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Snehan Kekre
Welcome to this 2 hour long project-based course on Principal Component Analysis with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals. By the time you complete this project, you will be able to implement and apply PCA from scratch using NumPy in Python, conduct basic exploratory data analysis, and create simple data visualizations with Seaborn and Matplotlib. The prerequisites for this project are prior programming experience in Python and a basic understanding of machine learning theory.
Read more
Welcome to this 2 hour long project-based course on Principal Component Analysis with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals. By the time you complete this project, you will be able to implement and apply PCA from scratch using NumPy in Python, conduct basic exploratory data analysis, and create simple data visualizations with Seaborn and Matplotlib. The prerequisites for this project are prior programming experience in Python and a basic understanding of machine learning theory.
Read more
Welcome to this 2 hour long project-based course on Principal Component Analysis with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals. By the time you complete this project, you will be able to implement and apply PCA from scratch using NumPy in Python, conduct basic exploratory data analysis, and create simple data visualizations with Seaborn and Matplotlib. The prerequisites for this project are prior programming experience in Python and a basic understanding of machine learning theory.
This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, NumPy, and Seaborn pre-installed.
Register for this course and see more details by visiting:
OpenCourser.com/course/0u08id/principal
What's inside
Syllabus
Project: Principal Component Analysis with NumPy
Welcome to this 2 hour long project-based course on Principal Component Analysis with NumPy and Python. In this project, you will do all the machine learning without using any of the popular machine learning libraries such as scikit-learn and statsmodels. The aim of this project and is to implement all the machinery of the various learning algorithms yourself, so you have a deeper understanding of the fundamentals. By the time you complete this project, you will be able to implement and apply PCA from scratch using NumPy in Python, conduct basic exploratory data analysis, and create simple data visualizations with Seaborn and Matplotlib.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops PCA algorithms and implementations that are industry standard
Students who understand Python and machine learning theory can enroll
The course includes hands-on projects
Students must be familiar with NumPy
Save this course
Save Principal Component Analysis with NumPy to your list so you can find it easily later:
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Reviews summary
Well-received practical introduction to pca with numpy
Learners say this is a well-received practical course for beginners to learn Principal Component Analysis (PCA) with NumPy. Learners appreciate the clear explanations, easy-to-understand examples, and guided projects that provide plenty of hands-on practice. Overall, this course is recommended for those looking to enhance their understanding of PCA fundamentals.
Instructor provides clear explanations that are easy to understand.
"The instructor was good with explanation ."
"Very good explanation with demo. Thank you."
"Excellence experiece, good content for begineers, thanx coursera."
Course provides engaging projects for learners to practice PCA.
"The couse was made very simple."
"Very Satisfactory"
"Good project"
"Great experience"
"Good Introductory project to gain insights into PCA using Numpy and python. "
"Learned Applying PCAConcise course.Liked the method of teaching."
Learners may encounter technical issues with Coursera's platform.
"The platform is really hard to use, the screen is small, and there're lags when I'm typing into the jupyter notebook on the virtual desktop. "
Course is a bit short.
"The course felt a bit too short and the time allotted for the guided project was barely enough to complete it in time leaving little to no room for thinking and rewinding the videos which made it a bit uncomfortable to take."
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 Principal Component Analysis with NumPy with these
activities:
Review PCA concepts and theoretical background
Show steps
Solidify your prior knowledge and prepare yourself for this course by revisiting the fundamental concepts of PCA.
Browse courses on
Principal Component Analysis
Show steps
-
Review lecture notes or textbooks on PCA.
-
Go through online tutorials or videos to refresh your understanding.
Compile a collection of PCA resources
Show steps
Stay up-to-date with the latest advancements in PCA by gathering relevant resources for future reference.
Browse courses on
Principal Component Analysis
Show steps
-
Search and identify valuable articles, tutorials, and videos related to PCA.
-
Organize and categorize the resources for easy retrieval.
-
Share your collection with others who may find it useful.
Practice Eigenvalues and Eigenvectors with sympy
Show steps
Principal Component Analysis heavily relies on the concepts of Eigenvalues and Eigenvectors. Solidify your understanding of them using this Python library.
Browse courses on
Eigenvalues
Show steps
-
Install sympy if you haven't already. Then try the following code in the command line to calculate the Eigenvalues of a given matrix:
-
Calculate the Eigenvectors of a given matrix with the following code.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Solve PCA exercises from scikit-learn tutorial
Show steps
PCA exercises from the scikit-learn tutorial can help you master the fundamentals of this technique.
