How does a computer learn to estimate the price of a house it has never seen, or tell a spam email from a genuine one, without a programmer writing a rule for every possible case? The answer lies in machine learning, a field that lets systems find patterns in data rather than follow fixed instructions.

This specialization builds that understanding step by step, moving from a single input variable to full multi-feature models and then to classification problems. By the end, participants will have the practical skills to apply machine learning to real business challenges, whether they are entering the AI field for the first time or formalizing experience they already have.

Knowledge and Benefits:

By the end of this course, participants will be able to:

• Build a broad understanding of machine learning, its concepts and its core methods.
• Implement linear regression models, including gradient descent, feature scaling and polynomial regression.
• Apply logistic regression to classification problems and interpret the decision boundary.
• Recognize the problem of overfitting and address it through regularization.
• Gain hands-on, real-world experience applying these techniques to practical projects.

Who Should Attend?

• Software engineers.
• Anyone with a strong interest in artificial intelligence.

Module 1: How Machines Learn From Data

• Applications of machine learning.
• What machine learning is, and the difference between supervised and unsupervised learning.
• Getting started with Jupyter Notebooks.

Module 2: Predicting Numbers: Linear Regression Foundations

• The linear regression model and the cost function formula.
• Building intuition for and visualizing the cost function.
• Gradient descent: implementation, intuition and choosing a learning rate.
• Running gradient descent for linear regression.

Module 3: Scaling Up: Regression with Multiple Variables

• Working with multiple features and vectorization.
• Gradient descent for multiple linear regression.
• Feature scaling and checking gradient descent for convergence.
• Feature engineering and polynomial regression.

Module 4: Sorting Things Out: Classification and Avoiding Overfitting

• Logistic regression and the decision boundary.
• The cost function for logistic regression, and its simplified form.
• Gradient descent implementation for classification.
• The problem of overfitting, and addressing it through regularized linear and logistic regression.