We may earn an affiliate commission when you visit our partners.
Janani Ravi
Dimensionality Reduction is a powerful and versatile machine learning technique that can be used to improve the performance of virtually every ML model. Using dimensionality reduction, you can significantly speed up model training and validation, saving both time and money, as well as greatly reduce the risk of overfitting. In this course, Reducing Dimensions in Data with scikit-learn, you will gain the ability to design and implement an exhaustive array of feature selection and dimensionality reduction techniques in scikit-learn. First, you will learn the importance of dimensionality reduction, and understand the pitfalls of...
Read more
Dimensionality Reduction is a powerful and versatile machine learning technique that can be used to improve the performance of virtually every ML model. Using dimensionality reduction, you can significantly speed up model training and validation, saving both time and money, as well as greatly reduce the risk of overfitting. In this course, Reducing Dimensions in Data with scikit-learn, you will gain the ability to design and implement an exhaustive array of feature selection and dimensionality reduction techniques in scikit-learn. First, you will learn the importance of dimensionality reduction, and understand the pitfalls of...
Read more
Dimensionality Reduction is a powerful and versatile machine learning technique that can be used to improve the performance of virtually every ML model. Using dimensionality reduction, you can significantly speed up model training and validation, saving both time and money, as well as greatly reduce the risk of overfitting. In this course, Reducing Dimensions in Data with scikit-learn, you will gain the ability to design and implement an exhaustive array of feature selection and dimensionality reduction techniques in scikit-learn. First, you will learn the importance of dimensionality reduction, and understand the pitfalls of working with data of excessively high-dimensionality, often referred to as the curse of dimensionality. Next, you will discover how to implement feature selection techniques to decide which subset of the existing features we might choose to use, while losing as little information from the original, full dataset as possible. You will then learn important techniques for reducing dimensionality in linear data. Such techniques, notably Principal Components Analysis and Linear Discriminant Analysis, seek to re-orient the original data using new, optimized axes. The choice of these axes is driven by numeric procedures such as Eigenvalue and Singular Value Decomposition. You will then move to dealing with manifold data, which is non-linear and often takes the form of swiss rolls and S-curves. Such data presents an illusion of complexity, but is actually easily simplified by unrolling the manifold. Finally, you will explore how to implement a wide variety of manifold learning techniques including multi-dimensional scaling (MDS), isomap, and t-distributed Stochastic Neighbor Embedding (t-SNE). You will round out the course by comparing the results of these manifold unrolling techniques with different datasets, including images of faces and handwritten data. When you’re finished with this course, you will have the skills and knowledge of Dimensionality Reduction needed to design and implement ways to mitigate the curse of dimensionality in scikit-learn.
Register for this course and see more details by visiting:
OpenCourser.com/course/se0bcl/reducing
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops knowledge of dimensionality reduction, allowing for faster training and validation of machine learning models
Introduces scikit-learn, a foundational Python library for handling data issues in machine learning
Explodes common misconceptions regarding the curse of dimensionality and provides methods to mitigate them
Exposes students to an extensive suite of feature selection and dimensionality reduction techniques within scikit-learn
Engages with linear data and nonlinear manifold data, providing techniques for addressing the complexities of each
Provides hands-on experience in comparing manifold learning techniques with real-world datasets
Save this course
Save Reducing Dimensions in Data with scikit-learn to your list so you can find it easily later:
Save
Save
Activities
Coming soon
We're preparing activities for
Reducing Dimensions in Data with scikit-learn.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Reducing Dimensions in Data with scikit-learn will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts need to explore and analyze large datasets, where dimensionality reduction plays a crucial role. This course equips you with practical techniques for feature selection and dimensionality reduction, empowering you to uncover insights and draw meaningful conclusions from high-dimensional data.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain ML models, ensuring optimal performance. The course's focus on reducing data dimensionality aligns directly with this role. By mastering techniques like Principal Components Analysis and manifold unrolling, you will gain essential skills for optimizing ML models and mitigating the curse of dimensionality.
Data Scientist
Data Scientist
In the field of Data Science, the curse of dimensionality has significant implications. Reducing Dimensions in Data with scikit-learn delves into techniques to mitigate this challenge using dimensionality reduction. The ability to extract insights from complex, high-dimensional data will make you a highly sought-after Data Scientist.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers work with text data, which often exhibits high dimensionality. This course's coverage of dimensionality reduction, including feature selection and manifold learning, provides valuable skills for extracting meaningful features and patterns from text data.
Computer Vision Engineer
Computer Vision Engineer
The course's focus on reducing dimensionality, particularly in manifold learning techniques like t-SNE, is highly relevant to Computer Vision Engineers. These techniques are used in image recognition, object detection, and facial recognition, where data often takes on complex, non-linear forms.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to biological data, often encountered in high-dimensional settings. This course helps Biostatisticians overcome the curse of dimensionality by providing techniques for feature selection and dimensionality reduction, enabling them to analyze complex biological data effectively.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts rely on data analysis and modeling to make investment decisions. Dimensionality reduction techniques, such as those covered in this course, play a significant role in financial data analysis. By understanding and applying these techniques, you can gain a competitive edge in the field of Quantitative Analysis.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. Dimensionality reduction can help simplify complex data, making it easier to identify patterns and develop effective solutions. This course provides valuable tools for enhancing problem-solving capabilities.
