AI & ML with Python

Artificial Intelligence (AI) has the potential to transform many business sectors and processes. This course will introduce the fundamental concepts behind AI, including what it is and where it's used. We'll also discuss some of the issues surrounding AI, such as ethical and privacy concerns, and consider the possible future developments of AI, including the possible consequences for jobs and even societies. This course is entirely non-technical and requires no prior knowledge of AI.

Artificial Intelligence (AI) has a variety of applications in business, such as text or image analysis, speech recognition, and behavior or sentiment analysis. In this course, we'll explain some of these applications, outlining how they're used, what algorithms they involve, and what results you might expect. However, we'll stop short of coding or implementing any AI solutions, meaning this course will remain relatively non-technical.

Artificial Intelligence techniques can help you predict the key values that help you make important business decisions, but to do so you need to understand what AI models can do this and how to evaluate them. In this course, you'll learn how to predict numeric values using regression techniques.

Not all business situations boil down to numbers. Sometimes you need to predict an outcome that has only a few possible values. In this course, you'll learn how to predict scenarios with a limited number of outcomes using classification techniques.

Learn how AI techniques can discover hidden patterns and trends that are not immediately obvious, but contain valuable insights. In this course, you'll learn how unsupervised learning techniques are used to find clusters and associations in datasets.

Create your first machine learning algorithm with Python. This course will teach you how to predict future numeric values based on existing data. You'll achieve this by applying linear regression analysis to a business scenario.

Help a Japanese car company re-enter the US market. On their previous attempt, sales were too low as consumers found their cars to be overpriced. They've asked for your help using machine learning to determine what factors can inform price-setting for the US market.

Learn how to build classification algorithms that can predict outcomes that can only have a few possible variations. There are many classification algorithms, but we'll focus on Decision Trees which are both easy to understand and to visualize.

Learn how to deploy the k-means clustering algorithm. We'll use these to segment customers into separate clusters which will allow the associated business to tailor its responses to these customers.

Learn about more advanced methods for applying regression models. The course starts with nonparametric forms of regression such as decision tree regression, k-nearest neighbors regression, and support vector regression. The course then finishes by covering logistic regression.

Build ML algorithms that can detect transaction anomalies or even determine the risk of a loan defaulting. Expand your classification skills with two new algorithms, Naïve Bayes and Support Vector Machines.

Help a bank identify the risk of customers defaulting on their loan repayments. You’ll use several classification algorithms in order to build a model that can separate likely defaulters from customers who’ll continue paying back their loans.

Split your data into clusters using advanced techniques. You'll learn Single Linkage Clustering and Soft Clustering, enabling you to identify fraudulent activity, segment customers, and much more.