100% Discount || Python for Data Science – NumPy, Pandas & Scikit-Learn

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    Python for Data Science – NumPy, Pandas & Scikit-Learn


    • basic knowledge of Python
    • basic knowledge of NumPy, Pandas and Scikit-Learn


    Welcome to the Python for Data Science – NumPy, Pandas & Scikit-Learn course, where you can test your Python programming skills in data science, specifically in NumPy, Pandas and Scikit-Learn.

    Some topics you will find in the NumPy exercises:

    • working with numpy arrays
    • generating numpy arrays
    • generating numpy arrays with random values
    • iterating through arrays
    • dealing with missing values
    • working with matrices
    • reading/writing files
    • joining arrays
    • reshaping arrays
    • computing basic array statistics
    • sorting arrays
    • filtering arrays
    • image as an array
    • linear algebra
    • matrix multiplication
    • determinant of the matrix
    • eigenvalues and eignevectors
    • inverse matrix
    • shuffling arrays
    • working with polynomials
    • working with dates
    • working with strings in array
    • solving systems of equations

    Some topics you will find in the Pandas exercises:

    • working with Series
    • working with DatetimeIndex
    • working with DataFrames
    • reading/writing files
    • working with different data types in DataFrames
    • working with indexes
    • working with missing values
    • filtering data
    • sorting data
    • grouping data
    • mapping columns
    • computing correlation
    • concatenating DataFrames
    • calculating cumulative statistics
    • working with duplicate values
    • preparing data to machine learning models
    • dummy encoding
    • working with csv and json filles
    • merging DataFrames
    • pivot tables

    Topics you will find in the Scikit-Learn 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 packages. It consists of 330 exercises with solutions. This is a great test for people who are learning the Python language and data science 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 Python programming skills
    • everyone who wants to improve data science skills
    • everyone who wants to prepare for an interview

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