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Avis et commentaires pour d'étudiants pour Supervised Machine Learning: Regression and Classification par deeplearning.ai

4.9
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6,990 évaluations

À propos du cours

In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start....

Meilleurs avis

JM

21 sept. 2022

Specacular course to learn the basics of ML. I was able to do it thanks to finnancial aid and I'm very grateful because this was really a great oportunity to learn. Looking forward to the next courses

AD

23 nov. 2022

Amazingly delivered course! Very impressed. The concepts are communicated very clearly and concisely, making the course content very accessible to those without a maths or computer science background.

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101 - 125 sur 1,564 Avis pour Supervised Machine Learning: Regression and Classification

par Charles B

30 oct. 2022

The course focused on learning how to do the equations and programming for the algorithms. It gave a good understand of not just the algorithms but when and how to implement them. There was no time wasted on being concerned how to generate plots or gather test data for the assignments. That was done for us... which was a great help. All in all a very well planned and executed course.

par Fredrik Ö

3 août 2022

Had already completed the old course "Machine Learning". Took this course because of the switch from Octave to Python. So i thought it was a great idea to repeat what i had learned and at the same time sharpen my skills in Python. Really liked the enhancements, like the extra optional labs with Scikit. Also this was a preparation for me since i intend to take the 2 continuation courses.

par Jeffrey C

14 août 2022

Terrific introductory course, but I wish it gave you the option for more hands on implementation of the supervised machine learning algorithms as you progressed. I could have easily passed this course with knowing the bare minimum, but I wanted to become proficient in the foundations, and unfortunately there wasn't much in the way of testing your knowledge without the training wheels.

par vijay s

14 août 2022

I felt after learning this, that my overall understanding has become very deep and now i feel very confident about implementing this in real life scenorio. It has given me clarity on "how to steps in Machine learning" . Very intutive and natural course for topic of vast calibre and application. Thanks to the team of coursera, deeplearning.ai and standford for sharing such information.

par Tharun N

5 juil. 2022

This course is fantastic, everything from the previous course but more. Adding Python instead of octave/Matlab is excellent, and the programming assignments are also beneficial. The teaching is exceptional as always. If you are looking for a course in machine learning, this is the best pick. I enrolled the day the course was released, looking forward to completing the specialization.

par Matthew T

7 nov. 2022

Very good - easy to understand instruction and enjoyable to listen to.

The lab's are excellent to take the theory and test it. I found using the labs was the best way to understand the maths and logic, and how the layers of iterations come together. Particularly in the last few lessons when you have operations happen on individual features, individuals examples and then the whole set.

par Argha B

28 juil. 2022

Andrew Ng is one of the pioneers in the field of AI. His original course, while very theortically enriched, was showing its age for the choice of its programming language. This new specialization was just the right thing for someone like me who needed to implement all the concepts in the de facto language of AI, all the while learning the said concepts from the leaders of the field.

par Saif U R

16 août 2022

Thank you Prof. Andrew, Eddy Shyu, Aarti Bagul, Geoff Ladwig, and all the members of the team for a wonderful course. It is very easy to understand and, at the same time, enjoyable. And, deeplearning community is also very supportive. I got stuck several times in the course and the community help me to go through that. Highly indebted to all of you. Hasta la vista in course 2.

par César D M C

6 oct. 2022

Durante el desarrollo del curso vas acercandote cada vez mas a problemas reales con la ayuda de herramientas que se utilizar en el desarrollo de ML. Te da el conocimiento basico y va profundizando en los conceptos sin saturar la leccion. Las notebooks son muy agradables y ayudan mucho a practicar la teoria, no cabe duda que es el mejor curso de ML con el que puedes comenzar.

par Krishnakanth G V

22 sept. 2022

its a good experience through out the course and keep my expectations to the mark with the coverage of topics in this specialization . I develop myself to find more about field of Machine Learning concepts around the world in my window.thanks to that , I got some confidence to say that I aware of what is supervised ML and use the optimal algorithms to particular problems

par Vincent A C T

5 janv. 2023

Thank you Andrew Ng and team for such an incredible journey in this first course of machine learning specialization. I have gained much better concept and understanding on supervised learning, especially in linear and logistic regression. This course really helps me establish a solid foundation in the world of machine learning. Again, thank you so much for the opportunity :)

par Svetlana V

18 janv. 2023

Excellent presentation by Dr. Ng, as usual. I like how the assignments are prefilled with data/values not related to the core learning, how clear the math behind it all is explained, and that this course uses the tools most likely found in most companies.