Database Administrator
Database Administrator
Database Administrators may encounter high-dimensional data in database management and optimization. This course introduces dimensionality reduction techniques that can enhance data storage efficiency, improve query performance, and facilitate data analysis.
Software Engineer
Software Engineer
Software Engineers who work on data-intensive applications will benefit from this course. Dimensionality reduction techniques can help optimize algorithms, reduce storage requirements, and improve model performance. This course provides a practical foundation for applying these techniques in software development.
Risk Manager
Risk Manager
Risk Managers assess and mitigate financial and operational risks. By mastering dimensionality reduction techniques, Risk Managers can effectively analyze complex data, identify potential risks, and develop strategies to mitigate their impact.
Actuary
Actuary
Actuaries analyze and manage financial risks using statistical and mathematical models. Dimensionality reduction techniques can help them extract meaningful features from high-dimensional financial data, enabling more accurate risk assessments and pricing models.
Financial Analyst
Financial Analyst
Financial Analysts interpret and communicate financial information to investors and stakeholders. This course provides valuable insights into reducing dimensionality in financial data, enabling Financial Analysts to extract meaningful trends and patterns, supporting accurate financial forecasting and investment decisions.
Market Researcher
Market Researcher
Market Researchers gather and analyze data to understand consumer behavior and trends. Dimensionality reduction techniques can help Market Researchers extract insights from large and complex datasets, enabling them to identify consumer preferences and market opportunities more effectively.
Business Analyst
Business Analyst
Business Analysts help organizations improve their operations and decision-making. Dimensionality reduction techniques can empower Business Analysts to analyze large and complex datasets, uncover hidden patterns, and provide valuable insights to stakeholders.
For more career information including salaries, visit:
OpenCourser.com/course/se0bcl/reducing
Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Reducing Dimensions in Data with scikit-learn.
Provides a clear and concise introduction to dimensionality reduction techniques. It covers a wide range of algorithms, including PCA, LDA, t-SNE, and manifold learning.
Covers feature engineering techniques that can be used to improve the performance of machine learning models. It includes a chapter on dimensionality reduction, which explains the importance of reducing dimensionality and the different techniques that can be used.
Provides a comprehensive introduction to machine learning using Python. It includes a chapter on dimensionality reduction that explains the different techniques that can be used and how to apply them in practice.
Provides a hands-on introduction to data science using Python. It includes a chapter on dimensionality reduction that explains the different techniques that can be used and how to apply them in practice.
Provides a comprehensive introduction to data analysis using Python. It includes a chapter on dimensionality reduction that explains the different techniques that can be used and how to apply them in practice.
Provides a theoretical foundation for machine learning. It includes a chapter on dimensionality reduction that explains the different techniques that can be used and how to apply them in practice.
Provides a comprehensive overview of pattern recognition and machine learning. It includes a chapter on dimensionality reduction that explains the different techniques that can be used and how to apply them in practice.
Provides a comprehensive overview of machine learning. It includes a chapter on dimensionality reduction that explains the different techniques that can be used and how to apply them in practice.
Provides a comprehensive overview of artificial intelligence. It includes a chapter on dimensionality reduction that explains the different techniques that can be used and how to apply them in practice.
Save
Provides a comprehensive overview of deep learning. It includes a chapter on dimensionality reduction that explains the different techniques that can be used and how to apply them in practice.
Provides a comprehensive overview of reinforcement learning. It includes a chapter on dimensionality reduction that explains the different techniques that can be used and how to apply them in practice.
Provides a comprehensive overview of natural language processing. It includes a chapter on dimensionality reduction that explains the different techniques that can be used and how to apply them in practice.
Save
Provides a comprehensive overview of computer vision. It includes a chapter on dimensionality reduction that explains the different techniques that can be used and how to apply them in practice.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/se0bcl/reducing
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
Time
2 hours
Via
Pluralsight
Instructor
Janani Ravi
This course belongs to a
collection
called
Building Machine Learning Solutions with scikit-learn. It's comprised of nine courses:
- Building Your First scikit-learn Solution
- Building Classification Models with scikit-learn
- Building Regression Models with scikit-learn
- Building Clustering Models with scikit-learn
- Building Neural Networks with scikit-learn
- Reducing Dimensions in Data with scikit-learn (viewing)
- Employing Ensemble Methods with scikit-learn
- Preparing Data for Modeling with scikit-learn
- Scaling scikit-learn Solutions
Good to know
Develops knowledge of dimensionality reduction, allowing for faster training and validation of machine learning models
Introduces scikit-learn, a foundational Python library for handling data issues in machine learning
Explodes common misconceptions regarding the curse of dimensionality and provides methods to mitigate them
Exposes students to an extensive suite of feature selection and dimensionality reduction techniques within scikit-learn
Engages with linear data and nonlinear manifold data, providing techniques for addressing the complexities of each
Provides hands-on experience in comparing manifold learning techniques with real-world datasets
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser