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.