Introduction
In this course, you will learn how to use scikit-learn to build predictive models from data. You will explore the basic concepts of machine learning and see how to use scikit-learn to solve supervised and unsupervised learning problems. You will also learn how to evaluate models, tune parameters, and avoid common pitfalls. You will work through examples of machine learning problems using real-world datasets.
🎯 Tasks
In this course, you will learn:
- How to use linear models, linear and quadratic discriminant analysis, and statistical learning techniques to solve machine learning problems
- How to apply kernel ridge regression, support vector machines, and stochastic gradient descent for supervised learning tasks
- How to perform unsupervised learning, including seeking representations of the data
- How to work with text data and use Gaussian processes and cross decomposition techniques
- How to use naive Bayes and decision trees for classification tasks
🏆 Achievements
After completing this course, you will be able to:
- Implement a variety of machine learning algorithms using scikit-learn
- Evaluate and tune the performance of your models
- Apply appropriate machine learning techniques to solve real-world problems
- Understand the strengths and limitations of different machine learning approaches
