- Introduction to Supervised Machine Learning in Python
- Introduction to Unsupervised Machine Learning in Python
- Telling Stories Using Data Visualization and Information Design
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Introduction to Supervised Machine Learning in Python
Course Overview
n this hands-on course, Introduction to Supervised Machine Learning in Python, you’ll learn to build and optimize classification models using Python libraries like scikit-learn and pandas. Starting with the basics of supervised learning, you’ll develop a machine learning workflow that addresses real-world classification problems. The course covers key topics such as data preprocessing, feature selection, model validation, cross-validation, and hyperparameter tuning, with practical exercises to reinforce your understanding.
By the end of the course, you’ll apply your skills to a capstone project predicting heart disease, showcasing your ability to manage end-to-end machine learning projects. Through step-by-step guidance and immediate feedback, you’ll gain the confidence to tackle classification tasks with accuracy and performance in mind.
Course Outline
| a) The Machine Learning Workflow |
| b) Introduction to K-Nearest Neighbors |
| c) Evaluating Model Performance |
| d) Hyperparameter Optimization |
| e) Guided Project: Predicting Heart Disease |
Projects in this course
For this project, we’ll take on the role of a data scientist at a healthcare solutions company to build a model that predicts a patient’s risk of developing heart disease based on their medical data.
Introduction to Unsupervised Machine Learning in Python
Course Overview
In this course, you’ll dive into the fundamentals of the k-means algorithm and its application in data segmentation. Starting with the basics, you’ll learn how to build a k-means model and use it to segment data effectively. The course also covers essential clustering activities, such as determining the optimal number of clusters and creating clusters using the k-means algorithm in scikit-learn.
You’ll gain practical experience interpreting the results of a k-means model and understanding its real-world applications. With hands-on exercises, you’ll build confidence in working with clustering techniques and refining your models for better outcomes.
Best of all, the course emphasizes learning by doing. You’ll practice directly in the browser and receive instant feedback to reinforce your skills. By the end, you’ll complete a project on credit card customer segmentation, showcasing your ability to apply k-means clustering in a meaningful context.
Course Outline
| a) Identified applications of unsupervised machine learning |
| b) Implement a basic k-means algorithm |
| c) Evaluate and optimize the performance of a k-means model |
| d) Visualize the model |
| e) Build a k-means model using scikit-learn |
| f) Guided Project: Automotive Parts Classification |
Projects in this Course
For this project, we’ll play the role of a data scientist at a credit card company to segment customers into groups using K-means clustering in Python, allowing the company to tailor strategies for each segment.
Telling Stories Using Data Visualization and Information Design
Course Overview
This course is designed for intermediate Python users, building on the foundational concepts covered in our earlier Python visualization lessons. You’ll explore how to leverage powerful Python libraries like Matplotlib and Seaborn to transform raw data into visually compelling and actionable insights. By mastering common data visualization techniques, you’ll create beautiful, meaningful visuals that bring your data to life.
The course emphasizes a hands-on approach, allowing you to practice directly in the browser and receive immediate feedback. Through guided exercises, you’ll gain confidence in applying visualization techniques to real-world datasets, ensuring your skills are both practical and impactful.
To solidify your learning, you’ll work on several guided projects based on realistic business scenarios. These projects will not only enhance your understanding but also help you build a robust portfolio to showcase your expertise, making you interview-ready for your next career opportunity.
Course Outline
| a) Design for an Audience |
| b) Storytelling Data Visualization |
| c) Gestalt Principles and Pre-Attentive Attributes |
| d) Matplotlib Styles: FiveThirtyEight Case Study |
| e) Guided Project: Storytelling Data Visualization on Heart Attack |
Projects in this course
Storytelling Data Visualization on Exchange Rates. For this project, we’ll assume the role of a data analyst tasked with creating an explanatory data visualization about Euro exchange rates to inform and engage an audience.