Preparing Data for Modeling with scikit-learn from Pluralsight

This course covers important steps in the pre-processing of data, including standardization, normalization, novelty and outlier detection, pre-processing image and text data, as well as explicit kernel approximations such as the RBF and Nystroem methods.

Even as the number of machine learning frameworks and libraries increases on a daily basis, scikit-learn is retaining its popularity with ease. Scikit-learn makes the common use-cases in machine learning - clustering, classification, dimensionality reduction and regression - incredibly easy. In this course, Preparing Data for Modeling with scikit-learn, you will gain the ability to appropriately pre-process data, identify outliers and apply kernel approximations. First, you will learn how pre-processing techniques such as standardization and scaling help improve the efficacy of ML algorithms. Next, you will discover how novelty and outlier detection is implemented in scikit-learn. Then, you will understand the typical set of steps needed to work with both text and image data in scikit-learn. Finally, you will round out your knowledge by applying implicit and explicit kernel transformations to transform data into higher dimensions. When you’re finished with this course, you will have the skills and knowledge to identify the correct data pre-processing technique for your use-case and detect outliers using theoretically robust techniques.

What's inside

Syllabus

Course Overview

Preparing Numeric Data for Machine Learning

Understanding and Implementing Novelty and Outlier Detection

Preparing Text Data for Machine Learning

Preparing Image Data for Machine Learning

Working with Specialized Datasets

Performing Kernel Approximations

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides a solid foundation for understanding data pre-processing techniques in machine learning

Demonstrates how to identify outliers and apply kernel approximations in scikit-learn

Explores various aspects of data pre-processing, including standardization, normalization, and image and text data handling

Taught by Janani Ravi, an experienced instructor in the field of data science

Suitable for learners with a basic understanding of machine learning and data science

Covers specialized datasets and kernel approximations, providing advanced knowledge for data pre-processing

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Preparing Data for Modeling with scikit-learn with these activities:

Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'

Show steps

Provide you with a comprehensive reference and practical guide to scikit-learn, enhancing your understanding of its capabilities.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

Show steps

Read and study the relevant sections of the book.
Complete the exercises and projects presented in the book.
Reference the book as needed throughout the course.

Examine resources on data scaling

Show steps

Help you understand data scaling techniques and their importance in machine learning.

Browse courses on Feature Scaling

Show steps

Read articles and tutorials on data scaling.
Explore online resources and videos that explain data scaling concepts.
Review course materials on data scaling.

Explore online tutorials and workshops on pre-processing image data with scikit-learn

Show steps

Provide practical guidance on implementing image pre-processing techniques using scikit-learn.

Browse courses on Image Preprocessing

Show steps

Identify reputable online tutorials and workshops.
Follow the instructions and complete the exercises provided in the tutorials.
Test your understanding by applying the techniques to your own image datasets.

Five other activities

Expand to see all activities and additional details

Show all eight activities

Work through guided examples and tutorials on standardization and normalization

Show steps

Provide hands-on experience in applying these techniques to real-world datasets, solidifying your understanding.

Browse courses on Normalization

Show steps

Follow step-by-step guides that demonstrate standardization and normalization techniques.
Complete practice exercises using scikit-learn functions.
Analyze the results of your data scaling operations.

Engage in discussions on the practical applications of kernel approximations

Show steps

Foster collaboration and knowledge exchange by sharing insights and experiences with fellow learners.

Show steps

Join online forums or discussion groups related to kernel approximations.
Participate in discussions, ask questions, and share your own knowledge.
Collaborate with peers on projects or assignments involving kernel approximations.

Develop a cheat sheet or infographic on outlier detection methods

Show steps

Enhance your understanding of outlier detection by creating a visual representation of the concepts and techniques.

Browse courses on Outlier Detection

Show steps

Research different outlier detection methods.
Summarize the key concepts and techniques in a clear and concise manner.
Design and create a visually appealing cheat sheet or infographic.

Develop a data pre-processing pipeline for a specific machine learning task

Show steps

Challenge you to apply your knowledge by designing and implementing a complete data pre-processing solution for a real-world machine learning problem.

Browse courses on Machine Learning Pipeline

Show steps

Define the machine learning task and data requirements.
Identify the necessary data pre-processing steps.
Implement the pre-processing pipeline using scikit-learn.
Evaluate the effectiveness of your pipeline on a test dataset.

