Scikit Learn Tutorial

Scikit Learn Tutorial

The Scikit Learn tutorial provides a brief description of how we can use scikit n python. Scikit is an open-source library for python that provides tools for predictive modeling and data analysis. This tutorial is a guide of the set of instructions and it will teach the user how we can use the library to solve the various problems of machine learning. This tutorial covers the basics of the python library.

Overviews of Scikit Learn Tutorial

The scikit learn tutorial will cover the definition of the scikit learn tutorial, overviews of the scikit learn tutorial, why we need to learn the scikit tutorial, scikit applications, scikit example, prerequisite, and target audience. The scikit learn tutorial is intended for users whose contain some background in ML and programming, also it will provide practical hands-on to solve real-world problems.

The scikit learn is a comprehensive guide to using the scikit learn library for ML in python. Scikit learn is a python library that offers multiple tools for multiple tasks which include clustering, regression, and dimensionality reduction. In this tutorial, we have provided an overview of scikit learn. To start this tutorial we need to install scikit in our system. We can use the pip command to install scikit.

Why do we need to learn Scikit Learn tutorial?

There are multiple reasons why we need to learn scikit learn tutorials. The below points show why we are required to learn scikit learn tutorial as follows.

• Popular and Powerful: Scikit-learn is a very powerful and popular python library that is used in ML with comprehensive tools for data processing, data analysis, model selection, and feature selection. Scikit learn is give us a powerful toolset for our ML projects.
• Versatility: Scikit-learn supports a wide range of ML models from traditional models like support vector and linear regression. This will make it a versatile tool for multiple types of ML problems.
• User-friendly: Scikit learn will contain a user-friendly API that is easy to learn and use.
• Community Support: Scikit learn has large community support of users and developers. This is useful when we encounter a problem or we need guidance that how we can use the library.
• High demand: ML is rapidly growing and it will have proficiency in scikit learn. To learn the scikit learn it will open career opportunities in multiple fields, like data science, artificial intelligence, and ML engineering.

Applications

Scikit-learn is used in multiple applications. Below is the application on which we are using scikit learn as follows.

• Classification: We use scikit learn for classification problems, such as identifying spam emails, predicting customer churn, and recognizing images.
• Regression: Scikit learn is also used in a regression problem, such as predicting the prices of housing, demand forecasting, and stock prices.
• Clustering: Scikit learn is also used in regression problems, such as identifying data anomalies and detecting fraud.
• Natural language processing: Scikit learn is also used in natural languages processing problems, such as text classification, topic modeling, and sentiment analysis.
• Dimensionality reduction: Scikit learn is also used in a dimensionality reduction problem, such as reducing the number of features in the dataset to improve the model performance and visualize the high-dimensional data.
• Recommender systems: Scikit learn is used to implement the recommender systems, which help us to personalize the recommendations to users based on their behavior and interests.
• Image processing: Scikit learn is used in problems of image processing, such as face detection, object recognition, and image segmentation.
• Pre-built models: Scikit learn is used in built-in models of machine learning. It will include different machine learning algorithms.
• Model selection and evaluation: Scikit learn provides tools to select a model for the specified task and evaluates the performance of scoring metrics.

Scikit learn is a versatile library that is applied to different types of machine learning tasks and data analysis.

Example

The below example shows scikit to build the machine learning model. To use the scikit we need to install it using the pip command. The below example shows the install scikit as follows.

python -m pip install scikit-learn

To use the scikit learn in our code we need to import it first. The below example shows how we can import the scikit learn model as follows.

import sklearn

In the below example, we are loading the dataset by using the scikit learn library. We are loading the dataset name as iris as follows.

Code:

from sklearn import datasets
irs = datasets.load_iris()
print(irs.data.shape)

After loading the data now in this step we are learning from it and predicting the new data, for this we have created an estimator to call this method as follows.

Code:

from sklearn import svm
from sklearn import datasets
irs = datasets.load_iris()
cl = svm.LinearSVC()
cl.fit(irs.data, irs.target)
cl.predict([[ 3.2,  3.4,  2.3,  0.35]])
print(cl.coef_)

In the below example, we have created a linear regression model by using scikit learn. Below is an example of regression as follows.

Code:

from sklearn import linear_model
re = linear_model.LinearRegression()
re.fit ([[3, 3], [5, 5], [7, 7]], [3, 5, 7])
print (re.coef_)

Below is an example of a simple classification algorithm. The classifier uses the algorithms of trees as follows.

Code:

from sklearn import datasets
ir = datasets.load_iris()
from sklearn import neighbors
kn = neighbors.KNeighborsClassifier ()
kn.fit (ir.data, ir.target)
res = kn.predict ([[0.3, 0.5, 0.7, 0.9]])
print(res)

Pre-requisites

Before we start with the scikit learn tutorial we required a basic understanding of the following prerequisites as follows.

• We are required to install python and Scikit-learn packages in our system.
• Python programming: We required a basic understanding of python programming language. We need to be familiar with python syntax and basic data structures.
• Numpy: This is a python library for scientific computing that provides support for matrices and arrays. Scikit learn relies on the numpy arrays for representing the data.
• Pandas: This is a python library that provides powerful data for analysis and manipulation. It is used to load and clean the data before feeding it into the ML model. We are familiar with the panda’s library.
• Matplotlib: Matplotlib is a python library that is used for data visualization. It will provide a wide range of functions for plotting to create different types of plots. We are familiar with matplotlib and its basic syntax.
• Concept of machine learning: It is an ML library, so a basic understanding of ML is essential. We were familiar with supervised and unsupervised learning.

Target Audience

The target audience of the scikit learn tutorial is vary depends on the level of the tutorial. Scikit learn tutorial aimed at people who are interested in ML to solve real-world problems. The beginner-level Scikit-learn is aimed at individuals who contain a basic understanding of python programming. It is often designed to help individuals to learn the basics of scikit learn, which includes the load and preprocessing of the data.

Scikit learn is aimed at individuals who contain a good understanding of python programming and ML. It will be designed to help individuals learn how we can use scikit learn to solve complex ML problems, such as model tuning, and building pipelines. This tutorial is also helpful for individuals who are strong in python and ML concepts.

Conclusion

The scikit learn tutorial is intended for users whose contain some background in ML and programming, also it will provide practical hands-on to solve real-world problems. Scikit is an open-source library for python that provides tools for predictive modeling and data analysis. Scikit-learn is a very powerful and popular python library that is used in ML with comprehensive tools for data processing.