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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 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.
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
WHAT WILL YOU LEARN IN THIS COURSE:
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