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100+ Exercises – Python – Data Science – scikit-learn

05 Aug, 2021by Admin 20
100+ Exercises – Python – Data Science – scikit-learn
100+ Exercises – Python – Data Science – scikit-learn

Get Udemy Coupon 100% OFF For 100+ Exercises – Python – Data Science – scikit-learn Course

PYTHON DEVELOPER:

200+ Exercises – Programming in Python – from A to Z 210+ Exercises – Python Standard Libraries – from A to Z 150+ Exercises – Object Oriented Programming in Python – OOP 150+ Exercises – Data Structures in Python – Hands-On 100+ Exercises – Advanced Python Programming 100+ Exercises – Unit tests in Python – unittest framework 100+ Exercises – Python Programming – Data Science – NumPy 100+ Exercises – Python Programming – Data Science – Pandas 100+ Exercises – Python – Data Science – scikit-learn 250+ Exercises – Data Science Bootcamp in Python

SQL DEVELOPER:

SQL Bootcamp – Hands-On Exercises – SQLite – Part I SQL Bootcamp – Hands-On Exercises – SQLite – Part II

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COURSE DESCRIPTION

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100+ Exercises – Python – Data Science – scikit-learn

Welcome to the course 100+ Exercises – Python – Data Science – scikit-learn where you can test your Python programming skills in machine learning, specifically in scikit-learn package.

Topics you will find in the exercises:

preparing data to machine learning models working with missing values, SimpleImputer class classification, regression, clustering discretization feature extraction PolynomialFeatures class LabelEncoder class OneHotEncoder class StandardScaler class dummy encoding splitting data into train and test set LogisticRegression class confusion matrix classification report LinearRegression class MAE – Mean Absolute Error MSE – Mean Squared Error sigmoid() function entorpy accuracy score DecisionTreeClassifier class GridSearchCV class RandomForestClassifier class CountVectorizer class TfidfVectorizer class KMeans class AgglomerativeClustering class HierarchicalClustering class DBSCAN class dimensionality reduction, PCA analysis Association Rules LocalOutlierFactor class IsolationForest class KNeighborsClassifier class MultinomialNB class GradientBoostingRegressor class

This course is designed for people who have basic knowledge in Python, numpy, pandas and scikit-learn. It consists of over 100 exercises with solutions.

This is a great test for people who are learning machine learning 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 doing everyone who wants to improve their Python programming skills everyone who wants to improve their data science skills everyone who wants to improve their machine learning skills everyone who wants to prepare for an interview

WHAT WILL YOU LEARN IN THIS COURSE:

solve over 100 exercises in numpy, pandas and scikit-learn
deal with real programming problems in data science
work with documentation and Stack Overflow
guaranteed instructor support

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IF YOU FIND THIS FREE UDEMY COURSEPython“USEFUL AND HELPFUL PLEASE GO AHEAD SHARE THE KNOWLEDGE WITH YOUR FRIENDS WHILE THE COURSE IS STILL AVAILABLE


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