Description

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

Some matters you’ll 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 fundamental 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 matters you’ll discover within the Pandas workouts:

  • working with Sequence

  • working with DatetimeIndex

  • working with DataFrames

  • studying/writing information

  • working with completely different knowledge sorts 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

  • making ready knowledge to machine studying fashions

  • dummy encoding

  • working with csv and json filles

  • merging DataFrames

  • pivot tables

Subjects you’ll discover within the Scikit-Be taught workouts:

  • making 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 practice 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 fundamental data in Python, NumPy, Pandas and Scikit-Be taught packages. It consists of 330 workouts with options. It is 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 standard matters had been coated on this course.

In the event you’re questioning if it is price taking a step in direction of Python, do not hesitate any longer and take the problem immediately.

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