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.

72 lessons 5h
Start course

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.

72 High Quality Lessons
20 Practical Tasks
5 Hours of Video
Certificate of Achievement

Skills you will gain

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


690 ratings
5 stars
585 (85%)
4 stars
91 (13%)
3 stars
7 (1%)
2 stars
6 (1%)
1 star
1 (0%)
Filter by rating
  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars
  • Newest
  • Oldest
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!
I have attended courses in school where I thought the teacher did not explain important details like r2. This course and this tutor explains everything in depth and everything gets clear to me now.
This course gives me huge informative about machine learning. and very good method of learning and instructor split the course in core topics this is very help full for us. Thanks, 365datascience
  • 1
  • 2
  • 3
  • ...
  • 12
  • ...
  • 22

“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

Start Course