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Reducing Complexity in Data
Janani Ravi
Janani Ravi
This course covers several techniques used to optimally simplify data used in supervised machine learning applications ranging from relatively simple feature selection techniques to very complex applications of clustering using deep neural networks.
Read more
This course covers several techniques used to optimally simplify data used in supervised machine learning applications ranging from relatively simple feature selection techniques to very complex applications of clustering using deep neural networks.
Read more
This course covers several techniques used to optimally simplify data used in supervised machine learning applications ranging from relatively simple feature selection techniques to very complex applications of clustering using deep neural networks.
Machine learning techniques have grown significantly more powerful in recent years, but excessive complexity in data is still a major problem. There are several reasons for this - distinguishing signal from noise gets harder with more complex data, and the risks of overfitting go up as well. Finally, as cloud-based machine learning becomes more and more popular, reducing complexity in data is crucial in making training more affordable. Cloud-based ML solutions can be very expensive indeed.
In this course, Reducing Complexity in Data you will learn how to make the data fed into machine learning models more tractable and more manageable, without resorting to any hacks or shortcuts, and without compromising on quality or correctness.
First, you will learn the importance of parsimony in data, 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 and when to resort to feature selection, employing statistically sound techniques to find a subset of the features input based on their information content and link to the output.
Finally, you will explore how to use two advanced techniques - clustering, and autoencoding. Both of these are applications of unsupervised learning used to simplify data as a precursor to a supervised learning algorithm. Each of them often relies on a sophisticated implementation such as deep learning using neural networks.
When you’re finished with this course, you will have the skills and knowledge of conceptually sound complexity reduction needed to reduce the complexity of data used in supervised machine learning applications.
Register for this course and see more details by visiting:
OpenCourser.com/course/pl8poe/reducing
What's inside
Syllabus
Course Overview
Understanding the Need for Dimensionality Reduction
Using Statistical Techniques for Feature Selection
Reducing Complexity in Linear Data
Read more
Syllabus
Course Overview
Understanding the Need for Dimensionality Reduction
Using Statistical Techniques for Feature Selection
Reducing Complexity in Linear Data
Read more
Reducing Complexity in Nonlinear Data
Dimensionality Reduction Using Clustering and Autoencoding Techniques
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds a strong foundation for beginners who are unfamiliar with feature selection and dimensionality reduction
Develops professional skills or deep expertise in data simplification techniques, a highly relevant area in machine learning
Teaches advanced techniques such as clustering and autoencoding, which are increasingly used in industry for complex data applications
Emphasizes the importance of parsimony and reducing data complexity for effective machine learning
Requires a strong background in machine learning and statistics, which may be a barrier for some learners
May require learners to purchase or access additional resources such as software for advanced techniques
Save this course
Save Reducing Complexity in Data to your list so you can find it easily later:
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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 Reducing Complexity in Data with these
activities:
Review Feature Selection Techniques
Show steps
Review the theory behind popular feature selection techniques to strengthen the foundation for this course.
Browse courses on
Feature Selection
Show steps
-
Summarize the benefits of using feature selection techniques.
-
List at least 3 feature selection techniques.
-
Give an example of how feature selection techniques can be applied in practice.
Follow a Tutorial on Clustering Applications
Show steps
Deepen the understanding of clustering by following a guided tutorial on how they can be used.
Browse courses on
Clustering
Show steps
-
Identify an appropriate tutorial that covers the applications of clustering.
-
Follow the tutorial step-by-step.
-
Reflect on how clustering applications can enhance the course concepts.
Review 'Data Reduction Techniques' by G. John
Show steps
Supplement the course materials by reading a comprehensive book on data reduction techniques.
Show steps
-
Read the chapters relevant to the course topics.
-
Take notes and summarize the key concepts discussed in the book.
-
Reflect on how the book's content complements and expands the course material.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Attend a Workshop on Deep Learning for Dimensionality Reduction
Show steps
Engage in a workshop that delves into the advanced applications of deep learning in the context of dimensionality reduction.
Browse courses on
Deep Learning
Show steps
-
Identify a relevant workshop on deep learning for dimensionality reduction.
-
Register and attend the workshop.
-
Actively participate in the workshop activities and discussions.
