In this course, We will learn how to use scikit-learn to build predictive models from data. We will explore the basic concepts of machine learning and see how to use scikit-learn to solve supervised and unsupervised learning problems. We will also learn how to evaluate models, tune parameters, and avoid common pitfalls. We will work through examples of machine learning problems using real-world datasets.
15 Labs
Linear Models in Scikit-Learn
Discriminant Analysis Classifiers Explained
Exploring Scikit-Learn Datasets and Estimators
Kernel Ridge Regression
Supervised Learning with Scikit-Learn
Model Selection: Choosing Estimators and Their Parameters
Supervised Learning with Support Vectors
Exploring Scikit-Learn SGD Classifiers
Unsupervised Learning: Seeking Representations of the Data
Implementing Stochastic Gradient Descent
Working with Text Data
Gaussian Process Regression and Classification
Dimensional Reduction with PLS Algorithms
Naive Bayes Example
Decision Tree Classification with Scikit-Learn
This course contains lots of labs for Sklearn, each lab is a small Sklearn project with detailed guidance and solutions. You can practice your Sklearn skills by completing these labs, improve your coding skills, and learn how to write clean and efficient code.
Lab
This course contains lots of challenges for Sklearn, each challenge is a small Sklearn project with detailed instructions and solutions. You can practice your Sklearn skills by solving these challenges, improve your problem-solving skills, and learn how to write clean and efficient code.
Lab