Browse courses on
Principal Component Analysis
Show steps
-
Open a notebook or code editor and walk through the examples in the given link.
Attend a PCA workshop or webinar
Show steps
Deepen your understanding of PCA by interacting with experts and learning from real-world applications.
Browse courses on
Principal Component Analysis
Show steps
-
Search for PCA workshops or webinars that align with your interests.
-
Register and attend the workshop or webinar.
Create a PCA demo
Show steps
Solidify your understanding of PCA by developing a demo that showcases its key concepts and applications.
Browse courses on
Principal Component Analysis
Show steps
-
Generate a sample dataset with multiple features.
-
Perform PCA on the dataset to reduce its dimensionality.
-
Visualize the original and transformed data using Seaborn or Matplotlib.
-
Interpret the results and write a brief explanation of the PCA process.
Mentor junior data scientists or students on PCA
Show steps
Enhance your understanding of PCA while developing leadership skills by mentoring others in its application.
Browse courses on
Principal Component Analysis
Show steps
-
Identify junior data scientists or students who are interested in learning about PCA.
-
Provide guidance and support on PCA concepts and implementation.
-
Review their work and offer feedback to help them grow.
Develop a PCA-based recommendation system
Show steps
Deepen your grasp of PCA by implementing it in a practical project. Build a recommendation system and see PCA in action.
Browse courses on
Principal Component Analysis
Show steps
-
Gather a dataset with user-item interactions.
-
Perform PCA on the user-item matrix to reduce its dimensionality.
-
Develop a recommendation algorithm based on the transformed data.
-
Evaluate the performance of your recommendation system using appropriate metrics.
-
Write a report documenting your project and findings.
Contribute to an open-source PCA library
Show steps
Make a valuable contribution to the PCA community by actively participating in the development and improvement of PCA libraries.
Browse courses on
Principal Component Analysis
Show steps
-
Identify an open-source PCA library that aligns with your interests.
-
Explore the library's documentation and identify areas where you can contribute.
-
Propose and implement improvements, bug fixes, or new features.
-
Collaborate with other contributors and maintainers to ensure your changes are integrated.
Review PCA concepts and theoretical background
Show steps
Solidify your prior knowledge and prepare yourself for this course by revisiting the fundamental concepts of PCA.
Browse courses on
Principal Component Analysis
Show steps
Show steps
Show steps
- Review lecture notes or textbooks on PCA.
- Go through online tutorials or videos to refresh your understanding.
Compile a collection of PCA resources
Show steps
Stay up-to-date with the latest advancements in PCA by gathering relevant resources for future reference.
Browse courses on
Principal Component Analysis
Show steps
Show steps
Show steps
- Search and identify valuable articles, tutorials, and videos related to PCA.
- Organize and categorize the resources for easy retrieval.
- Share your collection with others who may find it useful.
Practice Eigenvalues and Eigenvectors with sympy
Show steps
Principal Component Analysis heavily relies on the concepts of Eigenvalues and Eigenvectors. Solidify your understanding of them using this Python library.
Browse courses on
Eigenvalues
Show steps
Show steps
Show steps
- Install sympy if you haven't already. Then try the following code in the command line to calculate the Eigenvalues of a given matrix:
- Calculate the Eigenvectors of a given matrix with the following code.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Solve PCA exercises from scikit-learn tutorial
Show steps
PCA exercises from the scikit-learn tutorial can help you master the fundamentals of this technique.
Browse courses on
Principal Component Analysis
Show steps
Show steps
Show steps
- Open a notebook or code editor and walk through the examples in the given link.
Attend a PCA workshop or webinar
Show steps
Deepen your understanding of PCA by interacting with experts and learning from real-world applications.
Browse courses on
Principal Component Analysis
Show steps
Show steps
Show steps
- Search for PCA workshops or webinars that align with your interests.
- Register and attend the workshop or webinar.
Create a PCA demo
Show steps
Solidify your understanding of PCA by developing a demo that showcases its key concepts and applications.
Browse courses on
Principal Component Analysis
Show steps
Show steps
Show steps
- Generate a sample dataset with multiple features.
- Perform PCA on the dataset to reduce its dimensionality.
- Visualize the original and transformed data using Seaborn or Matplotlib.
- Interpret the results and write a brief explanation of the PCA process.
Mentor junior data scientists or students on PCA
Show steps
Enhance your understanding of PCA while developing leadership skills by mentoring others in its application.
Browse courses on
Principal Component Analysis
Show steps
Show steps
Show steps
- Identify junior data scientists or students who are interested in learning about PCA.
