Volver a Mathematics for Machine Learning: PCA

estrellas

2,836 calificaciones

This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction.
At the end of this course, you'll be familiar with important mathematical concepts and you can implement PCA all by yourself. If you’re struggling, you'll find a set of jupyter notebooks that will allow you to explore properties of the techniques and walk you through what you need to do to get on track. If you are already an expert, this course may refresh some of your knowledge.
The lectures, examples and exercises require:
1. Some ability of abstract thinking
2. Good background in linear algebra (e.g., matrix and vector algebra, linear independence, basis)
3. Basic background in multivariate calculus (e.g., partial derivatives, basic optimization)
4. Basic knowledge in python programming and numpy
Disclaimer: This course is substantially more abstract and requires more programming than the other two courses of the specialization. However, this type of abstract thinking, algebraic manipulation and programming is necessary if you want to understand and develop machine learning algorithms....

WS

6 de jul. de 2021

Now i feel confident about pursuing machine learning courses in the future as I have learned most of the mathematics which will be helpful in building the base for machine learning, data science.

JS

16 de jul. de 2018

This is one hell of an inspiring course that demystified the difficult concepts and math behind PCA. Excellent instructors in imparting the these knowledge with easy-to-understand illustrations.

Filtrar por:

por Lucas O S

•7 de nov. de 2019

Classers are good. However, the exercise platform is full of bugs. Notebook keeps disconnecting, making it unable to save the latest changes. The automatic grader requires a very specific implementation in the last notebook, which is not mentioned anywhere and can you make lose hours debugging an implementation that is otherwise correct.

por Tetteh H

•22 de ene. de 2021

I found this very challenging as there are fewer explanation of concepts. there was a huge difference between the lecture's exercise and the practice exercise or the quizzes, the lecturer's exercises were easy with no difficulty but the quizzes. If you want to take this course, be self-prepared to bring out the best in you.

por Jim A

•14 de abr. de 2020

The course should be longer and build a stronger foundation in order for the assignments to not feel disconnected from the instruction. There was a large amount of redundancy from previous courses. The PCA instruction from week 4 needs more development/insight. Great specialization overall. Part 3 needs more work though.

por Yuvaraj K

•2 de ene. de 2022

This specialization course was really challenging. While I do understand that PCA topic is tough to cover in just a month, the concepts can still be explained down to our level like it wass in Calculus and Linear Algebra specialization. However I liked the course and thanks coursera for this wonderful journey.

por Toan L T

•3 de oct. de 2018

Thank you to all the professors and staffs for such a wonderful program. I did learn a lot.

This last course is indeed a fun and challenging one. But it fells short compared to the other two due to some aspects which can be improved in the future.

Nevertheless, I'm glad that I can learn about PCA.

por Ankit C

•19 de abr. de 2020

The course contents were good, but I felt the explanation was not so clear. Since PCA is a very important topic in Machine Learning, after explaining some new concept, the instructor could've solved a couple of examples with it, so that the newly registered concepts would be crystal clear.

por Gautam K

•24 de jun. de 2020

Course content is very awesome. The instructor also teaches in a very splendid manner which makes it very easily understandable. But the evaluation method for practice exercise is very worse. Code get stuck for hours. It's been very frustrating waiting for code to get compiled.

por arnaud j

•12 de jun. de 2018

This course is way more brutal than the two previous courses in the specializationIt is also very mathematically oriented, it lacks the graphics / animation / intuition that was given in the first two courses.However, if you make it, you indeed have a good understanding of PCA.

por Philipp A R

•6 de mar. de 2020

A lot of input in relatively short time, main points could be pointed out better in the videos. Assignments were tough but manageable, the instructions could be clearer and more detailed. However, being pushed to figure out things by yourself is also a learning opportunity.

por Xin W

•12 de nov. de 2019

To me, the first 3 weeks in this course is good. But the 4th week is quite confusing. And I don't understand the applicable meaning for the materials in the 4th week. I may need to review what I learned in the 4th week and then decide whether I understand it completely.

por Manju S

•29 de ene. de 2019

Good stuff:

Instructor has good knowledge of the subject. The course content structure is designed well.

Bad stuff:

Concepts could have been presented with more clarity. Programming assignments need more instructions and less assumption on what the students already know.

por Gabriel C

•24 de abr. de 2020

Quality of the course is great, but I would question whether it belongs in this specialization given the huge jump in expected knowledge from the first two courses to this one. Relied alot on the forums and YouTube to gain sufficient knowledge to complete this course.

por Ashish P

•21 de oct. de 2020

Instructor has done lot of hard work. However, the course is little rigorous. If it is possible, I request the team to upload few more videos for this module. Nevertheless, thank you so much. I have still learned a lot from this course.

por helen l

•27 de abr. de 2020

The content is decent but there are some bugs in the programming assignments. Particularly the last two programming assignments. The auto-grader for the second to the last assignment passes in some input that is not of the correct form.

por V K

•23 de jul. de 2020

The course content was very good,but the assignments were harder as knowledge of python libraries was required. It would be very helpful if you change the assignments as I feel the course should rather be about math than python

por Pierre

•10 de abr. de 2020

Positive points: At the end of the module, you get a good understanding on how PCA works. It fulfill its objective.

