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Python for Data Science - NumPy, Pandas & Scikit-Learn | Free Udemy Course
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Python for Data Science - NumPy, Pandas & Scikit-Learn | Free Udemy Course

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Price expires 2 weeks ago or 596 uses

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

3.76
(24 ratings)
16184 students
Created by:
Last updated 8/2022Course Language EnglishCourse Caption EnglishCourse Length 01:28:04 to be exact 5284 seconds!Number of Lectures 345
This course includes:
  • 28 mins hours of on-demand video
  • 339 article
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of completion

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 arraysgenerating numpy arraysgenerating numpy arrays with random valuesiterating through arraysdealing with missing valuesworking with matricesreading/writing filesjoining arraysreshaping arrayscomputing basic array statisticssorting arraysfiltering arraysimage as an arraylinear algebramatrix multiplicationdeterminant of the matrixeigenvalues and eignevectorsinverse matrixshuffling arraysworking with polynomialsworking with datesworking with strings in arraysolving systems of equationsSome topics you will find in the Pandas exercises:working with Seriesworking with DatetimeIndexworking with DataFramesreading/writing filesworking with different data types in DataFramesworking with indexesworking with missing valuesfiltering datasorting datagrouping datamapping columnscomputing correlationconcatenating DataFramescalculating cumulative statisticsworking with duplicate valuespreparing data to machine learning modelsdummy encodingworking with csv and json fillesmerging DataFramespivot tablesTopics you will find in the Scikit-Learn exercises:preparing data to machine learning modelsworking with missing values, SimpleImputer classclassification, regression, clusteringdiscretizationfeature extractionPolynomialFeatures classLabelEncoder classOneHotEncoder classStandardScaler classdummy encodingsplitting data into train and test setLogisticRegression classconfusion matrixclassification reportLinearRegression classMAE - Mean Absolute ErrorMSE - Mean Squared Errorsigmoid() functionentorpyaccuracy scoreDecisionTreeClassifier classGridSearchCV classRandomForestClassifier classCountVectorizer classTfidfVectorizer classKMeans classAgglomerativeClustering classHierarchicalClustering classDBSCAN classdimensionality reduction, PCA analysisAssociation RulesLocalOutlierFactor classIsolationForest classKNeighborsClassifier classMultinomialNB classGradientBoostingRegressor classThis 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 doingeveryone who wants to improve Python programming skillseveryone who wants to improve data science skillseveryone who wants to prepare for an interview

Course Content:

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7 Lectures | 28:38
1 Lectures | 00:08
3.76
(24 course ratings)
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