Machine Learning with Python by IBM

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed!

Created by: Saeed Aghabozorgi

Produced in 2018

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Overall Score : 94 / 100

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Course Description

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components:First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed!By just putting in a few hours a week for the next few weeks, this is what you'll get. 1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy 2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more.3) And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media.If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course.

Instructor Details

Saeed Aghabozorgi

Saeed Aghabozorgi, PhD is a Sr. Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients' ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like deep learning, machine learning and statistical modelling on large datasets.



146 total reviews

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By Radhika K on 7-Feb-19

The course concentrates more on Maths rather than explaining how algorithm can be implemented in Python. This is difficult for a someone with less knowledge in Maths. The lab exercises when compared to rest of the course is not satisfactory because in lab sessions, the algorithms were not explained and lacks Student excercise. It also lacks clarity around when to use which algorithm.

By Karim C N on 5-Jun-19

This was my favorite course in the specialization and hence the only one that gets my 5* rating!Everything was well explained and thorough meaning I did not get lost. The quizzes were challenging but fair. The final project was spot on and related perfectly with what has been learnt (unlike many other final projects in this specialization). Overall a very good experience.The only constructive criticism I would give would be for the videos to give a quick overview/introduction of the code used in Python for the algorithms, which is then practiced in the labs. At the moment, the videos give an excellent explanation to everything but you don't see the actual code used until the lab.

By Dylan H on 10-Jun-19

Good theoretical background on how some machine learning tasks actually work mathematically, but, to be quite frank, much of it is a) not necessary, (i.e. I've used regression as a prime aspect of my job for approaching 3 decades now, and have never known that it used partial derivatives to determine what elements to vary, but, quite frankly, that knowledge has never been required or even vaguely useful for either its use or explanation) b) presented in a way that, as soon as it begins to get interesting from an algorithmic standpoint, stops with a "beyond the scope of this class," (to be fair, I have a -major- pet peeve about that phrase from working with developers for decades) and c) if such depth of knowledge was considered important, it should have been split up amongst more classes - i.e. at the point someone takes this class, they've been through 7 other classes in the IBM Data Science track, and only 3 of them have presented enough and important enough info that I've even bothered to keep notes for future reference. Instead of 4 classes that effectively wasted all of our time, (including the two whole intro classes) if the background mathematics is important, (again, I would venture that, to a non-expert-level general practitioner, which this class is aimed at, it's just not) move some of it out of this class and into some of the others so that we don't end up with effectively two important classes out of 9 - the Data Analysis with Python class, for being the most challenging mechanically, (i.e. what -exactly- should I be typing in at the command prompt to get what I want to happen) and this one, for being the most challenging theoretically. Would very much like to see a re-work of the overall curriculum to better space out the effort vs time invested relationship.

By Vincent L on 13-Sep-18

Errors in the presentations and in the Jupyter workbooks, plenty of typos. Not professional at all.The course does cover the topics and give us some practice exercises, but when I don't get the right result I cannot know if I don't understand a topic properly or if the instructor made a mistake without checking on other web resources. Plus, some approaches are just dubious, like normalizing by dividing by the max value. There are many other ways to do so that make no assumption on the data distribution.

By Rama S C on 7-Feb-19

The course was highly informative and very well presented. It was very easier to follow. Many complicated concepts were clearly explained. It improved my confidence with respect to programming skills.

By Dr S K on 5-Nov-18

The courses are good, but they presume the student knows very good python programming. The lectures are nice and concise but they do not go in too much depth and there is some disparity between the depth of knowledge that is needed in the labs vs the lectures. The labs assume very good programming expertise.

By SIDDHANT K on 4-Aug-19

The instructor was awesome. His voice was crisp and to the point. The course is actually well laid out with proper structure. Altogether a great learning experience. Cheers... Keep up the good work.

By Peter H on 5-May-19

Probably this is one of the course within the program that will give you the most important background on what Data Science is about. It is relatively easy to understand each algorithm with the support of the labs and the Notebooks provided by the team. The project at the end of the course is really interesting and challenging.

By RAVIKUMAR M on 12-Dec-18

Good Start with detailed explanation about each element in the syllabus. I thoroughly enjoyed working with labs and assignments. After the course, You'll have a solid understanding and you can explore almost any algorithm and understand it intuitively.

By Mike D on 19-Nov-18

Really high quality videos and labs.This is the best Coursera course I have taken so far, and I have taken many.Great job Saeed!

By Kevin L K on 12-Mar-19

This was an extremely hard course to understand because of the very dense mathematics. The laboratories were filled with typos which made understanding the concepts much harder. Sometimes code would even be wrong. Please review the labs carefully and try to explain the concepts better. It also helps when you explain what your code is doing so students can understand what is being written.

By APARAJITO S on 23-Oct-18

I am thoroughly enjoying the course. The codes written are the shortest possible codes but the narrations are just fabulous to comprehend and remember. I need more practice to write the codes correctly by my own but my fundas are all cleared and I know exactly why am I doing the next step.