Contribute to a scikit-learn community project

Show steps

Offer you the opportunity to engage with the scikit-learn community and make a tangible contribution to the project.

Browse courses on scikit-learn

Show steps

Identify a suitable community project or issue.
Propose your contribution and get it approved.
Implement your contribution according to the project guidelines.
Submit a pull request and work with the community to merge your changes.

Career center

Learners who complete Preparing Data for Modeling with scikit-learn will develop knowledge and skills that may be useful to these careers:

Data Analyst

As a Data Analyst, you will be responsible for collecting and analyzing data to help businesses make informed decisions. By leveraging techniques learned in this course, like pre-processing and outlier detection, you can build a strong foundation for ensuring the accuracy and reliability of your data analysis. Understanding these techniques can provide you with a competitive advantage in identifying trends, patterns, and insights that drive effective decision-making.

See salaries and explore the career path for Data Analyst

Machine Learning Engineer

In the role of a Machine Learning Engineer, you will design and develop machine learning models to solve complex business problems. This course, with its focus on preparing data for modeling, provides a solid foundation in pre-processing techniques, such as standardization and scaling, which are essential for improving the performance and accuracy of machine learning algorithms.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

As a Data Scientist, you will use your expertise in data analysis and machine learning to extract insights from large datasets. The techniques covered in this course, such as novelty and outlier detection, are crucial for identifying unusual patterns and anomalies in data, which can lead to more accurate and reliable insights.

See salaries and explore the career path for Data Scientist

Software Engineer

Software Engineers play a vital role in developing and maintaining software systems. This course provides a foundation in data pre-processing, including image and text data, which is essential for building robust and efficient software systems that handle various types of data.

See salaries and explore the career path for Software Engineer

Research Scientist

Research Scientists conduct research and develop new technologies and products. This course aligns well with this role, providing a strong foundation in preparing data for modeling, which is crucial for conducting rigorous and reliable research.

See salaries and explore the career path for Research Scientist

Business Analyst

Business Analysts help organizations improve their performance by analyzing data and identifying areas for improvement. This course provides a solid foundation in data pre-processing and outlier detection, which are essential for extracting meaningful insights from data and making informed business decisions.

See salaries and explore the career path for Business Analyst

Data Engineer

Data Engineers design and manage data pipelines to ensure the availability and quality of data for analysis. This course provides a strong foundation in data pre-processing, including handling specialized datasets, which is essential for building and maintaining efficient and reliable data pipelines.

See salaries and explore the career path for Data Engineer

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course provides a foundation in data pre-processing, including kernel approximations, which is essential for building accurate and reliable financial models.

See salaries and explore the career path for Quantitative Analyst

Statistician

Statisticians collect, analyze, and interpret data to provide insights and make predictions. This course provides a strong foundation in data pre-processing, including standardization and normalization, which are essential for ensuring the accuracy and reliability of statistical analysis.

See salaries and explore the career path for Statistician

Product Manager

Product Managers oversee the development and launch of new products. This course provides a foundation in data pre-processing, including handling image and text data, which is essential for understanding user needs and developing products that meet those needs.

See salaries and explore the career path for Product Manager

Operations Research Analyst

Operations Research Analysts use mathematical and analytical methods to solve complex business problems. This course provides a foundation in data pre-processing, including kernel approximations, which is essential for building accurate and reliable optimization models.

See salaries and explore the career path for Operations Research Analyst

Risk Analyst

Risk Analysts assess and manage financial and operational risks. This course provides a foundation in data pre-processing, including novelty and outlier detection, which is essential for identifying and mitigating potential risks.

See salaries and explore the career path for Risk Analyst

Actuary

Actuaries use mathematical and statistical methods to assess and manage financial risks. This course provides a foundation in data pre-processing, including standardization and normalization, which is essential for building accurate and reliable actuarial models.

See salaries and explore the career path for Actuary

Financial Analyst

Financial Analysts provide investment advice and make recommendations to clients. This course provides a foundation in data pre-processing, including handling specialized datasets, which is essential for analyzing financial data and making informed investment decisions.

See salaries and explore the career path for Financial Analyst

Auditor

Auditors examine financial records to ensure accuracy and compliance. This course may be useful for Auditors as it provides a foundation in data pre-processing, including novelty and outlier detection, which can assist in identifying anomalies and potential areas of concern in financial data.

See salaries and explore the career path for Auditor