100+ Exercises - Python - Data Science - scikit-learn - 2022

100+ Exercises - Python - Data Science - scikit-learn - 2022, Improve your machine learning skills and solve over 100 exercises in python, numpy, pandas and 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

Requirements

• completed course '200+ Exercises - Programming in Python - from A to Z'
• completed course '210+ Exercises - Python Standard Libraries - from A to Z'
• completed course '150+ Exercises - Object Oriented Programming in Python - OOP'
• completed course '100+ Exercises - Python Programming - Data Science - NumPy'
• completed course '100+ Exercises - Python Programming - Data Science - Pandas'
• basic knowledge of scikit-learn and machine learning concepts

Description

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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

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

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

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