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Get 100percentOFF Coupon For Python for Data Science – NumPy, Pandas & Scikit-Learn Course
Course Description:
Welcome to the Python for Data Science – NumPy, Pandas & Scikit-Learn course, the place you may check your Python programming expertise in information science, particularly in NumPy, Pandas and Scikit-Learn.
Some matters you’ll find within the NumPy workouts:
- working with numpy arrays
- producing numpy arrays
- producing numpy arrays with random values
- iterating by means of arrays
- coping with lacking values
- working with matrices
- studying/writing recordsdata
- 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 programs of equations
Some matters you’ll find within the Pandas workouts:
- working with Sequence
- working with DatetimeIndex
- working with DataFrames
- studying/writing recordsdata
- working with completely different information sorts in DataFrames
- working with indexes
- working with lacking values
- filtering information
- sorting information
- grouping information
- mapping columns
- computing correlation
- concatenating DataFrames
- calculating cumulative statistics
- working with duplicate values
- making ready information to machine studying fashions
- dummy encoding
- working with csv and json filles
- merging DataFrames
- pivot tables
Matters you’ll find within the Scikit-Learn workouts:
- making ready information 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 information 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-Learn packages. It consists of 330 workouts with options. It is a nice check for people who find themselves studying the Python language and information science and are wanting for new challenges. Workouts are additionally a great check earlier than the interview. Many widespread matters had been lined on this course.
In the event you’re questioning if it’s value taking a step in the direction of Python, don’t hesitate any longer and take the problem as we speak.
Who this course is for:
- everybody who needs to study by doing
- everybody who needs to enhance Python programming expertise
- everybody who needs to enhance information science expertise
- everybody who needs to organize for an interview