100+ Exercises – Python – Data Science – scikit-learn – 2022

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.