100+ Exercises - Python - Data Science - scikit-learn - 2023
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
Requirements
- completion of all courses in the Python Developer learning path
- completion of all courses in the Data Scientist learning path
- basic knowledge of NumPy
- basic knowledge of Pandas
- basic knowledge of scikit-learn and machine learning concepts
- I have courses which can assist in obtaining all the necessary skills for this course
Description
Welcome to the 100+ Exercises - Python - Data Science - scikit-learn course where you can test your Python programming skills in machine learning, specifically in scikit-learn package.
Topics you will find in the exercises:
preparing data to machine learning models
working with missing values, SimpleImputer class
classification, regression, clustering
discretization
feature extraction
PolynomialFeatures class
LabelEncoder class
OneHotEncoder class
StandardScaler class
dummy encoding
splitting data into train and test set
LogisticRegression class
confusion matrix
classification report
LinearRegression class
MAE - Mean Absolute Error
MSE - Mean Squared Error
sigmoid() function
entorpy
accuracy score
DecisionTreeClassifier class
GridSearchCV class
RandomForestClassifier class
CountVectorizer class
TfidfVectorizer class
KMeans class
AgglomerativeClustering class
HierarchicalClustering class
DBSCAN class
dimensionality reduction, PCA analysis
Association Rules
LocalOutlierFactor class
IsolationForest class
KNeighborsClassifier class
MultinomialNB class
GradientBoostingRegressor class
This 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 machine learning and are looking for new challenges. Exercises are also a good test before the interview. Many popular topics were covered in this course.
If you're wondering if it's worth taking a step towards Python, don't hesitate any longer and take the challenge today.
Stack Overflow Developer Survey
According to the Stack Overflow Developer Survey 2021, Python is the most wanted programming language with NumPy being the second most used tool in the "Other Frameworks and Libraries" category. Python passed SQL to become our third most popular technology. Python is the language developers want to work with most if they aren’t already doing so.
Who this course is for:
- everyone who wants to learn by doing
- everyone who wants to improve Python programming skills
- everyone who wants to improve data science skills
- everyone who wants to improve machine learning skills
- everyone who wants to prepare for an interview
- data scientists / data analytics / machine learning engineers
Instructor
EN
Python Developer/Data Scientist/Stockbroker
Founder at e-smartdata[.]org.
Big fan of new technologies!
Graduate of postgraduate studies at the Polish-Japanese Academy of Information Technology in the field of Computer Science and Big Data specialization.
Graduate of MA studies in Financial and Actuarial Mathematics at the Faculty of Mathematics and Computer Science at the University of Lodz. Former PhD student at the faculty of mathematics.
Stockbroker license holder (no 3073).
Lecturer at the GPW Foundation (technical analysis, behavioral finance and portfolio management).
PL
Data Scientist, Securities Broker
Założyciel platformy e-smartdata[.]org
Miłośnik nowych technologii, szczególnie w obszarze sztucznej inteligencji, języka Python 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 (nr 3073).
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 język Python, sztuczna inteligencja, web development oraz rynki finansowe.
IG: e_smartdata