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Top 10 Python Tools For Machine Learning You Must Know
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Python & AI Web Development

Top 10 Python Tools For Machine Learning You Must Know

As we all know, Python is an expressive language with outstanding support. Python continues attracting millions of users all over the world. Exploring the Python tools will equally help us to master the Python Language. As mentioned in the title, this guide aims to present the top 10 Python tools for Machine Learning. Whether you’re a beginner or expert, these will surely help you to completely understand the operation and the process of Python.

 

What is Machine Learning?

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. In this blog, we are going to define the top graded tools for machine learning.

 

Top 10 python tools for machine learning:

  1. Tensor Flow
  2. Shogun
  3. Scikit-Learn
  4. NumPy
  5. SciPy
  6. NLTK
  7. Panda
  8. Theano
  9. Keras
  10. Pattern

#1 TENSOR FLOW:

Tensor flow is the most advanced python framework which not only helps to develop but also to train the Machine Learning models with deep machine learning algorithms. It is a second-generation system and an open-source library used for several purposes such as production and research apart from developing machine learning applications such as neural networks. Also, It has a flexible architecture which helps in easy deployment.

Key features of tensor flow includes the following listed below:

  1. Responsive construct.
  2. Flexible.
  3. Easily trainable.
  4. Open-source.
  5. Event logger, etc.

#2 SHOGUN:

It is the oldest machine learning tool created in 1999 and written in C++. It focuses on Support Vector Machine(SVM) and provides a wide range of Machine Learning methods with transparent and accessible algorithms and free machine learning tools to anyone interested in the field of machine learning. It provides a well-formed python interface which is designed for large scale learning and high-performance speed. The only drawback is, its APIs are difficult to use.

Key features of shogun includes the following listed below:

  1. Capable of processing huge data sets consisting of 10 million samples.
  2. Permits the use of combined kernels.
  3. Offers multiple kernel learning functionality, etc.

#3 SCIKIT-LEARN

This tool offers an accessible and efficient means for intellectual data analysis. Relying on some specialized tools such as NumPy, SciPy, and matplotlib. This tool is basically a universal assistant in solving the classification, regression, and clusterization tasks.

Key features of scikit-learn includes the following listed below:

  1. Classification.
  2. Regression.
  3. Dimensionality reduction.
  4. Clustering.
  5. Model Selection, etc.

#4 NUMPY:

NumPy is one of the basic packages used for mathematical calculations in Python. It effortlessly operates the bulk multidimensional data. It covers even the most specific parts of linear algebra and mathematical analysis, allowing for the most voluminous calculations. Sametime, this will not affect the overall performance at all because the paralleling the code is required only when we work on large-scale software.

Key features of NumPy includes the following listed below:

  1. It is a powerful N-dimensional array object.
  2. Consists of sophisticated (broadcasting) functions.
  3. Contains several tools for integrating C/C++ and Fortran code, etc.

#5 SCIPY:

SciPy is a free library which is built to implement complex mathematical and engineering computations. It solves the multi-staged scientific tasks and it also includes NumPy, IPython, and Pandas packages, which provides all-around approach when combined. All the standard mathematical functions such as differential equation solvents, integral solvents are provided by it. And not only that, few of the peculiar capacities such as gesture and image recognition are also provided. It will be very useful for people who work with MATLAB.

Key features of SciPy includes the following listed below:

  1. It can integrate a system of ordinary differential equations.
  2. It works with the NumPy arrays.
  3. Routines for numerical integration.

#6 NLTK:

NLTK is Natural Language toolkit which is a free, open-source and community-driven project. It is a leading platform to build python programs which could work with the human language data and also provides the easy-to-use interfaces.

Key features of NLTK includes the following listed below:

  1. Text analytics and NLP(Natural Language Processing).
  2. Comparing text analytics, NLP and text mining.
  3. Text classification.
  4. Sentiment Analysis.
  5. Performing sentiment analysis using text classification, etc.

#7 PANDA:

It is a software library used to analyze and manipulate data in python programming. It is a tool used for reading and writing data between in-memory data structures and different file formats. Also, it offers data structures and operations for manipulating numerical tables and time series.

Key features of panda includes the following listed below:

  1. Merging and joining data sets.
  2. Reshaping and pivoting data sets.
  3. Performing split to combine data sets using the group by engine.
  4. Reading data from the file formats such as (CSV, TXT, XLSX).
  5. Analyzing time series.

#8 THEANO:

It is one of the most mature python deep learning libraries that allows the user to efficiently define, optimize and evaluate mathematical expressions that involve multi-dimensional arrays.

Key features of theano includes the following listed below:

  1. Tight integration.
  2. Transparency.
  3. Efficient symbolic differentiation.
  4. Speed.
  5. Stability optimizations, etc.

#9 KERAS:

Keras is a high-level, open-source library written in python that is used to create an artificial neural network to replicate the memory process that is similar to human neurons. It majorly focuses on being user-friendly, modular and extensible.

Key features of Keras includes the following listed below:

  1. Keras prioritizes developer experience
  2. It has broad adoption in the industry and research community.
  3. It makes it easy to turn the models to product.
  4. Has strong multi-GPU support and distributed training support, etc.

#10 PATTERN:

It is a free open-source web mining module that provides tools for data mining, Natural Language Processing(NLP), machine learning. It uses SVM and VSM methods and clusterization for data processing with well-formed documentation. Data processing is done via Google/ Wikipedia APIs/ Twitter/ human voice recognition, etc.

Key features of the pattern includes the following listed below:

  1. It is a free open-source tool.
  2. It is well-documented.
  3. It is bundled with 50+ examples and 350+ unit tests.
  4. It is a web crawler, etc.

Are these tools helpful to you? Want to explore more Python tools? Then you must not miss this,

Top 10 Python Tools For Data Science

Interesting? Then don’t forget to post us your comments below to hear more from our python experts!

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Aishwarya Damani

An aspiring adolescent started her career as Software Engineer Trainee and slowly she's mastering all the rising technologies like Nodejs, Express.js, React.js, MongoDB, Sails. On the other hand, a perfect decision maker who is passionate about coding. Wait! there's half a lie! Yeah, this crazy foodie has an intense long lasting affair on trying different foods & exploring places.

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