Description

Join our comprehensive exploration into the realm of supervised machine learning with our interactive online training “Mastering Machine Learning: A deep dive into Supervised Learning Techniques”. Take part in an insightful session where we unravel the intricacies of these powerful algorithms, empowering you to understand and apply them in real-world scenarios. Through a combination of theoretical concepts, practical demonstrations, and hands-on exercises, this online training is meticulously designed to enhance your understanding of supervised learning techniques. Immerse yourself in an interactive experience that will elevate your proficiency in leveraging these algorithms for predictive modelling.

This training is part of the learning path “Industrial Data Science” and may be combined with other sessions.

Main learning outcomes:

• k-NN (k-Nearest Neighbors): Understand the principles behind k-NN and demonstrate proficiency in implementing this algorithm for classification and regression tasks.
• Naive Bayes: Explore the Bayesian approach and learn how to apply Naive Bayes for classification tasks, understanding its assumptions and strengths.
• Linear and Multiple Regression: Gain a deep understanding of linear regression and multiple regression, and learn how to model relationships between variables and make predictions.
• Decision Trees/Random Forest: Explore the concepts of decision trees and random forests, and demonstrate the ability to build and interpret these ensemble models for classification and regression.
• Neural Networks: Delve into the world of neural networks, understanding their architecture, activation functions, and training methods, and apply them to complex tasks.

Who is this training for:

• Business analysts
• Industrial, mechanical and civil engineering students and alumni
• Practitioners without knowledge of data science