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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