Chevron Left
Zurück zu Supervised Machine Learning: Regression and Classification

Bewertung und Feedback des Lernenden für Supervised Machine Learning: Regression and Classification von

393 Bewertungen
99 Bewertungen

Über den Kurs

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....



29. Juni 2022

Perfect balance of application and theory, and wise choices in ramping up the complexity gradually. Discussion boards are very helpful, feels very much like personalized learning. Thank you!


22. Juni 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.

Filtern nach:

1 - 25 von 115 Bewertungen für Supervised Machine Learning: Regression and Classification

von Stefan C

17. Juni 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.

von Jamie H

17. Juni 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!


25. Juni 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.

von Vladimir S

28. Juni 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.

von Lewis C

25. Juni 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.

von Andrea N

18. Juni 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!

von Lydia A

22. Juni 2022

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

von Alina D

21. Juni 2022

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

von Michelle W

20. Juni 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.

von J R

21. Juni 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.

von Alireza S

19. Juni 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.

von Abhishek P

20. Juni 2022

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

von Alexander S

17. Juni 2022

- Amazing instructor

- Very clear and easy to understand examples

von Nazib E E K C

5. Juli 2022

Brilliantly Designed course to teach beginer on Machine Learning. The course focuses on the theory behind machine learning. The content convered in the course allows the student to get an intuitive idea behind machine learning and gives him an idea of the mathematics behind it. The course is not very math intensive, but there is just enough math covered here to give the student an intuitive idea of machine learning.

The coding labs provide very detailed code, which the user can learn and analyze to make his own machine learning algorithm

My favorite part about this course was how neatly the jupyter notebooks and python files of the lab were arranged and provided. These lab files take the burden of coding from scratch away from the students, and allow students to focus only on the algorithms behind machine learning.

After this course, machine learning codes will no longer be a black box, but will be something you will understand very well. So, after doing this course, the next time you use Machine learning libraries like SciKitLearn, you will know exactly what is going on behind the curtains, can you can adjust parameters of ready-built ML funcitons to fit your needs.

At the end of this course, you will learn how you can modify machine learning codes for each custom need, and you will gain the ability to do those modifications yourself. After this course, you will be able to write specific machine learning codes which are well suited for a different specific application

von Dinesha K V

4. Juli 2022

This is an excellent course on supervised lachine learning. The programming assignments are in python.

I have completed the previous machine learning course (programming in Octave ) by Andrew Ng hence I was comfortable with the concepts.

I was new to python and Jupeter notebook. Python implementation part (programming and explanation) is very friendly. I sincerely thank the mentor for immediate help on my problems in programming.

I comleted all assignments succesfully. But the strength of this course is also in the programming material given.This material is comprehensive, very rich and extremely useful. I need to go through in detail. I feel going through course material will help me to be comfortable in reading, writing, developing python programs for ML applications.

A big thanks to Professor Andrew Ng, Mentors and the deep learning community.

I strongly recommend the course for everyone interested in AI/ML.

von Zhenhao L

25. Juni 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.

von Konstantinos Z

22. Juni 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.

von DR A J

3. Juli 2022

E​xcellent course! Clear insight given by Andrew on complex concepts using simple examples. Alternative way of teaching this course would be getting into linear algebra and calculus, but then then learners would have missed practical aspects of this course. I liked the fact that the focus is on practical applications. Optional labs were very useful. They gave crisp demonstrations of concepts covered in the videos. As a beginner with python, I learnt a great deal of pythons as well.

von Carlos J G

28. Juni 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.


Carlos J. Gorricho

von Daniel W

29. Juni 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.

von Irene P

3. Juli 2022

With some Python experience, this was super hands on and easy to understand. I came into this course without a strong knowledge of how to decodify math algorithms, and with Andrew's super clear explanations and the super hands on optional labs, I found myself able to see how the alorithm was changing through visual graphs, and become able to apply the machine learning mathematical algorithms into code.


22. Juni 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.

von Andy W

21. Juni 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.

von rcotta

26. Juni 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.

von Chad W S

4. Juli 2022

Better than the original due to the interactive Jupyter notebooks written in modern Python, than the Octave environment on the previous versions of the materials. And as before, Professor Andrew Ng is an amazing and engaging instructor.