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.
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