Scikit-learn in Python: 100+ Data Science Exercises

Master Machine Learning - Unleash the Power of Data Science for Predictive Modeling!

4.85 (90 reviews)
Udemy
platform
English
language
Data Science
category
39,114
students
1 hour
content
Oct 2023
last update
$64.99
regular price

What you will 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

The "Scikit-learn in Python: 100+ Data Science Exercises" course is a comprehensive, hands-on guide to one of the most essential libraries for machine learning in Python, Scikit-learn. This course employs a practical, exercise-driven approach that helps learners understand and apply various machine learning algorithms and techniques.

The course is organized into different sections, each devoted to a specific aspect of the Scikit-learn library. It covers everything from data preprocessing, including feature extraction and selection, to various machine learning models such as linear regression, decision trees, support vector machines, and ensemble methods, to model evaluation and hyperparameter tuning.

Each section is packed with carefully designed exercises that reinforce each concept and give you the chance to apply what you've learned. You will solve real-world problems that mirror the challenges faced by data scientists in the field. Detailed solutions accompany each exercise, enabling you to compare your work and gain a better understanding of how to best use Scikit-learn for machine learning tasks.

The "Scikit-learn in Python: 100+ Data Science Exercises" course is perfect for anyone interested in expanding their data science toolkit. Whether you're a beginner looking to dive into machine learning, or a seasoned data scientist wanting to refine your skills, this course offers an enriching learning experience.


Scikit-learn - Unleash the Power of Machine Learning!

Scikit-learn is a versatile machine learning library in Python that provides a wide range of algorithms and tools for building and implementing machine learning models. It is widely used by data scientists, researchers, and developers to solve complex problems through classification, regression, clustering, and more. With Scikit-learn, you can efficiently preprocess data, select appropriate features, train and evaluate models, and perform model selection and hyperparameter tuning. It offers a consistent API, making it easy to experiment with different algorithms and techniques. Scikit-learn also provides useful utilities for data preprocessing, model evaluation, and model persistence. Its user-friendly interface and extensive documentation make it a go-to choice for machine learning practitioners looking to leverage the power of Python for their projects.


Topics you will find in this course:

  • 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

Content

Configuration (optional)

Intro
Info
Google Colab + Google Drive
Google Colab + GitHub
Google Colab - Intro
Anaconda installation - Windows 10
Introduction to Spyder
Anaconda installation - Linux

Tips

Intro
A few words from the author

Starter

Exercise 0
Solution 0

Exercises 1-10

Exercise 1
Solution 1
Exercise 2
Solution 2
Exercise 3
Solution 3
Exercise 4
Solution 4
Exercise 5
Solution 5
Exercise 6
Solution 6
Exercise 7
Solution 7
Exercise 8
Solution 8
Exercise 9
Solution 9
Exercise 10
Solution 10

Exercises 11-20

Exercise 11
Solution 11
Exercise 12
Solution 12
Exercise 13
Solution 13
Exercise 14
Solution 14
Exercise 15
Solution 15
Exercise 16
Solution 16
Exercise 17
Solution 17
Exercise 18
Solution 18
Exercise 19
Solution 19
Exercise 20
Solution 20

Exercises 21-30

Exercise 21
Solution 21
Exercise 22
Solution 22
Exercise 23
Solution 23
Exercise 24
Solution 24
Exercise 25
Solution 25
Exercise 26
Solution 26
Exercise 27
Solution 27
Exercise 28
Solution 28
Exercise 29
Solution 29
Exercise 30
Solution 30

Exercises 31-40

Exercise 31
Solution 31
Exercise 32
Solution 32
Exercise 33
Solution 33
Exercise 34
Solution 34
Exercise 35
Solution 35
Exercise 36
Solution 36
Exercise 37
Solution 37
Exercise 38
Solution 38
Exercise 39
Solution 39
Exercise 40
Solution 40

Exercises 41-50

Exercise 41
Solution 41
Exercise 42
Solution 42
Exercise 43
Solution 43
Exercise 44
Solution 44
Exercise 45
Solution 45
Exercise 46
Solution 46
Exercise 47
Solution 47
Exercise 48
Solution 48
Exercise 49
Solution 49
Exercise 50
Solution 50

Exercises 51-60

Exercise 51
Solution 51
Exercise 52
Solution 52
Exercise 53
Solution 53
Exercise 54
Solution 54
Exercise 55
Solution 55
Exercise 56
Solution 56
Exercise 57
Solution 57
Exercise 58
Solution 58
Exercise 59
Solution 59
Exercise 60
Solution 60

Exercises 61-70

Exercise 61
Solution 61
Exercise 62
Solution 62
Exercise 63
Solution 63
Exercise 64
Solution 64
Exercise 65
Solution 65
Exercise 66
Solution 66
Exercise 67
Solution 67
Exercise 68
Solution 68
Exercise 69
Solution 69
Exercise 70
Solution 70

Exercises 71-80

Exercise 71
Solution 71
Exercise 72
Solution 72
Exercise 73
Solution 73
Exercise 74
Solution 74
Exercise 75
Solution 75
Exercise 76
Solution 76
Exercise 77
Solution 77
Exercise 78
Solution 78
Exercise 79
Solution 79
Exercise 80
Solution 80

Exercises 81-90

Exercise 81
Solution 81
Exercise 82
Solution 82
Exercise 83
Solution 83
Exercise 84
Solution 84
Exercise 85
Solution 85
Exercise 86
Solution 86
Exercise 87
Solution 87
Exercise 88
Solution 88
Exercise 89
Solution 89
Exercise 90
Solution 90

Exercises 91-100

Exercise 91
Solution 91
Exercise 92
Solution 92
Exercise 93
Solution 93
Exercise 94
Solution 94
Exercise 95
Solution 95
Exercise 96
Solution 96
Exercise 97
Solution 97
Exercise 98
Solution 98
Exercise 99
Solution 99
Exercise 100
Solution 100

Exercises 100+

Exercise 101
Solution 101

Screenshots

Scikit-learn in Python: 100+ Data Science Exercises - Screenshot_01Scikit-learn in Python: 100+ Data Science Exercises - Screenshot_02Scikit-learn in Python: 100+ Data Science Exercises - Screenshot_03Scikit-learn in Python: 100+ Data Science Exercises - Screenshot_04

Reviews

Liang
September 2, 2022
Course concept is good. But it's necessary to put a title on each lecture and describe the reason why we do so. There's no excercises concerning 'cross_val_score', 'RobustScaler', 'ROC curve' and etc. which I expected. But in total it's nice course.
Ivar
August 13, 2021
The course is perfect! I was looking for more than a year for a all-in-one-place course to hone your python skills (for SQL there are alternatives). Of course, you can find similar exercises across the web, but for $10 you have everything curated and easy to access. I'm going to get of data-science related courses as well. Dziekuje bardzo!

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3305776
udemy ID
7/6/2020
course created date
7/24/2020
course indexed date
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