Machine Learning with Python

Machine learning is a subset of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed for specific tasks.

In essence, machine learning algorithms use patterns and insights discovered in data to improve their performance over time, without the need for human intervention. This course covers the essential components to machine learning using Python, over 10 days.

This 10-day course covers an overview of machine learning concepts and types, including supervised and unsupervised learning, with hands-on implementation using Python libraries like Scikit-learn. It includes techniques for evaluating and validating models, interpreting metrics, and improving performance, followed by an exploration of unsupervised learning algorithms and model selection strategies.

If there is time, advanced topics such as ensemble learning and optional deep learning concepts will be introduced. However, we do aim for practical application and will cover deployment techniques for deploying machine learning models in production environments.