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Working with the Python Machine Learning Library - Scikit-learn

Scikit-learn is a powerful machine learning library that provides a wide variety of modules for data access, data preparation and statistical model building. It has a good selection of clean toy datasets that are great for people just getting started with data analysis and machine learning. Easy access to these data sets removes the hassle of searching for and downloading files from an external data source. The library also enables data processing tasks such as imputation, data standardization and data normalization. These tasks can often lead to significant improvements in model performance.

Scikit-learn also provides a variety of packages for building linear models, tree-based models, clustering models and much more. It provides an easy-to-use interface for each model object type, which facilitates fast prototyping and experimentation with models. Beginners in machine learning will also find the library useful since each model object is equipped with default parameters that provide baseline performance. Overall, Scikit-learn provides many easy-to-use modules and methods for accessing and processing data and building machine learning models in Python. This tutorial will serve as an introduction to some of its functions.

- Scikit-learn Datasets
- Data Imputation
- Data Standardization & Normalization
- Statistical Modeling with Scikit-Learn
- Linear Regression
- Logistic Regression
- Random Forests

#data-science #software-development #software-engineering #python #machine-learning

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Amilestone for open source projects — French President Emmanuel Macron has recently been introduced to Scikit-learn. In fact, in a recent tweet, Scikit-learn creator and Inria tenured research director, Gael Varoquaux announced the presentation of Scikit-Learn, with applications of machine learning in digital health, to the president of France.

He stated the advancement of this free software machine learning library — “started from the grassroots, built by a community, we are powering digital revolutions, adding transparency and independence.”

#news #application of scikit learn for machine learning #applications of scikit learn for digital health #scikit learn #scikit learn introduced to french president

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Scikit-Learn is one of the popular software machine learning libraries. The library is built on top of NumPy, SciPy, and Matplotlib and supports supervised and unsupervised learning as well as provides various tools for model fitting, data preprocessing, model selection and evaluation.

**About:** From the developers of Scikit-Learn, this tutorial provides an introduction to machine learning with Scikit-Learn. It includes topics such as problem setting, loading an example dataset, learning and predicting. The tutorial is suitable for both beginners and advanced students.

**About: **In this project-based course, you will learn the fundamentals of sentiment analysis, and build a logistic regression model to classify movie reviews as either positive or negative. You will learn how to develop and employ a logistic regression classifier using Scikit-Learn, perform feature extraction with The Natural Language Toolkit (NLTK), tune model hyperparameters and evaluate model accuracy etc.

**About: **Python Machine Learning: Scikit-Learn tutorial will help you learn the basics of Python machine learning. You will learn how to use Python and its libraries to explore your data with the help of Matplotlib and Principal Component Analysis (PCA). You will also learn how to work with the KMeans algorithm to construct an unsupervised model, fit this model to your data, predict values, and validate the model.

**About: **Edureka’s video tutorial introduces machine learning in Python. It will take you through regression and clustering techniques along with a demo of SVM classification on the famous iris dataset. This video helps you to learn the introduction to Scikit-learn and how to install it, understand how machine learning works, among other things.

**About:** In this Coursera offering, you will learn about Linear Regression, Regression using Random Forest Algorithm, Regression using Support Vector Machine Algorithm. Scikit-Learn provides a comprehensive array of tools for building regression models.

**About:** In this course, you will learn about machine learning, algorithms, and how Scikit-Learn makes it all so easy. You will get to know the machine learning approach, jargons to understand a dataset, features of supervised and unsupervised learning models, algorithms such as regression, classification, clustering, and dimensionality reduction.

**About:** In this two-hour long project-based course, you will build and evaluate a simple linear regression model using Python. You will employ the Scikit-Learn module for calculating the linear regression while using pandas for data management and seaborn for plotting. By the end of this course, you will be able to build a simple linear regression model in Python with Scikit-Learn, employ Exploratory Data Analysis (EDA) to small data sets with seaborn and pandas.

**About: **This tutorial is available on GitHub. It includes an introduction to machine learning with sample applications, data formats, preparation and representation, supervised learning: training and test data, the Scikit-Learn estimator interface and more.

**About:** This is a two-hour long project-based course, where you will understand the business problem and the dataset and learn how to generate a hypothesis to create new features based on existing data. You will learn to perform text pre-processing and create custom transformers to generate new features. You will also learn to implement an NLP pipeline, create custom transformers and build a text classification model.

#developers corner #learn scikit-learn #machine learning library #scikit learn

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Undoubtedly, Scikit-learn is one of the best machine learning libraries available today. There are several reasons for that. The consistency among Scikit-learn estimators is one reason. You cannot find such consistency in any other machine learning library. The .fit()/.predict() paradigm best describes the consistency. Another reason is that Scikit-learn has a variety of uses. It can be used for classification, regression, clustering, dimensionality reduction, anomaly detection.

Therefore, Scikit-learn is a must-have Python library in your data science toolkit. But, learning to use Scikit-learn is not straightforward. It’s not simple as you imagine. You have to set up some background before learning it. Even while you learning Scikit-learn, you should follow some guidelines and best practices. In this article, I’m happy to share 9 guidelines that worked for me to master the Scikit-learn without giving up the learning process in the middle. Whenever possible, I will include the links to my previous posts which will help you to set up the background and continue to learn the Scikit-learn.

#data-science #scikit-learn #machine-learning #unsupervised-learning #supervised-learning

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If you have just taken your first step in the data science industry and are learning the Python programming language then, being a Pythonist, you should be aware of the Scikit-learn library. If you are seriously considering bringing data science and machine learning into a productive system, then you should be comprehensive for the Scikit-learn Python library. In this article, let us explore the Scikit-Learn Python library and learn different aspects of its utilization.

Scikit-learn is also known with the synonyms like scikits.learn (previously known) or sklearn. It is a free and open-source machine learning library that is used for the Python programming language. The library was developed by David Cournapeau as a Google Summer Code project in 2007. The project was later joined by Matthieu Brucher in 2010. The library was first made public in February 2010, and in just two years, that is, in November 2012, the library became one of the most popular libraries of machine learning on Github. The primary features of the Scikit-learn library include classification, regression, and clustering algorithms (support vector machines, random forests, gradient boosting, k-means, and DBSCAN). The sklearn is designed to deal with numerical and scientific libraries of Python like NumPy and SciPy.

Sklearn is utilized majorly in Python programming language and NumPy is used to extend its high-performance in linear algebra and operations. Some of the core algorithms that are written in Cython also use this library to improvise the performance.

#python #machine learning #ai #scikit-learn #python library

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Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.

The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.

#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources