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

100+ Exercises - Python - Data Science - scikit-learn
Improve your machine learning skills and solve over 100 exercises in python, numpy, pandas and scikit-learn!

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RECOMMENDED LEARNING PATH

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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, numpypandas 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.

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