Design a Mind Map on Dimensionality Reduction
Show steps
Create a visual representation to connect and visualize key concepts related to dimensionality reduction.
Browse courses on
Dimensionality Reduction
Show steps
-
Brainstorm the main concepts of dimensionality reduction.
-
Organize the concepts into a hierarchical structure.
-
Create a visual representation using a mind mapping tool.
Solve Practice Problems on Autoencoders
Show steps
Reinforce the understanding of autoencoders by solving practice problems and exercises.
Browse courses on
Autoencoders
Show steps
-
Find a set of practice problems or exercises on autoencoders.
-
Attempt to solve the problems independently.
-
Check your solutions against the provided answers or consult with an expert.
Build a Model to Implement Dimensionality Reduction
Show steps
Apply the concepts of dimensionality reduction by building and evaluating a machine learning model that incorporates these techniques.
Browse courses on
Dimensionality Reduction
Show steps
-
Choose a dataset suitable for dimensionality reduction.
-
Select appropriate dimensionality reduction techniques.
-
Build and train a machine learning model incorporating the chosen techniques.
-
Evaluate the model's performance.
Mentor Students in a Data Analysis or Machine Learning Project
Show steps
Reinforce your understanding by guiding others through practical applications of data analysis or machine learning, particularly in projects involving dimensionality reduction.
Show steps
-
Identify students who could benefit from your guidance in data analysis or machine learning projects.
-
Provide mentorship and support throughout the project.
-
Offer constructive feedback and encourage the application of dimensionality reduction techniques.
Participate in a Machine Learning Hackathon with a Focus on Dimensionality Reduction
Show steps
Challenge yourself and apply your knowledge of dimensionality reduction in a competitive environment.
Browse courses on
Machine Learning
Show steps
-
Find a machine learning hackathon that emphasizes dimensionality reduction.
-
Form a team or collaborate with others.
-
Develop a solution that leverages dimensionality reduction techniques.
Review Feature Selection Techniques
Show steps
Review the theory behind popular feature selection techniques to strengthen the foundation for this course.
Browse courses on
Feature Selection
Show steps
Show steps
Show steps
- Summarize the benefits of using feature selection techniques.
- List at least 3 feature selection techniques.
- Give an example of how feature selection techniques can be applied in practice.
Follow a Tutorial on Clustering Applications
Show steps
Deepen the understanding of clustering by following a guided tutorial on how they can be used.
Browse courses on
Clustering
Show steps
Show steps
Show steps
- Identify an appropriate tutorial that covers the applications of clustering.
- Follow the tutorial step-by-step.
- Reflect on how clustering applications can enhance the course concepts.
Review 'Data Reduction Techniques' by G. John
Show steps
Supplement the course materials by reading a comprehensive book on data reduction techniques.
Show steps
Show steps
Show steps
- Read the chapters relevant to the course topics.
- Take notes and summarize the key concepts discussed in the book.
- Reflect on how the book's content complements and expands the course material.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Attend a Workshop on Deep Learning for Dimensionality Reduction
Show steps
Engage in a workshop that delves into the advanced applications of deep learning in the context of dimensionality reduction.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Identify a relevant workshop on deep learning for dimensionality reduction.
- Register and attend the workshop.
- Actively participate in the workshop activities and discussions.
Design a Mind Map on Dimensionality Reduction
Show steps
Create a visual representation to connect and visualize key concepts related to dimensionality reduction.
Browse courses on
Dimensionality Reduction
Show steps
Show steps
Show steps
- Brainstorm the main concepts of dimensionality reduction.
- Organize the concepts into a hierarchical structure.
- Create a visual representation using a mind mapping tool.
Solve Practice Problems on Autoencoders
Show steps
Reinforce the understanding of autoencoders by solving practice problems and exercises.
Browse courses on
Autoencoders
Show steps
Show steps
Show steps
- Find a set of practice problems or exercises on autoencoders.
- Attempt to solve the problems independently.
- Check your solutions against the provided answers or consult with an expert.
Build a Model to Implement Dimensionality Reduction
Show steps
Apply the concepts of dimensionality reduction by building and evaluating a machine learning model that incorporates these techniques.
