Logistic Regression Practical Case Study (Udemy.com)

Good case study. While as a small piece of logistic regression focused lecture, too much too fundamental staffs. I mean, for zero basis learners, it is not good to start from logistic regression case study directly, while for learners with some foundations like me (I was directed here from the Machine Learning A-Z course, which is good for beginners), this lecture is not concise enough. But overall, amazing course!

learning about Artificial Neural Networks (ANN) was a downright fascinating experience. These fancy networks, inspired by the human brain, sure do shine in things like image and speech recognition and natural language processing. My teacher made the whole complicated mess feel as simple as sweet tea on a hot day. Now, don’t get me wrong, training these ANNs still takes a heap of computational power and loads of data, which ain't always easy to come by. But despite that, their abilities are truly impressive and keep pushing the envelope.

Sometimes some student don't provide review the main reason for that is they want to complete course as soon as possible. This criteria of students include me also. But Now after watching all this videos i think i should give a sincere feedback. I would really like to thank Kirill & Hadelin . Teachers like this are really needed to understand any basic concepts. Their ways of teaching of by practical implication is really a good way in which any student can grap these concepts.

It's a great course with real world cases where I get to understand what the problems are and how to solve them. Very practical. I got this course from Machine Learning A to Z which was delivered by the same instructor Hadelin and he's a very good teacher and always made everything easy to learn.

I must say that it was an extremely enriching experience. The instructor has managed to strike the perfect balance between theory and application, making this complex statistical concept very accessible. The practical case study used to illustrate logistic regression was thoughtfully chosen. It helped me to see how this technique can be applied in a real-world scenario, and I found the step-by-step approach to building and evaluating the model particularly helpful. I especially appreciated the inclusion of code snippets, the guidance on data preprocessing, and the attention to model evaluation metrics. It wasn't just about getting the model to work; it was about understanding why it worked and how to ensure its reliability. The additional resources and exercises provided at the end of the lecture were the cherry on top, allowing me to deepen my understanding further. This lecture is a must for anyone looking to grasp logistic regression in a hands-on and intuitive way. Thank you for this excellent learning opportunity!

Excellent course , and I like that both Python and R are being used in the course. Some hiccups in the presented information apparently because of updates in "components" of the "languages". New concepts were introduced at times without sufficient explanation but it is still a tremendous learning experience. Very engaging instructors and how to dig information out to aid in one's learning experience. Great Course!!!!!

Got an intuition about what can one do after learning the Machine Learning Algorithms. By the way, explanations of the instructor were very clear. Before starting, this real life implementation seemed very difficult to me, but the instructor made it quite easy. Really, it felt way easier than I anticipated. Kudos to Hadelin de Ponteves, Kirill Eremenko and the SuperDataScience team.

I am inspired by what Hadelin De Ponteves sir and Kirill Ermenko sir have created. This course is really great. The machine learning course A-Z is also very great where this course was suggested. I am inspired and very much interested in this field of machine learning. And I hope I can make it my living someday. Thank you very much

Came to this course after finding the link the link in bigger course bonus section of Machine Learning A-Z. It was a nice hands on for logistic regression. Only thing was, i did not take the dataset from what he described, instead i grabbed it from sklearn.datasets. For this very reason i had to scale the dataset and got more accuracy . Enjoy Machine Learning.

I kinda jumped into a difficult lesson. I am a very newbie in python coding. I got errors with my code even though i follow the code carefully, it gave me error-message that the codes has not enough iteration and other stuff that I don't understand. Nevertheless, its okay, because I think this lesson motivated me to learn more. This is the most important part. Thank you so much.