Вернуться к Applied Machine Learning in Python

4.6

звезд

Оценки: 6,215

•

Рецензии: 1,114

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis.
This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python....

Oct 14, 2017

Very well structured course, and very interesting too! Has made me want to pursue a career in machine learning. I originally just wanted to learn to program, without true goal, now I have one thanks!!

Sep 09, 2017

This course is ideally designed for understanding, which tools you can use to do machine learning tasks in python. However, for deep understanding ML algorithms you should take more math based courses

Фильтр по:

автор: Roger S

•Jun 15, 2020

Gives a good overview on ML-Techniques. I liked the evaluation part. "Applied" means - they provide no technical/mathematical details of the different methods. You should get it somewhere else.

Everything is well set up. You need the knowledge of the previous courses of this specialization.

автор: Rajan G

•Jul 06, 2020

The course was very good. It has covered a lot of topics in a small time and has provided a good insights about all of them. It would be good if some hints can be provided with each question during the assignment as while facing confusion or problem it can help us to progress further.

автор: Sumit M

•Feb 19, 2019

This is a very good course about How to apply Machine Learning but I think before taking this course the student should take the Andrew Ng machine learning course by Stanford University to Learn the Important Mathematics behind the ML algorithms

But Enjoyed this course a lot

thank you

автор: Abhishek B

•May 02, 2020

The course definitely provided me with great insight. It allowed me to see different things & try out manifold elements in my own projects at work. Getting to know extensively on classification was really good. Just the only thing missing was the same depth for regression problems.

автор: Mark H

•Feb 01, 2018

Excellent course! Well paced lectures, challenging quiz questions that also require insight and understanding, and programming assignments with explicit instructions leading to very little auto grader frustration. The perfect python complement to Andrew Ngs machine learning course.

автор: Bharath R

•Jun 17, 2019

Initially i had issues in getting in to video learning mode, got accustomed to it. One of the best way to learn in your own time as and when it suits you. Submission issues got sorted when discussed with peer. Maybe a SPOC for each course can be of more help to do it more quicker.

автор: Kunal c

•Jun 21, 2017

Wonderful course. The video lectures are very much to the point and this course is especially useful for someone who is more interested in application of Ml algorithms rather than their development. The intuition for all the algorithms are good and the course is very comprehensive

автор: XL T

•May 21, 2020

wonderful course. It requires a lot of self learning time to be honest. For my case, I have to do a lot of google search and background reading so to keep up to the learning pace of this mooc. However, I am very happy to be able to finish the assignments and it feels productive.

автор: David H

•Aug 04, 2018

Helped me to get the solid concept of Machine Learning. Since this course is mainly focused on the ways to use the machine learning skills in the real world problems, if you are interested in the mathematical approach of each skill, you might need to look into the other courses.

автор: Subham B

•Jun 11, 2020

Consider about buying this course if you have some pre-knowledge about ML....Understand that this is not a full ML Course, but a course that describes a lot about applications of this and different ML Algorithms. But this a very good course cause it does what it says very well.

автор: Chrisada S

•Jan 02, 2018

I really like that this course focuses on the application of machine learning methods, at the same time still provide enough insight of the working of each model. I do have the math background to follow the proofs, but I would rather spend my time doing rather than proofing.

автор: Angadvir S P

•Feb 24, 2019

The course was very useful, however, few of the assignments (specifically assignment 2) had a few errors in accurately displaying the question content and grading method was found to be slightly inconsistent with what was asked in the cells (Jupyter notebook).

4.5/5.0 stars

автор: Sashi B

•Jul 31, 2017

One of the best courses I have taken online! The professor lectures are great and very well laid out. The assignments are very challenging and meant to teach you real life scenarios. Highly recommend to anyone who wants to learn the basics of machine learning using Python.

автор: Atilio T

•Mar 21, 2020

Excellent course. Not only show how to use python for machine learning, it also teaches the key points in order to achieve a good model. Highly recommended, The instructor provides a clear message about the general idea of machine learning and the most important aspects.

автор: Kristóf U

•Mar 08, 2018

Really really good introduction to applied machine learning. It resolves the fear from the difficult application of complex mathematical formulas. It demystifies the topic of machine learning and provides a perfect introduction how to approach real world problems.

автор: Shahir

•Nov 03, 2017

One of the best courses I have ever taken. I wish I would have taken this course earlier. it gives provides you with a lot of practical tools in a shortest time. This course is perfectly designed and the instructor conveys information in the most efficient way.

автор: Christos G

•Sep 01, 2017

Following the first 2 sessions of this specialisation, this one seems easy and gives the student a lot of confidence. Make sure you follow the sequence suggested in this specialization, even if you do not plan to continue with Text Mining and Social Networks.

автор: John B

•Mar 18, 2018

Challenging but worthwhile mix of essential theory (explained well) and hand-on practice with good, sensible exercises to help one get a confident grasp of scikit learn packages which one can use in the real world. Many thanks to the organisers and Coursera.

автор: Naman M

•Feb 26, 2019

The Instructor is marvelous. The Assignments are amazing, The TA is really responsive. The content only for one month course was outstanding, my feedback would be to increase the amount of exercises(coding) and assignments, and make the course for 2 months.

автор: Jonathan B

•Jul 14, 2020

Excellent introduction into machine learning with Python. I came into this class with little knowledge of machine learning and was taking this to aid in my data science career. As a result of this course, I've decided to focus more on machine learning.

автор: Melissacrawford

•May 06, 2020

This course does a really good job taking you through the basics of ML through use of Scikit Learn models. It goes over a broad swath of models in a black-box fashion so you can start getting a feel for how each model is tuned and what parameters to use.

автор: Farzad E

•Mar 14, 2019

Assignments and quizzes help you a lot in consolidating the concepts. However, some questions in quizzes are tricky but not in a way that really adds to your understanding of the topic. Overall a pretty good course. (4.5/5 is the rating I would give)

автор: Amitava C

•Apr 18, 2020

The course content is excellent and the instructor makes stuffs easier. Few assignments are very tough but if you go through the course properly can able to solve it. One request to the instructors to a bit slow the pace for better understanding. :)

автор: 谢仑辰

•Mar 07, 2018

Though it just give us a limited amount of information about Machine Learning, it really drive me into the novel world of this field.The course told me a lot of basic concepts about ML, thus I can go through many thesis related to the realm, thanks.

автор: H.-M. F C

•Jan 26, 2019

The course ire great and illustrates many useful topics. The only thing it needs to improve is about the assignment 4 which requires more information to solve the problem, in particular, people who deal with the complete machine learning problem.

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