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.

5 hours 72 lessons
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72 High Quality Lessons
20 Practical Tasks
5 Hours of Video
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


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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.
Another course in the Data Science Career Path that I really enjoyed! Using all the theoretical aspects of data science and seeing them in practice was great. Everything was well explained and I am a big fan of the Jupyter notebooks with commentaries that I can use anytime I need a refresher.
The Machine Learning In Python course is an awesome resource for my ESG and Sustainability in Project Management. The learnings that I obtained from creating Heat Map using Seaborn has been most helpful amongst others. Highly recommended!
The course was exceptional and it covered what it set out to! The only thing I felt was that the mathematics behind clustering & regression was just glossed over. But programming wise it's top-notch.
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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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