糖心vlog官网观看

A very difficult and challenging course yet very important for my carreer growth.

I urge everyone who wants to keep on growing on his/hers job to get this course done.

Each and every feature makes this course more robust and riched. Practical quizzes and labs have been very effective to learn the features of this course efficiently.

This is a really good course.

Every lab gives the code implementation with proper explanation. The final project was a good way to put everything we learn to practice.

While the final project is again terrible (like so many other courses), the content of this course is great. I will refer back to my notes from here for a long time.

I have completed the course with financial aid . I am very much satisfied with the content. Thanks 糖心vlog官网观看 to give the world such chance to enrich their knowledge

One of the best & most interesting courses in this complete data science specialization. As a naive to this domain, I aquired a ton of insights in Machine Learning.

This is good course through intro for machine learning better if you add intro to some advanced topics like tensor flow,artificial neural networks and use of them.

Its a nice course to start with. It explains all commonly used machine learning techniques in detail along with Python code. One should definitely take this course.

This course is awesome. The instructor was very good. His Teaching style and understanding of concepts is very good. This course gonna help me to achieve my goals.

A very nice course. It includes both theoretical and practical tutorials. After completion of this course one will have an in depth knowledge on machine learning.

Very practical course which helps you learn Python and how it's implemented in machine learning algorithms. Having some examples as well, so you can learn easier.

Very nice course. The course material was very comprehensive.

However, I still feel there should be a more standard grading process other than simply peer review.

Fantastic course! Only thing given the level of detail contained herein, is to consider splitting this into part 1 and 2 so can more fully capture the essentials.

This is the best experience learning machine learning with Python. There are many good examples to practice and it gives the clear idea on what we are learning.

very good examples and practical exercises. perhaps more of a global overview of ML techniques/uses would have been better but still a good introductory course.

Excellent notes, labs and quizzes. The best, simplest and most direct way to learn Python and machine learning. Wish all courses were taught this way world-wide.

Very nice course, that covers a lot of concepts from Machine learning, and includes very useful practical modules to get more familiar with the python routines

The course content is exemplary. Examples given reflects real life issues to understand better. Its is very useful to develop good understanding of the concept.

Absolutely recommended if you want to taste what is machine learning... I love the structure of the course. Must take course if you are a newbie in data science

A very good introduction to machine learning using Python, covers the basic concepts well and provides good introduction for various widely used ML algorithms.

curso incre铆ble sencillo, la tarea con honores fue al punto, y aunque temas un poco pesados, para alguien con experiencia en ingenier铆a o matem谩ticas no tanto,

Very good introduction course.

I think it can be much better if there is any alternative on displaying decision trees since the anaconda libraries always crash.

Data Analysis with Python and ML are the best courses in the IBM certificate, the course is well laid out, the labs are excellent and the capstone is hands-on.

Great Course! The instructors are simply awesome and the text, slides, graphics and exercises were manifestly meticulously prepaired. I can just recommend it!

Very interesting subjects like intro with ML, regression model principles , classifications, KNN, Decision tree, clustering , recommender systems with ML.