Negative points: The assignements are poorly directed, the material is not always clearly explained.

por Alexander Z

•14 de sep. de 2018

Good Course, but

Too less examples to do the quizes on the first run.

Programming assignments are not clearly stated, so you need unnecessary much time to succeed.

I liked the Linear Algebra & Multivariate Modul more!

por devansh v

•3 de abr. de 2020

The course is Satisfactory.The content is Good,no doubt about it,but many topics(both mathematical and computational) were unknown and coding assignments of Jupyter notebooks of this course(PCA) are very Buggy

por Norah

•25 de ago. de 2020

Kinda complicated but doable. The stuff do not monitor the discussion forums unfortunately. Without Susan's detailed & well informative replies I won't be able to complete the course. Big THANK YOU to Susan.

por Marina P

•6 de sep. de 2019

The course is interesting, but some of the quizzes were not done very well. After the first 2 parts of this course, which were just amazing, this one seems kind of worse, although by itself its not that bad.

por Ahmed A

•19 de jun. de 2022

They need to slow down while explaining concepts. The instructor assumes the viewers know each and every step. The other two Mathematics for Machine Learning courses were much better compared to this.

por Deleted A

•5 de ene. de 2021

I was expecting this course to connect with the previous two but it turned out to be self contained. Jupyter notebooks contain inconsistent comments and assignment steps. Certain tasks were not clear.

por Rosanna H

•1 de abr. de 2021

The jump in difficulty for the final two modules was too hard going from the previous two courses in my opinion. also I would have liked more practical examples rather than being directed to reading.

por Chad K

•8 de jul. de 2020

Difficult course. They need more formal tutorials to help with the gap between the videos and the tests and projects. I found it very helpful to buy the instructor's textbook and read along in it.

por Cécile L

•14 de abr. de 2019

Amazing topic, great teachers and nice videos, but assignments can be slightly frustrating and some aspects (matrix calculus, derivatives, etc.) are really expedited... Still worth your time!!!

- Analista de datos de Google
- Gestión de proyectos de Google
- Diseño de experiencia del usuario (UX) de Google
- Soporte de TI de Google
- Ciencia de datos de IBM
- Analista en datos de IBM
- Análisis de datos de IBM con Excel y R
- Analista de ciberseguridad de IBM
- Ingeniería de Datos de IBM
- Desarrollador de la nube de pila completa de IBM
- Marketing en redes sociales: Facebook
- Analítica del marketing de Facebook
- Representante de desarrollo de ventas de Salesforce
- Operaciones de venta de Salesforce
- Contabilidad en Intuit
- Prepárate para una certificación en Google Cloud: arquitecto de la nube
- Prepárate para una certificación en Google Cloud: ingeniero de datos de la nube
- Lanza tu carrera profesional
- Prepárate para una certificación
- Avanza en tu carrera

- cursos gratuitos
- Aprende un idioma
- python
- Java
- diseño web
- SQL
- Cursos gratis
- Microsoft Excel
- Administración de proyectos
- seguridad cibernética
- Recursos Humanos
- Cursos gratis en Ciencia de los Datos
- hablar inglés
- Redacción de contenidos
- Desarrollo web de pila completa
- Inteligencia artificial
- Programación C
- Aptitudes de comunicación
- Cadena de bloques
- Ver todos los cursos

- Habilidades para equipos de ciencia de datos
- Toma de decisiones basada en datos
- Habilidades de ingeniería de software
- Habilidades sociales para equipos de ingeniería
- Habilidades para administración
- Habilidades en marketing
- Habilidades para equipos de ventas
- Habilidades para gerentes de productos
- Habilidades para finanzas
- Cursos populares de Ciencia de los Datos en el Reino Unido
- Beliebte Technologiekurse in Deutschland
- Certificaciones populares en Seguridad Cibernética
- Certificaciones populares en TI
- Certificaciones populares en SQL
- Guía profesional de gerente de Marketing
- Guía profesional de gerente de proyectos
- Habilidades en programación Python
- Guía profesional de desarrollador web
- Habilidades como analista de datos
- Habilidades para diseñadores de experiencia del usuario

- MasterTrack® Certificates
- Certificados profesionales
- Certificados universitarios
- MBA y títulos de grado en negocios
- Títulos de grado en ciencias de los datos
- Títulos en ciencias informáticas
- Títulos de grado en Análisis de datos
- Títulos de grado en salud pública
- Títulos de grado en Ciencias Sociales
- Títulos de grado en administración
- Títulos de grado de las principales universidades europeas
- Maestrías
- Licenciaturas
- Títulos de grado con trayectoria de desempeño
- Cursos BSc
- ¿Qué es una licenciatura?
- ¿Cuánto tiempo dura una Maestría?
- ¿Vale la pena hacer una MBA en línea?
- Siete maneras de pagar la escuela de posgrado
- Ver todos los certificados