Machine Learning with Python and SciKit Learn

Understanding Artificial Intelligence and Machine-Learning

• In this introduction, you'll learn how Machine Learning (ML) fits in the overall field of Artificial Intelligence, the difference between Supervised Learning and Unsupervised Learning, and explore the circumstances where you might find Machine Learning useful.

Supervised Learning: 

• Classification

  • Learn how to train and test a model such as K-Nearest Neighbours to classify data. You'll also learn techniques to measure and tune the performance of your classifier in order to make the best possible predictions.

• Regression

  • When data is continuous and doesn't fit neatly into categories, a regression model may be a better choice than a classifier. In addition to training and testing your model, you'll learn about cross-validation and regularisation.

• Decision Trees and Random Forests

• Bayesian Data Analysis

Unsupervised Learning: 

• Clustering

  • Find underlying patterns and clusters in unlabeled datasets.

• Principal Component Analysis

Pipelines

• Learn how to build pipelines.

Preprocessing

• Improve your model's performance by preprocessing the data.

Data Analysis Workshop

• Put your new skills into practice!

Next Steps

• We'll close with an overview of some other commonly used Machine Learning algorithms, and the scenarios in which they may be useful. You'll also get a quick introduction to Deep Learning and how it differs from Machine Learning.