Python ML - Unlock the languages' potential with Machine Learning

Overview

🕮 Off the shelf (OTS)

This course is designed for software developers, data scientists, analysts, and technical professionals who want to gain practical, hands-on experience in building and applying machine learning models using Python. It is particularly relevant for those working with data-driven systems or seeking to integrate predictive analytics into their workflows.

Some background in mathematics (such as statistics, probability, and linear algebra) is recommended. Prior programming experience, particularly in Python, is helpful but not essential.

The Machine Learning with Python Training Course provides a practical and theory-grounded introduction to machine learning techniques using Python. Participants will begin by exploring the core concepts of machine learning, including how to formulate problems, prepare data, and select suitable algorithms. The course covers both supervised and unsupervised learning models in depth, including classification, regression, clustering, and dimensionality reduction techniques. Participants will also explore model evaluation methods and receive a concise introduction to deep learning and neural network fundamentals. Practical exercises throughout reinforce key concepts and provide real-world context.

Key Topics Covered:
• Understanding and framing real-world problems as machine learning tasks
• Data preprocessing and feature engineering with Python and Scikit-learn
• Supervised learning techniques: regression, classification, decision trees, support vector machines
• Unsupervised learning: clustering methods, principal component analysis, and dimensionality reduction
• Model performance assessment: validation techniques, overfitting, underfitting, bias-variance tradeoff
• Introduction to deep learning: neural network structures, backpropagation, activation functions, loss functions

The course is delivered over two days and includes hands-on labs and exercises using real-world datasets to reinforce learning.