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

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100% OFF Get Course

What you’ll learn

  • remedy over 330 workouts in NumPy, Pandas and Scikit-Study
  • cope with actual programming issues in knowledge science
  • work with documentation and Stack Overflow
  • assured teacher assist


  • primary data of Python
  • primary data of NumPy, Pandas and Scikit-Study


Welcome to the Python for Data Science – NumPy, Pandas & Scikit-Study course, the place you’ll be able to check your Python programming abilities in knowledge science, particularly in NumPy, Pandas and Scikit-Study.

Some subjects you will discover within the NumPy workouts:

  • working with numpy arrays
  • producing numpy arrays
  • producing numpy arrays with random values
  • iterating by way of arrays
  • coping with lacking values
  • working with matrices
  • studying/writing information
  • becoming a member of arrays
  • reshaping arrays
  • computing primary array statistics
  • sorting arrays
  • filtering arrays
  • picture 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
  • fixing methods of equations

Some subjects you will discover within the Pandas workouts:

  • working with Sequence
  • working with DatetimeIndex
  • working with DataFrames
  • studying/writing information
  • working with totally different knowledge varieties in DataFrames
  • working with indexes
  • working with lacking values
  • filtering knowledge
  • sorting knowledge
  • grouping knowledge
  • mapping columns
  • computing correlation
  • concatenating DataFrames
  • calculating cumulative statistics
  • working with duplicate values
  • getting ready knowledge to machine studying fashions
  • dummy encoding
  • working with csv and json filles
  • merging DataFrames
  • pivot tables

Matters you will discover within the Scikit-Study workouts:

  • getting ready knowledge to machine studying fashions
  • working with lacking values, SimpleImputer class
  • classification, regression, clustering
  • discretization
  • function extraction
  • PolynomialFeatures class
  • LabelEncoder class
  • OneHotEncoder class
  • StandardScaler class
  • dummy encoding
  • splitting knowledge into prepare and check set
  • LogisticRegression class
  • confusion matrix
  • classification report
  • LinearRegression class
  • MAE – Imply Absolute Error
  • MSE – Imply Squared Error
  • sigmoid() perform
  • entorpy
  • accuracy rating
  • DecisionTreeClassifier class
  • GridSearchCV class
  • RandomForestClassifier class
  • CountVectorizer class
  • TfidfVectorizer class
  • KMeans class
  • AgglomerativeClustering class
  • HierarchicalClustering class
  • DBSCAN class
  • dimensionality discount, PCA evaluation
  • Affiliation Guidelines
  • LocalOutlierFactor class
  • IsolationForest class
  • KNeighborsClassifier class
  • MultinomialNB class
  • GradientBoostingRegressor class

This course is designed for individuals who have primary data in Python, NumPy, Pandas and Scikit-Study packages. It consists of 330 workouts with options. This can be a nice check for people who find themselves studying the Python language and knowledge science and are trying for new challenges. Workout routines are additionally check earlier than the interview. Many well-liked subjects had been coated on this course.

Who this course is for:

  • everybody who desires to study by doing
  • everybody who desires to enhance Python programming abilities
  • everybody who desires to enhance knowledge science abilities

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