Introduction to Machine Learning

Contents

Introduction to Machine Learning#

This session introduces the principles of machine learning and guides you through the foundations of core concepts, algorithms, and practical applications.

Course Overview#

  • ML vs. AI vs. Deep Learning – key differences and connections

  • ML Workflow – problem definition → data preparation → modeling → evaluation

  • Training & Optimization – how models learn from data

  • Evaluation – performance metrics for classification tasks

  • Logistic Regression – fundamental binary classification model

  • Underfitting, Overfitting & Regularization – bias-variance tradeoff

  • Hands-on Example – classification on the PIMA Indians Diabetes dataset

You can download the slides here

  • notebooks – PIMA classification Jupyter notebooks here

  • datasets – PIMA dataset here

references

  • Scikit-learn Documentation

  • Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow (Third Edition) – Aurelien Geron

  • Introduction to Machine Learning (Forth Edition) – Ethem Alpaydin

Contents