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Volver a Supervised Machine Learning: Regression and Classification

Opiniones y comentarios de aprendices correspondientes a Supervised Machine Learning: Regression and Classification por parte de deeplearning.ai

4.9
estrellas
275 calificaciones
72 reseña

Acerca del Curso

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....

Principales reseñas

DC

22 de jun. de 2022

Excellent course, very logical and well structured. Highly recommended to anyone interested in learning about this topic. Assignments are on the easy side but you learn a lot nonetheless.

R

24 de jun. de 2022

absolutely amazing course, coding assignments are designed perfectly and the course helps in understanding the working and the math behind the algorithms which makes it so recommendable.

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1 - 25 de 92 revisiones para Supervised Machine Learning: Regression and Classification

por Stefan C

17 de jun. de 2022

tldr The course is a great introduction to ML for an audience already comfortable with mathematics and Python. For what it aims to achieve, I think it does a great job. /tldr

T​he mathematics involved in the first course of this specialisation is not that difficult if you already have a solid foundation on calculus. S​ome functions used in the Optional Labs are called for you from already written python scripts (which you have access to, and can download to inspect). The first 3 weeks (and probably the rest of the course) will not teach you fundamentals on Python or mathematics or statistics, and some details regarding the choice of loss function for logistic regression were omitted. Furthermore, libraries such as scikit-learn were used to complement the material, but not explained in depth. (Granted, this course is not about Python libraries.)

A​ll in all this seems like a great introduction to ML for people already comfortable with mathematics and Python.

If you already have the foundations required (Undergrad basic calculus, Python) you can do all 3 weeks in one day fairly easily without distractions.

por Jamie H

17 de jun. de 2022

Excellent content. I'm a math guy so I would have enjoyed some more in-depth theory, but that's what books are for I suppose!

I've been using Python for a long time now so understanding the code was nice and easy.

Thank you for your hard work putting this together!

por Lewis C

25 de jun. de 2022

Really enjoyed the course, had a few questions by the end of it that were resolved quickly in the forums. I would implore others to use them too as they are a great resource.

por Andrea N

18 de jun. de 2022

Andrew Ng is a very good professor, he explains complex concepts in a very simple way and with the help of many visualization and graphing tools. Highly recommended course!

por Lydia A

22 de jun. de 2022

The course is very interesting. I have learnt a deep understanding on machine learning, now I know the difference between regression and classification.

por Alina D

21 de jun. de 2022

Good, I keept working on these codes and searching for clues in videos. Good structure, reinforcment of some knowledge.

por Michelle W

20 de jun. de 2022

Excellent course, it really lays the groundwork for understanding the concepts and some of the math behind it, and provides an opportunity to play with the python code in labs. This is a step up from "AI for Everybody", and a good prep for the Deep Learning Specialization. I'm a data analyst with some coding experience, prior coursework in calculus & linear algebra & basic statistics, and found this a great supplement as I'm also working through the Deep Learning Specialization.

por J R

21 de jun. de 2022

Fantastic introduction to Machine Learning. The labs have been updated with widgets. You can add data points, change the polynomial order and many other changes that makes this a great way to understand how the different components of machine learning are done. Highly recommend.

por Alireza S

19 de jun. de 2022

This is a great Machine Learning course for the first-time learners offered by the best in the field. IMHO, the focus of course is on learning the underlying theories of machine learning rather than short-circuiting the basic concepts to the helpers libraries developed in Python.

por Vladimir S

28 de jun. de 2022

Excellent balance of theory and practice provided by exceptionally well documented and visualized examples and code in Jupyter Notebooks that one can interact with to build intuition.

por Abhishek P

20 de jun. de 2022

Precise explanation of the fundamentals of Machine learning techniques, using mathematical examples and python.

por Alexander S

17 de jun. de 2022

- Amazing instructor

- Very clear and easy to understand examples

por Zhenhao L

25 de jun. de 2022

This is really a fantastic course as it provides hands-on machine learning experience, but also a lot of intuition as Andrew is so brilliant at explaining complex concepts in very simple and understandable language and visualizations.

It is very friendly to non-math students as well as high school math such as basic linear algebra and calculus may suffice to get a lot of intuition yet without being too overwhelmed by the formality of math.

I also really like the structure of the course, and I now understand very well concepts such as the loss of a single data entry, aggregating losses into an overall cost function, and using the gradient descent algorithm to minimize the cost function to find optimal parameters for learning a curve that fits the input data.

por Konstantinos Z

22 de jun. de 2022

Very well structured course with great explanations in the appropriate pace. The maths are discribed clearly and the connection between algebra and algorithms (Machine Learning) becomes and easy process.

The assignments are in the indermediate level and the student should understand the theory/maths to complete them with 100% grade. They are all explained in the lectures videos but you need to think before you submit them.

Overall, is an upgrade of the previous course that is adjusted on Python and Jupyter Notebooks. 5/5 stars.

por Carlos J G

28 de jun. de 2022

El curso es muy claro y bien dictado. Es me jor que el curso de achine Learning que estoy tomando también con NG. Recomendaría unos ejemplos mas trabajados y un curso previo de Python, pues esta es la parte que me costó mas trabajo. Aunque los ejemplos son en Jupieter, hay mucho software oculto que uno no puede entender y analizar. Lástima que por los costos no pueda continuar con los demás cursos, por eso quedo a la espera de la ayuda financiera.

Gracias

Carlos J. Gorricho

por Daniel W

29 de jun. de 2022

T​hought it was great and felt it was much more beginner-friendly than the previous course. The programming aspect of it can be tricky if you've never had programming experience, so I highly recommend you learn the basics of python (variables, for-loops, functions, etc.) before taking the course. If you have some brief background in ML and programming you should be able to finish this course relatively quickly.

por NAVJOT S

22 de jun. de 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.

por Andy W

21 de jun. de 2022

A great learning journey with Andrew Ng and thanks to all of the people behind to make it so intuitive and fun to learn .. I never thought that ML could be such easy to understand and with the this new Jupyter notebook and all graphics and animations this course turns the boring math into an excited exploration into the future.

por rcotta

26 de jun. de 2022

Great course! Provides a very good understanding on how some of the supervised learning algorithms work and makes you code a bit in Python to bind theory and practice together. Ng's explanations are very clear and I had a relevant increase on my knowledge after completing this.

por gishe t

19 de jun. de 2022

Well-designed course and the concepts are to the point. The instructor was very knowledgeable. Most important of all, the instructor is encouraging. The sample codes are very helpful. However, the content is very short, only 3 weeks.

por Houston M

28 de jun. de 2022

A great update to an already brilliant course. This is where all aspiring data scientists need to start. Even practitioners will gain something from using this course to revise concepts forgotten due to the use of pre-made packages.

por Nabil C

20 de jun. de 2022

The content is very well architected with math being introduced just in time. The delivery is excellent. Quizzes and labs keep you engaged and focused. This was a very pleasant journey. I am definitely going for the next course.

por Divyesh S

27 de jun. de 2022

I really like this course . The course is very interactive . Andew sir and team did a fabulous job explaining each and every concept of classification and regression very intuitively. Thank you so much Andrew sir and the team

por Mirnajaf A

27 de jun. de 2022

This course is so cool because not only it teaches the detailed concepts of Machine Learning Algorithms but also provides us with practical part with modern programming language Python which makes it extra amazing.

por aakash b

23 de jun. de 2022

In the world of today when most people are pursuing courses for an embellishment of their CV, this course shows that the best minds are those which focus only on learning without worrying about the consequences.