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

    Posted By: ELK1nG
    Python for Data Science - NumPy, Pandas & Scikit-Learn

    Python for Data Science - NumPy, Pandas & Scikit-Learn
    MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
    Language: English | Size: 200 MB | Duration: 1h 28m

    Improve your data science skills and solve over 330 exercises in Python, NumPy, Pandas and Scikit-Learn!

    What you'll learn
    solve over 330 exercises in NumPy, Pandas and Scikit-Learn
    deal with real programming problems in data science
    work with documentation and Stack Overflow
    guaranteed instructor support

    Requirements
    basic knowledge of Python
    basic knowledge of NumPy, Pandas and Scikit-Learn
    Description
    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