About This Course
Scikit-learn has evolved as a robust library for machine learning applications in Python with support for a wide range of supervised and unsupervised learning algorithms.
This course begins by taking you through videos on evaluating the statistical properties of data and generating synthetic data for machine learning modeling. As you progress through the sections, you will come across videos that will teach you to implement techniques such as data pre-processing, linear regression, logistic regression, and K-NN. You will also look at Pre-Model and Pre-Processing workflows, to help you choose the right models. Finally, you'll explore dimensionality reduction with various parameters.
- Certificate will provided in this course on Completion
- Full lifetime access
- Available on Mobile & Laptop
What Students Will Learn In Your Course?
- Build predictive models in minutes by using scikit-learn
- Understand the differences and relationships between Classification and Regression
- Use distance metrics to predict in Clustering
- Find points with similar characteristics with Nearest Neighbors
- Use automation and cross-validation to find the best model and focus on it for a data product
Are There Any Course Requirements Or Prerequisites?
A sound knowledge of Anaconda and its libraries such as NumPy and Sckit-learn is required.
Who Are Your Target Students?
This course is ideal for the Machine Learning beginner with some basic Python experience.
- 19 lectures