- Provide guidance and support on PCA concepts and implementation.
- Review their work and offer feedback to help them grow.
Develop a PCA-based recommendation system
Show steps
Deepen your grasp of PCA by implementing it in a practical project. Build a recommendation system and see PCA in action.
Browse courses on
Principal Component Analysis
Show steps
Show steps
Show steps
- Gather a dataset with user-item interactions.
- Perform PCA on the user-item matrix to reduce its dimensionality.
- Develop a recommendation algorithm based on the transformed data.
- Evaluate the performance of your recommendation system using appropriate metrics.
- Write a report documenting your project and findings.
Contribute to an open-source PCA library
Show steps
Make a valuable contribution to the PCA community by actively participating in the development and improvement of PCA libraries.
Browse courses on
Principal Component Analysis
Show steps
Show steps
Show steps
- Identify an open-source PCA library that aligns with your interests.
- Explore the library's documentation and identify areas where you can contribute.
- Propose and implement improvements, bug fixes, or new features.
- Collaborate with other contributors and maintainers to ensure your changes are integrated.
Career center
Learners who complete Principal Component Analysis with NumPy will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you will design and develop machine learning models to solve real-world problems. This course in Principal Component Analysis with NumPy will provide you with the foundation you need to understand the mathematics behind machine learning algorithms. You will learn how to apply PCA to improve the performance of machine learning models and make them more efficient. This course will help you develop the skills you need to be successful in this role.
Data Analyst
Data Analyst
As a Data Analyst, you will collect, clean, and analyze data to help businesses make informed decisions. This course in Principal Component Analysis with NumPy will provide you with the skills you need to work with large datasets and identify patterns and trends. You will learn how to apply PCA to reduce the dimensionality of data and visualize it in a way that makes it easy to understand. This course will help you develop the skills you need to be successful in this role.
Data Scientist
Data Scientist
As a Data Scientist, you will use data to solve business problems and make informed decisions. This course in Principal Component Analysis with NumPy will provide you with the skills you need to work with large datasets and identify patterns and trends. You will learn how to apply PCA to reduce the dimensionality of data and visualize it in a way that makes it easy to understand. This course will help you develop the skills you need to be successful in this role.
Data Engineer
Data Engineer
As a Data Engineer, you will design and build data pipelines to collect, clean, and store data. This course in Principal Component Analysis with NumPy will provide you with the skills you need to work with large datasets and identify patterns and trends. You will learn how to apply PCA to reduce the dimensionality of data and visualize it in a way that makes it easy to understand. This course will help you develop the skills you need to be successful in this role.
Quantitative Analyst
Quantitative Analyst
As a Quantitative Analyst, you will use mathematical and statistical models to analyze financial data and make investment decisions. This course in Principal Component Analysis with NumPy will provide you with the skills you need to work with large datasets and identify patterns and trends. You will learn how to apply PCA to reduce the dimensionality of data and visualize it in a way that makes it easy to understand. This course will help you develop the skills you need to be successful in this role.
Financial Analyst
Financial Analyst
As a Financial Analyst, you will analyze financial data and make recommendations to businesses and investors. This course in Principal Component Analysis with NumPy will provide you with the skills you need to work with large datasets and identify patterns and trends. You will learn how to apply PCA to reduce the dimensionality of data and visualize it in a way that makes it easy to understand. This course will help you develop the skills you need to be successful in this role.
Operations Research Analyst
Operations Research Analyst
As an Operations Research Analyst, you will use mathematical and statistical models to solve business problems. This course in Principal Component Analysis with NumPy will provide you with the skills you need to work with large datasets and identify patterns and trends. You will learn how to apply PCA to reduce the dimensionality of data and visualize it in a way that makes it easy to understand. This course will help you develop the skills you need to be successful in this role.
Statistician
Statistician
As a Statistician, you will collect, analyze, and interpret data to help businesses and organizations make informed decisions. This course in Principal Component Analysis with NumPy will provide you with the skills you need to work with large datasets and identify patterns and trends. You will learn how to apply PCA to reduce the dimensionality of data and visualize it in a way that makes it easy to understand. This course will help you develop the skills you need to be successful in this role.
Actuary
Actuary
As an Actuary, you will use mathematical and statistical models to assess risk and uncertainty. This course in Principal Component Analysis with NumPy will provide you with the skills you need to work with large datasets and identify patterns and trends. You will learn how to apply PCA to reduce the dimensionality of data and visualize it in a way that makes it easy to understand. This course will help you develop the skills you need to be successful in this role.
