Machine Learning in Python

with Iliya Valchanov

Sharpening your predictive modelling skills to set you apart as a data scientist instead of data analyst covers regressions, classifications, and clustering.

7 hours 72 lessons
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72 High Quality Lessons
20 Practical Tasks
7 Hours of Content
Certificate of Achievement

Course Overview

Machine Learning in Python builds upon the statistical knowledge you gained earlier in the program. This course focuses on predictive modelling and enters multidimensional spaces which require an understanding of mathematical methods, transformations, and distributions. We will introduce these concepts, as well as complex means of analysis such as clustering, factoring, Bayesian inference, and decision theory, while also allowing you to exercise your Python programming skills.

Topics covered

data analysismachine learningProgrammingPythonTheory

What You'll Learn

This course is focused on predictive modelling via an array of approaches such as linear regression, logistic regression, and cluster analysis. It combines comprehensive theory with lots of practice to allow you to exercise your Python skills.

Learn the fundamentals of predictive modelling 
Understand the theory behind linear regression 
Perform linear regression with sklearn 
Grasp logistic regression 
Approach cluster analysis 
Implement K-means clustering 


Student feedback


1136 ratings
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Being able to code along is an important part of knowledge retention. The information was being given way too fast and forced me to pause multiple times to be able to code along. The resources that have the finalized versions are, imo, not a sufficient replacement in terms of learning value. Moreover I believe a variety of concepts could have been broken down more to increase understanding; it seems like a lot of assumptions were made about what the user would know, and I often found myself a bit lost even having taken all the other courses of the Data Science career path in order.
There are 2 repetitive videos about overfitting in different sections. Also in the last lecture, the lecturer said ' But this topic for a next section' but there is no next section. The same was for scalers. The lecturer said smth like this "We will show how to scale different features separately", but never showed. And I expected something deeper and also with some practical homework/exercises which can be automatically checked. Like "Write code to show scatter plot of "Salaries" csv file". It is not bad, quite interesting but too way kinda shallow.
I am really enjoying the content of these videos so far and think I am understanding it well. If I am being picky I personally find the very short videos a little perplexing, and the "see you next time" at the end of the video starting to put me off watching the next video. I would rather have longer videos with a very brief summary of the next video, with no overly excited "see you there" etc, at least with the frequency of the small videos the repetition starts to seem a little ingenious, maybe this will be less of an issue with longer videos.
This course did not made me feel confident as others and a bit fast but very understandable. Must have more concentration and write down things to understand the concept that is been tought. I gave it a 4 star because I was expecting it to be very intresting but was little hard to understand since still a above beginner in python and I got many errors and using CHATGPT I could eleminate those errors. I would like to go in depth after finishing the entire DataScience course.
This was a very good introduction to the concept of ML and the most common statistical methods. I enjoyed the practical examples that showed the whole process from the ground up. It also provides a fair amount of homework for us to apply the knowledge ourselves. While it ends quite abruptly, promising to go into another section without actually doing so I feel the material here is definitely worth the time. Thank you!
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Iliya Valchanov

“This is the place where you will learn the advanced statistical techniques that are used by successful data scientists. I will teach you regression analysis, clustering, and factor analysis. After this course, you’ll be able to fill your resume with skills and have plenty left over to show off at the interview.”

Iliya Valchanov

Co-founder at 365 Data Science

Machine Learning in Python

with Iliya Valchanov

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