Browse courses on
Dimensionality Reduction
Show steps
Show steps
Show steps
- Choose a dataset suitable for dimensionality reduction.
- Select appropriate dimensionality reduction techniques.
- Build and train a machine learning model incorporating the chosen techniques.
- Evaluate the model's performance.
Mentor Students in a Data Analysis or Machine Learning Project
Show steps
Reinforce your understanding by guiding others through practical applications of data analysis or machine learning, particularly in projects involving dimensionality reduction.
Show steps
Show steps
Show steps
- Identify students who could benefit from your guidance in data analysis or machine learning projects.
- Provide mentorship and support throughout the project.
- Offer constructive feedback and encourage the application of dimensionality reduction techniques.
Participate in a Machine Learning Hackathon with a Focus on Dimensionality Reduction
Show steps
Challenge yourself and apply your knowledge of dimensionality reduction in a competitive environment.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a machine learning hackathon that emphasizes dimensionality reduction.
- Form a team or collaborate with others.
- Develop a solution that leverages dimensionality reduction techniques.
Career center
Learners who complete Reducing Complexity in Data will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers are responsible for designing, developing, and maintaining machine learning models. They use these models to solve a variety of problems, such as fraud detection, spam filtering, and product recommendations. The skills you will learn in this course, such as clustering and autoencoding, will be essential for your success as a machine learning engineer. This course will help you build a foundation in machine learning and prepare you for a successful career in this field.
Data Scientist
Data Scientist
Data scientists are responsible for using data to solve business problems. They use a variety of techniques, such as machine learning, statistics, and data visualization, to extract insights from data. The skills you will learn in this course, such as feature selection and dimensionality reduction, will be essential for your success as a data scientist. This course will help you build a foundation in data science and prepare you for a successful career in this field.
Data Analyst
Data Analyst
Data analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. They use this information to make recommendations to businesses on how to improve their operations or marketing strategies. The skills you will learn in this course, such as feature selection and dimensionality reduction, will be essential for your success as a data analyst. This course will help you build a foundation in data analysis and prepare you for a successful career in this field.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data. They use this information to make recommendations to businesses and organizations on how to improve their operations or decision-making. The skills you will learn in this course, such as feature selection and dimensionality reduction, will be essential for your success as a statistician. This course will help you build a foundation in statistics and prepare you for a successful career in this field.
Data Engineer
Data Engineer
Data engineers are responsible for designing, building, and maintaining data pipelines. They ensure that data is clean, consistent, and accessible to data analysts and scientists. The skills you will learn in this course, such as feature selection and dimensionality reduction, will be essential for your success as a data engineer. This course will help you build a foundation in data engineering and prepare you for a successful career in this field.
Business Analyst
Business Analyst
Business analysts are responsible for analyzing business data to identify opportunities and solve problems. They use a variety of techniques, such as data mining, statistical analysis, and financial modeling, to extract insights from data. The skills you will learn in this course, such as feature selection and dimensionality reduction, will be essential for your success as a business analyst. This course will help you build a foundation in business analysis and prepare you for a successful career in this field.
Software Engineer
Software Engineer
Software engineers are responsible for designing, developing, and maintaining software applications. They use a variety of programming languages and technologies to create software that meets the needs of users. The skills you will learn in this course, such as feature selection and dimensionality reduction, will be essential for your success as a software engineer. This course will help you build a foundation in software engineering and prepare you for a successful career in this field.
Operations Research Analyst
Operations Research Analyst
Operations research analysts are responsible for using mathematical and statistical techniques to solve business problems. They use a variety of techniques, such as linear programming, simulation, and queuing theory, to optimize business processes. The skills you will learn in this course, such as feature selection and dimensionality reduction, will be essential for your success as an operations research analyst. This course will help you build a foundation in operations research and prepare you for a successful career in this field.
Marketing Analyst
Marketing Analyst
Marketing analysts are responsible for analyzing marketing data to identify opportunities and solve problems. They use a variety of techniques, such as data mining, statistical analysis, and data visualization, to extract insights from data. The skills you will learn in this course, such as feature selection and dimensionality reduction, will be essential for your success as a marketing analyst. This course will help you build a foundation in marketing analysis and prepare you for a successful career in this field.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts are responsible for using mathematical and statistical techniques to analyze financial data. They use a variety of techniques, such as financial modeling, statistical analysis, and data visualization, to extract insights from data. The skills you will learn in this course, such as feature selection and dimensionality reduction, will be essential for your success as a quantitative analyst. This course will help you build a foundation in quantitative analysis and prepare you for a successful career in this field.
