Machine learning for NeuroImaging in Python

nilearn

Nilearn enables approachable and versatile analyses of brain volumes. It provides statistical and machine-learning tools, with instructive documentation & friendly community.

It supports general linear model (GLM) based analysis and leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.

Important links

• Official source code repo: https://github.com/nilearn/nilearn/
• HTML documentation (stable release): http://nilearn.github.io/

Office Hours

The nilearn team organizes regular online office hours to answer questions, discuss feature requests, or have any Nilearn-related discussions. Nilearn office hours occur every Friday from 4pm to 5pm UTC, and we make sure that at least one member of the core-developer team is available. These events are held on our on Discord server and are fully open, anyone is welcome to join!

Dependencies

The required dependencies to use the software are:

• Python >= 3.6
• setuptools
• Numpy >= 1.16
• SciPy >= 1.2
• Scikit-learn >= 0.21
• Joblib >= 0.12
• Nibabel >= 3.0
• Pandas >= 0.24

If you are using nilearn plotting functionalities or running the examples, matplotlib >= 1.5.1 is required.

Some plotting functions in Nilearn support both matplotlib and plotly as plotting engines. In order to use the plotly engine in these functions, you will need to install both plotly and kaleido, which can both be installed with pip and anaconda.

If you want to run the tests, you need pytest >= 3.9 and pytest-cov for coverage reporting.

Install

First make sure you have installed all the dependencies listed above. Then you can install nilearn by running the following command in a command prompt:

pip install -U --user nilearn

More detailed instructions are available at http://nilearn.github.io/introduction.html#installation.

Development

Detailed instructions on how to contribute are available at http://nilearn.github.io/development.html

Author: Nilearn
Source Code: https://github.com/nilearn/nilearn 
License: View license

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Python Packages in SQL Server – Get Started with SQL Server Machine Learning Services

Introduction

When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.

Python Packages

When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,

• revoscalepy – This Microsoft Python package is used for remote compute contexts, streaming, parallel execution of rx functions for data import and transformation, modeling, visualization, and analysis.
• microsoftml – This is another Microsoft Python package which adds machine learning algorithms in Python.
• Anaconda 4.2 – Anaconda is an opensource Python package

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Top Machine Learning Projects in Python For Beginners [2021]

If you want to become a machine learning professional, you’d have to gain experience using its technologies. The best way to do so is by completing projects. That’s why in this article, we’re sharing multiple machine learning projects in Python so you can quickly start testing your skills and gain valuable experience.

However, before you begin, make sure that you’re familiar with machine learning and its algorithm. If you haven’t worked on a project before, don’t worry because we have also shared a detailed tutorial on one project:

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Top Machine Learning Projects in Python For Beginners [2021] | upGrad blog

If you want to become a machine learning professional, you’d have to gain experience using its technologies. The best way to do so is by completing projects. That’s why in this article, we’re sharing multiple machine learning projects in Python so you can quickly start testing your skills and gain valuable experience.

However, before you begin, make sure that you’re familiar with machine learning and its algorithm. If you haven’t worked on a project before, don’t worry because we have also shared a detailed tutorial on one project:

The Iris Dataset: For the Beginners

The Iris dataset is easily one of the most popular machine learning projects in Python. It is relatively small, but its simplicity and compact size make it perfect for beginners. If you haven’t worked on any machine learning projects in Python, you should start with it. The Iris dataset is a collection of flower sepal and petal sizes of the flower Iris. It has three classes, with 50 instances in every one of them.

We’ve provided sample code on various places, but you should only use it to understand how it works. Implementing the code without understanding it would fail the premise of doing the project. So be sure to understand the code well before implementing it.

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5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

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