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    100% Discount || 100+ Exercises – Python – Data Science – scikit-learn

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

    Requirements

    • completed course ‘200+ Exercises – Programming in Python – from A to Z’
    • completed course ‘210+ Exercises – Python Standard Libraries – from A to Z’
    • completed course ‘150+ Exercises – Object Oriented Programming in Python – OOP’
    • completed course ‘100+ Exercises – Python Programming – Data Science – NumPy’
    • completed course ‘100+ Exercises – Python Programming – Data Science – Pandas’
    • basic knowledge of scikit-learn and machine learning concepts

    Description

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    RECOMMENDED LEARNING PATH

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    PYTHON DEVELOPER:

    • 200+ Exercises – Programming in Python – from A to Z
    • 210+ Exercises – Python Standard Libraries – from A to Z
    • 150+ Exercises – Object Oriented Programming in Python – OOP
    • 150+ Exercises – Data Structures in Python – Hands-On
    • 100+ Exercises – Advanced Python Programming
    • 100+ Exercises – Unit tests in Python – unittest framework
    • 100+ Exercises – Python Programming – Data Science – NumPy
    • 100+ Exercises – Python Programming – Data Science – Pandas
    • 100+ Exercises – Python – Data Science – scikit-learn
    • 250+ Exercises – Data Science Bootcamp in Python

    SQL DEVELOPER:

    • SQL Bootcamp – Hands-On Exercises – SQLite – Part I
    • SQL Bootcamp – Hands-On Exercises – SQLite – Part II

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    COURSE DESCRIPTION

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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, numpypandas 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


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