Machine Learning in Python

with Iliya Valchanov
4.8/5
(1006)

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
Start course
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 

Curriculum

Student feedback

4.8/5

1006 ratings
5 stars
852 (85%)
4 stars
134 (13%)
3 stars
11 (1%)
2 stars
7 (1%)
1 star
2 (0%)
Filter by rating
  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars
Clear all
Sort
  • Newest
  • Oldest
Clear all
Christina
27.02.2023
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.
Yekatierina Korotova
06.08.2022
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.
Chris Kirk
08.11.2023
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.
DHIRAJ A PRASAD
29.07.2023
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.
Tomas Kejha
20.01.2023
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.
  • 1
  • 2
  • 3
  • ...
  • 17
  • ...
  • 33
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

Courses You May Like

SQL + Tableau + Python

SQL + Tableau + Python

with Martin Ganchev
4.8/5
(313)
5 hours 61 lessons
Deep Learning with TensorFlow 2

Deep Learning with TensorFlow 2

with Iskren Vankov, Iliya Valchanov
4.8/5
(568)
6 hours 166 lessons
Convolutional Neural Networks with TensorFlow in Python

Convolutional Neural Networks with TensorFlow in Python

with Nikola Pulev, Iskren Vankov
4.9/5
(331)
5 hours 96 lessons
Linear Algebra and Feature Selection

Linear Algebra and Feature Selection

with Aleksandar Samsiev, Ivan Manov
4.7/5
(269)
3 hours 64 lessons
Machine Learning with Naïve Bayes

Machine Learning with Naïve Bayes

with Hristina Hristova
4.8/5
(552)
2 hours 14 lessons
Machine Learning in Excel

Machine Learning in Excel

with Ivan Kitov
4.9/5
(385)
8 hours 70 lessons
Machine Learning with Support Vector Machines

Machine Learning with Support Vector Machines

with Elitsa Kaloyanova
4.8/5
(411)
1 hour 15 lessons
Machine Learning with Decision Trees and Random Forests

Machine Learning with Decision Trees and Random Forests

with Nikola Pulev
4.8/5
(474)
2 hours 20 lessons
Machine Learning with K-Nearest Neighbors

Machine Learning with K-Nearest Neighbors

with Hristina Hristova
4.8/5
(409)
2 hours 17 lessons
Machine Learning with Ridge and Lasso Regression

Machine Learning with Ridge and Lasso Regression

with Ivan Manov
4.8/5
(249)
2 hours 19 lessons
The Machine Learning Process A-Z

The Machine Learning Process A-Z

with Ken Jee, Jeff Li
4.8/5
(460)
6 hours 290 lessons
The Machine Learning Algorithms A-Z

The Machine Learning Algorithms A-Z

with Jeff Li, Ken Jee
4.7/5
(245)
5 hours 380 lessons
Machine Learning Deep Dive: Business Applications and Coding Walkthroughs

Machine Learning Deep Dive: Business Applications and Coding Walkthroughs

with Jeff Li, Ken Jee
4.8/5
(113)
3 hours 68 lessons
ChatGPT for Data Science

ChatGPT for Data Science

with Elitsa Kaloyanova
4.7/5
(17)
1 hour 18 lessons

Machine Learning in Python

with Iliya Valchanov

Start Course