Actuary
Actuary
Actuaries are responsible for assessing and managing risk. They use a variety of mathematical and statistical techniques to calculate the probability of future events, such as accidents, illnesses, and deaths. The skills you will learn in this course, such as feature selection and dimensionality reduction, will be essential for your success as an actuary. This course will help you build a foundation in actuarial science and prepare you for a successful career in this field.
Financial Analyst
Financial Analyst
Financial analysts are responsible for analyzing financial data to make recommendations to investors. They use a variety of techniques, such as financial modeling, statistical analysis, and data visualization, to extract insights from data. The skills you will learn in this course, such as feature selection and dimensionality reduction, will be essential for your success as a financial analyst. This course will help you build a foundation in financial analysis and prepare you for a successful career in this field.
Data Architect
Data Architect
Data architects are responsible for designing and implementing data management solutions. They ensure that data is stored securely and efficiently, and that it is available to users when they need it. The skills you will learn in this course, such as feature selection and dimensionality reduction, will be essential for your success as a data architect. This course will help you build a foundation in data architecture and prepare you for a successful career in this field.
Risk Manager
Risk Manager
Risk managers are responsible for identifying, assessing, and managing risk. They use a variety of techniques, such as risk modeling, scenario analysis, and risk assessment, to help organizations minimize their exposure to risk. The skills you will learn in this course, such as feature selection and dimensionality reduction, will be essential for your success as a risk manager. This course will help you build a foundation in risk management and prepare you for a successful career in this field.
Database Administrator
Database Administrator
Database administrators are responsible for managing and maintaining databases. They ensure that data is stored securely and efficiently, and that it is available to users when they need it. The skills you will learn in this course, such as feature selection and dimensionality reduction, will be essential for your success as a database administrator. This course will help you build a foundation in database administration and prepare you for a successful career in this field.
For more career information including salaries, visit:
OpenCourser.com/course/pl8poe/reducing
Reading list
We've selected 11 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 Complexity in Data.
Provides a comprehensive introduction to statistical learning methods, including feature selection, dimensionality reduction, and clustering.
Save
Comprehensive treatment of deep learning, and it provides a strong theoretical foundation for these methods.
Comprehensive treatment of deep learning for natural language processing, and it provides a strong theoretical foundation for these methods.
More advanced treatment of statistical learning methods, and it provides a deeper dive into the mathematical foundations of these methods.
Comprehensive treatment of pattern recognition and machine learning, and it provides a strong theoretical foundation for these methods.
Comprehensive treatment of reinforcement learning, and it provides a strong theoretical foundation for these methods.
Comprehensive treatment of convex optimization, and it provides a strong theoretical foundation for these methods.
Comprehensive treatment of Gaussian processes for machine learning, and it provides a strong theoretical foundation for these methods.
Comprehensive treatment of Bayesian reasoning and machine learning, and it provides a strong theoretical foundation for these methods.
Comprehensive treatment of information theory, inference, and learning algorithms, and it provides a strong theoretical foundation for these methods.
Practical guide to machine learning for data science, and it covers a wide range of topics, including feature selection, dimensionality reduction, and clustering.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/pl8poe/reducing
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Time
200 hours
Level
Intermediate
Via
Pluralsight
Instructor
Janani Ravi
Language
English
This course belongs to a
collection
called
Feature Engineering. It's comprised of six courses:
Good to know
Builds a strong foundation for beginners who are unfamiliar with feature selection and dimensionality reduction
Develops professional skills or deep expertise in data simplification techniques, a highly relevant area in machine learning
Teaches advanced techniques such as clustering and autoencoding, which are increasingly used in industry for complex data applications
Emphasizes the importance of parsimony and reducing data complexity for effective machine learning
Requires a strong background in machine learning and statistics, which may be a barrier for some learners
May require learners to purchase or access additional resources such as software for advanced techniques
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