Market Researcher
Market Researcher
As a Market Researcher, you will collect, analyze, and interpret data to help businesses understand their customers and make informed decisions. This course in Principal Component Analysis with NumPy will provide you with the skills you need to work with large datasets and identify patterns and trends. You will learn how to apply PCA to reduce the dimensionality of data and visualize it in a way that makes it easy to understand. This course will help you develop the skills you need to be successful in this role.
Business Analyst
Business Analyst
As a Business Analyst, you will use data to analyze business problems and make recommendations to improve performance. This course in Principal Component Analysis with NumPy will provide you with the skills you need to work with large datasets and identify patterns and trends. You will learn how to apply PCA to reduce the dimensionality of data and visualize it in a way that makes it easy to understand. This course will help you develop the skills you need to be successful in this role.
Risk Analyst
Risk Analyst
As a Risk Analyst, you will assess and manage risk for businesses and organizations. This course in Principal Component Analysis with NumPy will provide you with the skills you need to work with large datasets and identify patterns and trends. You will learn how to apply PCA to reduce the dimensionality of data and visualize it in a way that makes it easy to understand. This course will help you develop the skills you need to be successful in this role.
Epidemiologist
Epidemiologist
As an Epidemiologist, you will investigate the causes of disease and develop strategies to prevent and control it. This course in Principal Component Analysis with NumPy will provide you with the skills you need to work with large datasets and identify patterns and trends. You will learn how to apply PCA to reduce the dimensionality of data and visualize it in a way that makes it easy to understand. This course will help you develop the skills you need to be successful in this role.
Biostatistician
Biostatistician
As a Biostatistician, you will use statistical methods to analyze biological data and solve health-related problems. This course in Principal Component Analysis with NumPy will provide you with the skills you need to work with large datasets and identify patterns and trends. You will learn how to apply PCA to reduce the dimensionality of data and visualize it in a way that makes it easy to understand. This course will help you develop the skills you need to be successful in this role.
Software Engineer
Software Engineer
As a Software Engineer, you will design, develop, and maintain software systems. This course in Principal Component Analysis with NumPy will provide you with the skills you need to work with large datasets and identify patterns and trends. You will learn how to apply PCA to improve the performance of software systems and make them more efficient. This course will help you develop the skills you need to be successful in this role.
For more career information including salaries, visit:
OpenCourser.com/course/0u08id/principal
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
Principal Component Analysis with NumPy.
Save
Provides a comprehensive overview of pattern recognition and machine learning, covering a wide range of topics from supervised and unsupervised learning algorithms to Bayesian methods, and includes exercises and projects to help readers understand and apply these techniques.
Provides a comprehensive overview of statistical learning, covering a wide range of topics from linear models to tree-based methods, and includes exercises and projects to help readers understand and apply these techniques.
Provides a comprehensive overview of machine learning from a probabilistic perspective, covering a wide range of topics from Bayesian methods to graphical models, and includes exercises and projects to help readers understand and apply these techniques.
Provides a comprehensive introduction to machine learning in Python, covering a wide range of topics from data preprocessing to model evaluation, and includes hands-on exercises and projects.
Save
Provides a comprehensive overview of machine learning algorithms, covering a wide range of topics from supervised and unsupervised learning algorithms to reinforcement learning, and includes exercises and projects to help readers understand and apply these techniques.
Provides a practical guide to machine learning with Python, covering a wide range of topics from data preprocessing to model deployment, and includes hands-on examples and projects.
Save
Provides a comprehensive overview of data mining and machine learning techniques, covering a wide range of topics from data preprocessing to model evaluation, and includes exercises and projects to help readers understand and apply these techniques.
Provides a gentle introduction to machine learning for beginners, covering the basics of supervised and unsupervised learning algorithms, and includes hands-on examples and exercises.
Provides a practical guide to machine learning for hackers, covering a wide range of topics from data preprocessing to model deployment, and includes hands-on examples and projects.
Save
Provides a comprehensive overview of deep learning, covering a wide range of topics from neural networks to reinforcement learning, and includes exercises and projects to help readers understand and apply these techniques.
Provides a practical guide to machine learning for business, covering a wide range of topics from data preprocessing to model deployment, and includes hands-on examples and projects.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0u08id/principal
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Effort
1.5 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Snehan Kekre
Language
English
Good to know
Develops PCA algorithms and implementations that are industry standard
Students who understand Python and machine learning theory can enroll
The course includes hands-on projects
Students must be familiar with NumPy
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