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

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

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What you’ll learn:
  • 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
Description:

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
  • 110+ Exercises – Python + SQL (sqlite3) – SQLite Databases
  • 250+ Questions – Job Interview – Python Developer

SQL DEVELOPER:

  • SQL Bootcamp – Hands-On Exercises – SQLite – Part I
  • SQL Bootcamp – Hands-On Exercises – SQLite – Part II
  • 110+ Exercises – Python + SQL (sqlite3) – SQLite Databases
  • 200+ Questions – Job Interview – SQL Developer

JOB INTERVIEW SERIES:

  • 250+ Questions – Job Interview – Python Developer
  • 200+ Questions – Job Interview – SQL Developer
  • 200+ Questions – Job Interview – Software Developer – Git
  • 200+ Questions – Job Interview – Data Scientist

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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
    • 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.

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

Enroll Now -:

Free 12800 100% off

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