Top 10 Python tools for machine learning and data science - JAXenter

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Experts have made it quite clear that 2018 will be a bright year for artificial intelligence and machine learning. Some of them have also expressed their opinion that "Machine learning tends to have a Python flavor because it's more user-friendly than Java". When it comes to data science, Python's syntax is the closest to the mathematical syntax and, therefore, is the language that is most easily understood and learned by professions like mathematicians or economists. Here I will present my top 10 list of the most useful Python tools for both machine learning and data science applications. If you feel like deepening your knowledge in either field and you don't know where to start, this is the best place for you!


Intro to Machine Learning for Developers - DZone AI

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Welcome to the world of machine learning with scikit-learn. Machine learning can be overwhelming at times, and this is partly due to a large number of tools that are available on the market. This post will simplify this process of tool selection down to one -- scikit-learn. In this series, you will learn how to construct an end-to-end machine learning pipeline using some of the most popular algorithms that are widely used in industry and professional competitions, such as Kaggle. Now, let's begin this fun journey into the world of machine learning with scikit-learn!


Best Python Tools For Machine Learning And Data Science

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Python programming language has huge libraries and frameworks to facilitate coding and save development time. It is famed for its simplicity, easily readable code, and brief syntax and logic. Since machine learning deals with extremely complex algorithms and multi-stage workflows, here python's brief and easy logics play important role in saving developer's time. On the other hand, when it comes to Data Science, Python has packages that are rooted specifically for data science job. SciPy, NumPy, and pandas facilitate data analysis and can be easily integrated with web apps.


5 Best Frameworks For Machine Learning

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In this article, let's check about some of the best frameworks and libraries for Machine Learning. This list is created by me based on a variety of parameters, some would surely not accept it but again it is according to me and would vary from person to person. If you are a beginner, check out our articles on "Machine learning crash course" and "Machine learning specialization course". Each of these Frameworks is different from each other and takes much time to learn, during the time of making this list we took care of features other than the basic ones, User base and community & support was one of the most important parameters. Some frameworks are more mathematically oriented, and hence geared more towards statistical than neural networks.


Pomegranate: fast and flexible probabilistic modeling in python

arXiv.org Machine Learning

We present pomegranate, an open source machine learning package for probabilistic modeling in Python. Probabilistic modeling encompasses a wide range of methods that explicitly describe uncertainty using probability distributions. Three widely used probabilistic models implemented in pomegranate are general mixture models, hidden Markov models, and Bayesian networks. A primary focus of pomegranate is to abstract away the complexities of training models from their definition. This allows users to focus on specifying the correct model for their application instead of being limited by their understanding of the underlying algorithms. An aspect of this focus involves the collection of additive sufficient statistics from data sets as a strategy for training models. This approach trivially enables many useful learning strategies, such as out-of-core learning, minibatch learning, and semi-supervised learning, without requiring the user to consider how to partition data or modify the algorithms to handle these tasks themselves. pomegranate is written in Cython to speed up calculations and releases the global interpreter lock to allow for built-in multithreaded parallelism, making it competitive with---or outperform---other implementations of similar algorithms. This paper presents an overview of the design choices in pomegranate, and how they have enabled complex features to be supported by simple code.