# 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!

**Language**: english

**Note**: 4.5/5 (77 notes) 30,942 students

**Instructor(s)**: Paweł Krakowiak

**Last update**: 2022-10-24

## 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

- completion of all courses in the Python Developer learning path
- completion of all courses in the Data Scientist learning path
- basic knowledge of NumPy
- basic knowledge of Pandas
- basic knowledge of scikit-learn and machine learning concepts
- I have courses which can assist in obtaining all the necessary skills for this course

## Description

Welcome to the **100+ Exercises – Python – Data Science – scikit-learn** course 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*classclassification, regression, clustering

discretization

feature extraction

*PolynomialFeatures*class*LabelEncoder*class*OneHotEncoder*class*StandardScaler*classdummy encoding

splitting data into train and test set

*LogisticRegression*classconfusion matrix

classification report

*LinearRegression*class*MAE*– Mean Absolute Error*MSE*– Mean Squared Error*sigmoid()*functionentorpy

accuracy score

*DecisionTreeClassifier*class*GridSearchCV*class*RandomForestClassifier*class*CountVectorizer*class*TfidfVectorizer*class*KMeans*class*AgglomerativeClustering*class*HierarchicalClustering*class*DBSCAN*classdimensionality reduction,

*PCA*analysisAssociation 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.

**Stack Overflow Developer Survey**

According to the Stack Overflow Developer Survey 2021, Python is the most wanted programming language with NumPy being the second most used tool in the “Other Frameworks and Libraries” category. Python passed SQL to become our third most popular technology. Python is the language developers want to work with most if they aren’t already doing so.

## Who this course is for

- everyone who wants to learn by doing
- everyone who wants to improve Python programming skills
- everyone who wants to improve data science skills
- everyone who wants to improve machine learning skills
- everyone who wants to prepare for an interview
- data scientists / data analytics / machine learning engineers

## Course content

- Configuration (optional)
- Info
- Requirements
- Google Colab + Google Drive
- Google Colab + GitHub
- Google Colab – Intro
- Anaconda installation – Windows 10
- Introduction to Spyder
- Anaconda installation – Linux

- Tips
- 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

- Bonus
- Bonus

**Time remaining or 982 enrolls left**

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