100+ Exercises - Python - Data Science - scikit-learn
What you'll learn
- solve over 100 exercises in numpy, pandas and scikit-learn
- deal with real programming problems in data science
- work with documentation and Stack Overflow
- guaranteed instructor support
Course content
- 00:16Info
- 00:12Requirements
- Preview02:56
- Preview03:47
- 09:57Google Colab - Intro
- Preview04:07
- 02:57Introduction to Spyder
- 04:39Anaconda installation - Linux
Requirements
- completed course '200+ Exercises - Programming in Python - from A to Z'
- completed course '210+ Exercises - Python Standard Libraries - from A to Z'
- completed course '150+ Exercises - Object Oriented Programming in Python - OOP'
- completed course '100+ Exercises - Python Programming - Data Science - NumPy'
- completed course '100+ Exercises - Python Programming - Data Science - Pandas'
- basic knowledge of scikit-learn and machine learning concepts
Description
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RECOMMENDED LEARNING PATH
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200+ Exercises - Programming in Python - from A to Z
210+ Exercises - Python Standard Libraries - from A to Z
150+ Exercises - Object Oriented Programming in Python - OOP
100+ Exercises - Unit tests in Python - unittest framework
100+ Exercises - Python Programming - Data Science - NumPy
100+ Exercises - Python Programming - Data Science - Pandas
100+ Exercises - Python - Data Science - scikit-learn
250+ Exercises - Data Science Bootcamp in Python
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COURSE DESCRIPTION
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100+ Exercises - Python - Data Science - scikit-learn
Welcome to the course 100+ Exercises - Python - Data Science - scikit-learn, where you can test your Python programming skills in machine learning, specifically in scikit-learn library.
The course is designed for people who have basic knowledge in Python, numpy, pandas and scikit-learn. It consists of over 100 exercises with solutions.
This is a great test for people who are learning the Python language and machine learning and are looking for new challenges. Exercises are also a good test before the interview.
If you're wondering if it's worth taking a step towards Python, don't hesitate any longer and take the challenge today.
Who this course is for:
- everyone who wants to learn by doing
- everyone who wants to improve their Python programming skills
- everyone who wants to improve their data science skills
- everyone who wants to improve their machine learning skills
- everyone who wants to prepare for an interview
Instructor
![Paweł Krakowiak](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.6 Instructor Rating
- 1,416 Reviews
- 38,480 Students
- 37 Courses
EN
Data Scientist/Python Developer/Securities Broker
Founder at e-smartdata[.]org.
A big fan of new technologies, especially in the areas of artificial intelligence, big data and cloud solutions.
A graduate of postgraduate studies at the Polish-Japanese Academy of Information Technology in the field of Computer Science in the Big Data specialization.
A graduate of Master's Degree in Financial and Actuarial Mathematics at the Faculty of Mathematics and Computer Science of the University of Lodz.
Stockbroker license holder with experience in teaching at a university.
Lecturer at the GPW Foundation (technical analysis, behavioral finance and portfolio management).
The main areas of interest are artificial intelligence, machine learning, deep learning and financial markets.
PL
Data Scientist, Securities Broker
Założyciel platformy e-smartdata[.]org
Miłośnik nowych technologii, szczególnie w obszarze sztucznej inteligencji, big data oraz rozwiązań chmurowych.
Absolwent podyplomowych studiów na Polsko-Japońskiej Akademii Technik Komputerowych na kierunku Informatyka, spec. Big Data.
Absolwent studiów magisterskich z matematyki finansowej i aktuarialnej na wydziale Matematyki i Informatyki Uniwersytetu Łódzkiego.
Od 2015 roku posiadacz licencji maklera papierów wartościowych z uprawnieniami do czynności doradztwa inwestycyjnego.
Wykładowca w Fundacji GPW prowadzący szkolenia dla inwestorów z zakresu analizy technicznej, finansów behawioralnych i zasad zarządzania portfelem instrumentów finansowych.
Z doświadczeniem w prowadzeniu zajęć dydaktycznych na wyższej uczelni z przedmiotów związanych z rachunkiem prawdopodobieństwa i statystyką.
Główne obszary zainteresowań to sztuczna inteligencja, uczenie maszynowe, uczenie głębokie i rynki finansowe.