Python-Introduction to Data Science and Machine learning A-Z (Udemy.com)

better and try to enhance with more knowledge so that one can select into a basic intern valuation certification. thanks for your knowledge I think this will help me a bit for my first internship.

This is a good course for anyone, who already knows how to program in Python, to get some preliminary background on statistics, Jupyter Notebook, and Python libraries NumPy, Pandas, SciPy, Matplotlib, Seaborn, and Scikit-Learn, and some very high-level background on machine learning (excluding anything to do with neural networks, as these are not covered in any way) so that you can then spend your own time on researching these and playing with them. The course leaves quite a bit to be desired, however, and can be difficult to follow, if you actually want to take some notes or try out the coding examples yourself. As the course doesn't seem to have been planned with a clear transcript before the trainer recorded it (or maybe the trainer doesn't actually know the subject as well as they should), the trainer tends to not explain things very well and waffles a lot (e.g. talks about how things are "simple" or how things are "basically complicated", and contradicting themselves about whether Seaborn is better than Matplotlib when introducing Seaborn), uses some incorrect terminology (e.g. calling colons two-dots, probably due to a literal translation from French, and referring to output of functions as functions, rather than variables/objects), and makes mistakes in their code and imports libraries that don't actually need to be imported. Sadly, except for explaining how to install Python libraries though PyCharm, the trainer provides no information on how else this could be done and makes no references to official documentation of the libraries being used during demonstrations to explain why the code is being written in the way that it is being written (e.g. why certain arguments are provided to certain functions). So, if you have any level of curiosity, you will most likely have to keep pausing the videos to search for online documentation and other examples to understand why the trainer is writing their code the way that they're writing it and get a much better understanding through your own research. Due to inadequate planning, there is no overarching demonstration of how all of the tools and libraries introduced earlier in the course come together (except a few such examples near the end of the course). For example, Jupyter Notebooks is introduced very early in the course, but none of the coding examples actually make use of it, which leaves us wondering why it was even mentioned in the first place. The coding exercises are additionally difficult to follow because the maximum video resolution of the course videos is 720p, but the trainer's screen was running at a higher resolution when the videos were recorded, so the code in the video is rather difficult to read due to blurring caused by down-sampling of the original higher resolution video.

A very excellent course, especially for beginners, because it provides a simplified explanation of each paragraph, and the best courses are in which the lecturer writes the code with you, thank you very much

All in all it was a good course to get an overview but also some practical experience. I expected to have a deeper dive into Data Science but regarding the fact that the course also addresses people who don't know anything about python it was useful. Only real point of criqitue: the teacher should prepare a little more, the videos are very spontaneous because he is skilled but the 'uhm' and 'ehm' here and there made it hart to listen and stay concentrated.

As mentioned in the course though it is an intro course it explains some parts like machine learning algorithms which was mind-blowing. This course is so amazing and interesting.

I'll give you 4 stars, the course started as I tought pretty well, however it got down when you started to use Pandas, of course, in general whoever who wants to use the course it's really well such as an introduction and after that you can take a look of more information with Google, I studied physics and I think that for people who doesn't know anything about mathematics or actuarial science it's a complety introduction for this world of maths in general

Firstly, I opine the title of this course should have been 'Introduction to Python' instead of 'Intro to Data Science / ML'. Secondly, I think this course is just designed to cover many topics without proper intro/fundamentals. For example: You have no idea what exactly logistic regression is, but you write a basic program to perform it. Concretely, we'l end up writing basic codes for some random algorithms, and remain neither a pro nor a layman.

Though it was a short course but it gave a good understanding of all the libraries tho the part of Machine Learning was tough but it is OK. The only problem that needs to sought out is that some features or functions shown in this course doesn't work in newer updates of those libraries but there are only 2-3 functions out there which doesn't work but overall this course is good and would recommend it to beginners in data science. At last I want to thank Yassin sir to teach us and clear many concepts of data science and helping me to get enhance myself..

The course is amazing... All the basic concepts of Statistics is covered and python tools such as Numpy, pandas, matplotlib, scipy & seaborn And also basics of various ML algorithms as well

First of all ,I'm very thank to you, I'm very fortunate, have a chance to learn from you such a amazing subject, thank you to make this topic very convenient and easy for me. It's very helpful to understand the basic of Data Science, Machine Learning and all associate topics and algorithms from you , hope it'll be improve over time and meet with latest trend in data science. Again thank you very much!