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!

Language : english

Note: 3.6 / 5.0

Description

——————————————————————————

RECOMMENDED LEARNING PATH

——————————————————————————

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


——————————————————————————

COURSE DESCRIPTION

——————————————————————————

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.

Related Posts

Ads Blocker Image Powered by Code Help Pro
Ads Blocker Detected!!!

We have detected that you are using extensions to block ads. Please support us by disabling these ads blocker or add this website to your whitelist.

Refresh