Machine Learning with Python

In this subject, we focus on the main branches of machine learning. In supervised learning, we explore linear regression and classification, focusing on decision trees for the latter. In unsupervised learning, we learn about k-means clustering.

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Courses

Machine Learning in Python

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

Regression Analysis in Python

1. Regression Analysis in Python

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.

11 Lessons
125 minutes
Created with Sketch. Advanced
CPD Credits
Decision Trees in Python

2. Decision Trees in Python

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.

11 Lessons
100 minutes
Created with Sketch. Advanced
CPD Credits
K-Means Clustering in Python

3. K-Means Clustering in Python

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.

7 Lessons
50 minutes
Created with Sketch. Advanced
CPD Credits
Feature Engineering

4. Feature Engineering

Learn how to reshape your data to make it better fit your model. This course covers 3 types of feature engineering. The first, feature scaling, makes the scale of the data uniform. The second, feature selection, shows us which features contain the most predictive power. The third, dimensionality reduction, allows us to remove a large amount of data while minimizing loss to predictive power.

8 Lessons
50 minutes
Created with Sketch. Advanced
CPD Credits
Advanced Regression

5. Advanced Regression

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.

8 Lessons
50 minutes
Created with Sketch. Advanced
CPD Credits

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