Learn scikit-learn with online courses and programs
What is scikit-learn?
Scikit-learn, also called sklearn, is a robust open-source machine learning (ML) library for Python that helps you explore and solve complex problems in data analysis, predictive modeling, and pattern recognition. It is built on top of other scientific computing libraries in Python, such as NumPy, SciPy, and matplotlib.
With the help of scikit-learn, you can efficiently implement a wide range of algorithms, including support vector machines (SVM), decision trees, random forests, gradient boosting, and k-means clustering algorithms on large, complex datasets.Footnote 1
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Scikit-learn tutorial curriculum
Are you curious about how to learn scikit-learn? Online courses and programs are available to help you on your educational journey. Those already familiar with Python but new to scikit-learn might find an introductory course helpful to understand how to:
Install scikit-learn and its dependencies
Use data preprocessing techniques, such as handling missing data, scaling features, and encoding categorical variables
Use scikit-learn for linear regression algorithms
Use scikit-learn for logistic regression algorithms
Use scikit-learn for random forest algorithms
Once you master the basics of scikit-learn, you can start learning intermediate concepts, such as how to leverage scikit-learn for PCA and unsupervised learning algorithms, or how to use scikit-learn for the train-test-split technique. Additional machine learning concepts include advanced metrics for classification like precision and recall, and feature selection techniques like variance thresholding and recursive feature elimination.
For learners who want a full command over the complete scikit-learn skillset, an advanced course might cover how to:
Perform text data processing
Work with time series data
Export trained models for production
Handle imbalanced datasets
Use feature engineering techniques
Use hierarchical clustering, density-based (DBSCAN), and spectral clustering techniques
Learning scikit-learn for Python can supplement your knowledge of data science and machine learning. edX offers a variety of educational opportunities for learners interested in studying these topics, as well as a host of other disciplines. A boot camp can provide flexible, hands-on learning for those who want to upskill quickly, while executive education courses are designed for busy professionals. You can also pursue a more comprehensive curriculum in a bachelor’s degree program or, for more advanced learners, a master’s degree program. Find the right learning path for you.
Explore scikit-learn jobs
Several job roles can benefit from having a command over this robust machine-learning library, including:
Python developer: Writes efficient and reusable code in Python. With the help of the development team, they design, develop, and maintain software applications.Footnote 2
Data scientist: Works with large datasets, apply statistical models, and develop machine-learning algorithms to solve business problems.Footnote 3
Data analyst: Collects, cleans, and analyzes data to uncover insights and support decision-making processes.Footnote 4
AI/ML engineer: Designs and implements artificial intelligence and machine learning solutions. These professionals deploy machine learning models into production systems.Footnote 5
Each of these roles will have different education and skills requirements. For example, you may be able to build relevant programming skills in a coding boot camp. However, if you want to land a data analyst job, then a data analytics boot camp would likely be the better choice for upskilling quickly. Depending on the role, some employers may also seek candidates with a bachelor’s or master’s degree in data science. Before deciding on a specific learning path, research the positions you hope to pursue and align your coursework with your career goals.
How to use scikit-learn in your career
Scikit-learn is a versatile library that professionals in various domains can use to enhance their data analysis and machine learning capabilities. Here’s how some roles may use scikit-learn in their respective careers:
Python developers might use scikit-learn to incorporate machine learning capabilities into their applications. They can implement scikit-learn’s algorithms and models to solve classification, regression, and clustering problems.
For data scientists, scikit-learn provides a wide range of machine learning algorithms, preprocessing techniques, and model evaluation metrics. They can use it to explore and analyze datasets, preprocess data, train and fine-tune models, and assess model performance.
ML engineers can leverage scikit-learn to streamline the development and deployment of machine learning models. They can use scikit-learn’s functionalities for data preprocessing, model selection, feature engineering, and hyperparameter tuning.
Are you ready for your next career move? Propel your professional development forward with online courses from edX.