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, PCAanalysis

• Association Rules

• LocalOutlierFactor class

• IsolationForest class

• KNeighborsClassifier class

• MultinomialNBclass

• 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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