Introduction to Data Science and Supervised Machine Learning Course

Overview

Syllabus 6

1. The Data Science Process

   Learn about the basics of data science and machine learning. Walk through the CRISP-DM process and how you can apply it to many data science problems.

2. Supervised Machine Learning Algorithms

   Explore supervised machine learning algorithms: regression, classification, linear models, decision trees, random forests, and neural networks, with interactive exercises in scikit-learn.

3. Machine Learning Model Evaluation

   Learn why default accuracy metrics can be misleading with real-world datasets, and the alternative metrics you can utilize to communicate the benefits and limitations of your models.

4. Model Interpretability and Fairness

   To apply AI ethically and transparently, you need to understand how your models make decisions and whether their impacts are fair. Apply feature importances, SHAP values, and the Aequitas framework

5. Communicating to Stakeholders

   Create a GitHub repository and Medium blog post to communicate your findings

6. Project: Data Science Blog Post

   Complete the CRISP-DM process with a dataset of your choice, and deploy your findings in the format of a blog post.

Skills you'll practise

Blog postsData storytellingCRISP-DMAI algorithms in PythonGithub portfoliosFeature importance calculationModel bias analysisGradient descentRandom forest modelsTree-based modelsscikit-learnModel evaluationNeural network basicsBasic supervised machine learning

Advantages and disadvantages

Some points apply to every course of this kind; see how we rank.

Before you start

NumPy. Github. Linear regression. Basic calculus. Pandas. matplotlib

Taught by

• David Elliott
• Antje Muntzinger
• Nathan Klarer
• Joshua Bernhard