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

September 11, 2022 Development

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100% OFF Get Course

Deal Score0

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

Requirements

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

Description

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.