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Supervised Machine Learning: Regression and Classification (으)로 돌아가기

deeplearning.ai의 Supervised Machine Learning: Regression and Classification 학습자 리뷰 및 피드백

4.9
별점
147개의 평가
33개의 리뷰

강좌 소개

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....

최상위 리뷰

DC

2022년 6월 22일

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.

AN

2022년 6월 17일

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!

필터링 기준:

Supervised Machine Learning: Regression and Classification 의 45개 리뷰 중 1~25

교육 기관: Stefan C

2022년 6월 17일

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.

교육 기관: Jamie H

2022년 6월 17일

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!

교육 기관: Andrea N

2022년 6월 18일

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!

교육 기관: Michelle W

2022년 6월 20일

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.

교육 기관: J R

2022년 6월 21일

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.

교육 기관: Alireza S

2022년 6월 19일

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.

교육 기관: Abhishek P

2022년 6월 20일

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

교육 기관: Alexander S

2022년 6월 17일

- Amazing instructor

- Very clear and easy to understand examples

교육 기관: Zhenhao L

2022년 6월 25일

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.

교육 기관: Konstantinos Z

2022년 6월 22일

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.

교육 기관: NAVJOT S

2022년 6월 22일

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.

교육 기관: Andy W

2022년 6월 21일

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.

교육 기관: gishe t

2022년 6월 19일

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.

교육 기관: Nabil C

2022년 6월 20일

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.

교육 기관: aakash b

2022년 6월 23일

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.

교육 기관: Dan C

2022년 6월 23일

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.

교육 기관: RITUL M S

2022년 6월 25일

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.

교육 기관: Lewis C

2022년 6월 25일

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.

교육 기관: Jianhua M

2022년 6월 25일

A​ndrew Ng can turn unimaginable and ingenious ideas into very simple and obvious truths !

~​"The fragrance always remains in the hand that gives the rose."~

교육 기관: Lydia A

2022년 6월 22일

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

교육 기관: Rahul P

2022년 6월 23일

Very good explanition of concepts of machine learning.Most impresive is optional labs with intractive viz of concepts .Thanks for such great course!!!

교육 기관: Ryan M

2022년 6월 25일

Good for beginners. If you have taken the previous online course 'Machine Learning' taught by Prof. Andrew Ng, you may find this course much easier.

교육 기관: Diego C M

2022년 6월 25일

Andrew Ng makes an outstanding job explaining the math behind the magic :) The Jupyter Notebooks are definitely a game changer. Well done!

교육 기관: Davi W M

2022년 6월 21일

If you are new in the world of ML, here is where you begin your journey. Thanks Andrew Ng and Coursera for this amazing course :)

교육 기관: Alina D

2022년 6월 21일

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