The previous version of course was great, too, but skewed a little too academic. You did a great job, folks! Thank you!

par Mohamed A K

1 nov. 2022

I binged watched the whole course in two days since i couldnt just stop. Andrew is amazing, most of the time i was able to understand some mathematical concepts that is didnt think i will be able to understand before. I think that every beginner in machine learning has to start with this course. It explains mathematically all what you will be using as code later on.

par Sachin B

5 sept. 2022

The Course was very interactive and has many quizzes to evaluate your understanding. Exited to Complete the upcoming courses on Advance Algorithms and unsupervised Learning. I would like to thank Andrew ng and the whole teaching staff for creating the best material for machine learning. I especially liked the optional labs, which helped me dive deep into the topic.

par Jot

22 juin 2022

Really learned a lot of mathematical concepts behind machine learning algorithms in depth. The course content is in sequence andintroduces complex topics in a quite simple manner. The associated optional labs and programming assignments hep get better understanding of underlying concepts. Nevertheless, the pre-requisites such as python, statistics are important.

par Said P

2 oct. 2022

Professor Andrew Ng delivered a high quality course as always. I refreshed my knowledge of regression and classification. The course has amazing examples of how gradient descent works under the hood. Each new concept has a jupyter notebook example and visualization of how each step works. Highly recommend this course to anyone interested in introduction to ML.

par Akshay A

8 janv. 2023

Developed lots of mathematical intuitions, had fun labs which I must say were absolutely beautiful, notebooks provided by the instructors were phenomenal, I would recommend the course for the labs!! Just watching the videos and doing quizzes is *not* enough and the instructors know that.

I am grateful to Andrew and team for the wonderful work they have done.

par 21SCMEA132 K A

3 sept. 2022

Since this is my very 1st course ever in field of Machine Learning (career switch from civil engineering domain), I am very thankful to Dr Ng for making the tough terminolgies so easy to understand. The practical assignments (python programming) is what makes this course industry oriented. Thank you so much for this masterpiece Dr Ng !

Regards

Kasib Ahmed

par Pradeep C

30 sept. 2022

ML concepts very well explained. For practicing and actual world challenge additional resources on Numpy, Tensor Flow, Keras are required. Professor makes this a cake walk to understand core of machine learning concept for new to the field. I am weak in programming still I could see (experience ) the vast expanse of this alien world of machine learning.

par Muhammad S S D

15 nov. 2022

This is the best course on the internet for supervised machine learning and its basic algorithm. I learned a lot of new concepts from this course and I hope to learn new things after this. Andrew Ng is an awesome instructor . I loved the way the whole course was conducted. All of the topics were simplified and optional labs were very helpful as well.

par Manuel M G

25 sept. 2022

Explicaciones claras y buen material de laboratorios para acercarse al contenido mediante experimientos y visualizaciones. El único punto del curso que podría mejorar son las tareas de programación: considero que tener una mayor cantidad de ejercicios a realizar, quizás más breves pero más frecuentes, haría más fácil asimilar el contenido dado.

par Shivanshu U

31 juil. 2022

Such a beautiful course I have ever seen about machine learning. No, one can explain like andrew Ng sir . He explain all the algorithm with mathematical aspect too. I can solve all the algorithm with or without sklearn library. Thanks for making these type of course.It is help to make a perfect root of student in the feild of machine learning.

par Rian F J

26 juil. 2022

The course is very good since the topic really explains the theory behind the concepts needed for machine learning. Andrew Ng also discusses the concepts very well and the lab assignments are very helpful to solidify the ideas you have to learn from the tutorial videos. I would definitely recommend this course, especially for beginners in ML.

par Valerie D

3 nov. 2022

Even auditing it, you learn a wealth of material from the videos. In fact, in some respects, not having access to the optional labs (you have to "upgrade", or subscribe, to gain access) motivates you to create your linear regression code from scratch. That makes it a bit more of a challenge and helps you work on your coding skills as well.

par Jaya K K

5 sept. 2022

Professor Andrew NG and his team did a great job yet again with structuring this course. Coming in with some background in Machine Learning, this course for me served as a great refresher for the introductory concepts in Machine Learning. I'm also delighted to take baby steps into python programming and scikit library through this course.