Python ML

In this subject, we learn how to use Python to deploy machine learning algorithms. This course is designed for people with no programming experience. We start with the basics of Python before moving onto courses dedicated to building machine learning algorithms for a variety of business use cases.

Categories

  • Python Fundamentals
  • Machine Learning in Python

Courses

Machine Learning in Python

In this track, we explore how to use Python to build linear regression, decision trees, and K-means clustering algorithms.

Linear Regression in Python

1. Linear Regression in Python

In this course, we deploy our first machine learning algorithm. This is a supervised machine learning algorithm meaning that it can be used to predict future values. We’ll discover the logic behind linear regression and then explore how it can be deployed to predict numeric values. We’ll turn our attention to a specific business use case that will become the basis of our first linear regression model.

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CPD Credits
Decision Trees in Python

2. Decision Trees in Python

In this course, we focus on classification. These algorithms are used to predict outcomes that can only have a few possible variations. For example, it can be used by a subscription-based company to predict whether a customer will cancel, stay, downgrade or upgrade. There are many classification algorithms, but we’ll focus on Decision Trees which are both easy to understand and to visualize.

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CPD Credits
Clustering in Python

3. Clustering in Python

In this course, we deploy a clustering algorithm. Unlike supervised learning techniques, this can't make future predictions for a specific value, but it can be used to find patterns within our data. We’ll use these to segment customers into separate clusters. This will allow the business to tailor its responses to these customers. There are many clustering algorithms, but we’ll focus on K-Means clustering which is both easy to understand and to visualize.

Coming Soon
Created with Sketch. Advanced
CPD Credits