematics, linear algebra, and statistics/probability concepts.
Analytical thinking skills and a strong interest in deriving insights from data.

Course Curriculum

Course Introduction and Objectives

  • Scope of the training content, objectives, learning outcomes, and expected acquisitions
  • Explanation of the training methodology (theoretical lectures, interactive workshops, case studies, hands-on projects)

Definition and History of Machine Learning

  • Historical development, evolution, and traditional approaches to machine learning
  • Differences between classical ML and modern methods (excluding Deep Learning)

Core Concepts and Terminology

  • Datasets, features, labels, train/test set splits
  • Overfitting, underfitting, bias, variance, hyperparameters, cross-validation

Mathematical and Statistical Foundations

  • Basic statistics: mean, variance, distributions, correlation
  • Probability theory, regression, optimization, linear algebra, and basic calculus

Instructor

Şükrü Yusuf Kaya

Şükrü Yusuf Kaya

AI Consultant & Instructor

Şükrü Yusuf KAYA is an internationally experienced AI Consultant and Technology Strategist leading the integration of artificial intelligence technologies into the global business landscape. With operations spanning 6 different countries, he bridges the gap between the theoretical boundaries of technology and practical business needs, overseeing end-to-end AI projects in data-critical sectors such as banking, e-commerce, retail, and logistics. Deepening his technical expertise particularly in Generative AI and Large Language Models (LLMs), KAYA ensures that organizations build architectures that shape the future rather than relying on short-term solutions. His visionary approach to transforming complex algorithms and advanced systems into tangible business value aligned with corporate growth targets has positioned him as a sought-after solution partner in the industry. Distinguished by his role as an instructor alongside his consulting and project management career, Şükrü Yusuf KAYA is driven by the motto of "Making AI accessible and applicable for everyone." Through comprehensive training programs designed for a wide spectrum of professionals—from technical teams to C-level executives—he prioritizes increasing organizational AI literacy and establishing a sustainable culture of technological transformation.

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