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There must have been many times when you tried to find a library in Python to help you with your Machine Learning project. However you’ve experience one thing quite often! With so many Python libraries available today and many been launching greatly after every leap year, picking the right one isn’t easy.

There are times when you spend hours searching for the right library so that your programs may become compatible than the others, however then you choose a incompatible one and waste your time learning something that doesn’t work well or may it work great but the program have become just as large as an elephant. And you all know, indentations are a must in Python along with tabs and spaces. To make things easier, I’ve compiled the top 15 Basic Python libraries you all need to know.

What Is Machine Learning?

Machine learning is a subfield of computer science that lets computers learn (i.e., progressively improve performance on a specific task)with data, without being explicitly programmed. One way to think about machine learning is that “The more information machines ingest, process and analyze, the more suitable are the programs those could be stable to survive in new environment.”

Machine learning relies on statistical analysis; based on patterns in past performance — combined with algorithms modeled on what humans do when presented with problems or data— machines can learn to perform certain tasks better than any individual could by doing it manually.

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Where Is ML Used?

Machine learning has become an important part of every industry, and almost large companies use it in some capacity. As a result, there are plenty of places where you can apply ML to your work.

Whether you’re a researcher or a developer, looking at how machine learning is implemented in real-world situations can help you envision how it could be used in your own projects and learn more about what’s possible with existing tools. These site has plenty of articles of when and why specific businesses use ML and gives you a good idea of what works well.

1. SHAP

SHAP is a library for doing eXplainable Artificial Intelligence (XAI). It was developed by the University of Pennsylvania(Cal U). Calculations from the discipline of game theory are used in order to determine which factors have the most impact on the predictions made by machine learning algorithms.

If you are working with black box models, SHAP will help you understand how choices are made (random forest or neural networks). You may get explanations for individual forecasts as well as for the whole group of predicted outcomes. Use of its API is a simple and straightforward process.

Check website: SHAP

2. Keras

Keras is a high-level interface for dealing with neural networks that is easy to learn and use. The Keras interface is far more user-friendly than the TensorFlow interface. Its primary advantage is the ease with which it may be used.

With Keras, it is quite simple to see whether our ideas would provide positive outcomes in a short period of time. According to Quora, Keras works with other deep learning libraries (such as TensorFlow, CNTK, or Theano) in a transparent manner to complete the tasks we assign to it.

Check website: Keras

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3. Gensim

Radim Rehurek designed Gensim, a library for natural language processing that is now widely used. The subject modeling capability of Gensim is a significant advantage. That is, it is capable of automatically determining the subject matter of a collection of documents.

Additionally, Gensim is excellent for creating or importing distributed vector representations, such as word2vec, into a simulation. Using Gensim, we can also compare and contrast the similarity of two papers, which is quite beneficial when doing searches.

Check website: Gensim

4. Anaconda

Numerical computing, data analysis, and machine learning are all supported by Anaconda, a python distribution. It comprises the libraries that data scientists find to be the most useful. It also makes it extremely simple to install any other libraries that you may want.

If you are working on many projects at the same time, it is also feasible to establish several working environments using Anaconda. Suppose one of the projects requires Python 3 and the other requires Python 2. This may be handy in situations such as these. Alternatively, if you are working on a project that necessitates the use of certain libraries or that requires a specific version, you should consult the documentation.

Check website: Anaconda

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5. Matplotlib

Matplotlib is the most widely used and well-known graphics package for Python. The matplotlib library may be used to produce graphs of the high quality required for publication on both print and digital media.

Matplotlib allows you to construct a wide variety of plots, including time series, histograms, power spectra, bar charts, error plots, and more.

Check website: Matplotlib

6. Pandas

In the world of data scientists, Pandas is one of the most valuable Python modules. The Series data structure is used for one-dimensional data, while the Data Frame data structure is used for two-dimensional data in pandas.

Among numerous domains, such as finance, statistics, social sciences, and a variety of engineering fields, these are the most often encountered data structures. When it comes to data processing and analysis, Pandas stands out for how simple and versatile it is to use.

Check website: Pandas

7. Jupyter

Jupyter is not a Python library in the traditional sense. However, since we are looking at which tools data scientists use the most, the list would be incomplete if Jupyter were not included. Jupyter is something I use on a regular basis to test ideas and construct small prototypes. When the code is more sophisticated or when we wish to make libraries of our work to be reused in other projects, I do not advocate using this method.

The interface is similar in functionality to an interactive Python terminal in a browser, with the added capability of running Python code and seeing data and images while also documenting your work.

Check website: Jupyter

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8. TensorFlow

TensorFlow is a Python library created by Google that allows you to conduct numerical computations using data flow diagrams. For some, this may come as a surprise, since we will be writing a graph rather than a program. Mathematical operations will be represented by the nodes of this graph, and tensors will be represented by the edges (multidimensional data matrices).

TensorFlow, which uses graph-based processing, can be used for the deep learning as well as other scientific computing applications, among other things.

Check website: TensorFlow

9. Bokeh

Bokeh is a library that allows you to interact with data visualization in a web browser, and it’s free. With bokeh, we can construct charts that are adaptable, attractive, and interactive.

Bokeh developers are aiming for high speed when dealing with massive volumes of data, even when the data is being fed in real time from several sources.

Check website: Bokeh

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10. SciPy

Travis Oliphant’s initial collection of Python extension modules evolved into the open source library Scipy, which contains mathematical tools and algorithms. Scipy is a collection of mathematical tools and algorithms.

Among the many activities and operations supported by Scipy are optimization, linear algebra, interpolation, special functions, fast Fourier transform (FFT), signal and image processing, solving ordinary differential equations, and a variety of other scientific and engineering-related activities and operations. Scilab competes with software such as MATLAB, GNU Octave, and Scilab, which are all aimed at the same types of users as Scilab.

Check website: SciPy

11. NumPy

NumPy offers a universal data format that allows for data analysis and data sharing across algorithms of a variety of various types. There are many other types of data structures that it can implement, including multidimensional vectors and arrays with huge data capacity.

In addition, this library contains high-level mathematical functions that may be used to work on the data structures included inside it.

Check website: NumPy

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12. Seaborn

Seaborn is a graphical package built on the matplotlib framework that is particularly well-suited for the display of statistical data. It has a high-level interface for making statistical visuals that are both aesthetically beautiful and useful.

Seaborn regards data visualization as a crucial component of the process of discovering and comprehending information. It works nicely in conjunction with the pandas data manipulation package.

Check website: Seaborn

13. Scikit-learn

Scikit-learn is a Python module that integrates a broad variety of state-of-the-art machine learning methods for medium-scale supervised and unsupervised tasks. Scikit-learn is available as a free download from the Python Software Foundation.

Acording to activestate, this package is dedicated to making machine learning accessible to non-specialists via the use of a general-purpose high-level language.

The simplicity of use, speed, documentation, and consistency of the API are all given top priority. It has a small number of dependencies and is offered under a simplified BSD license, which encourages its usage in both academic and commercial environments, respectively.

Check website: Scikit-learn

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14. NLTK

It is also known as the Natural Language Toolkit (NLTK), and it is a collection of libraries and applications for symbolic and statistical natural language processing that are designed to be used with the Python programming language.

Currently, it is one of the most popular and well-known Machine Learning libraries. In addition, according to RealPython, graphical examples and sample data, NLTK contains a documentation section.

Check website: NLTK

15. Theano

Theano is a Python machine learning toolkit that may be used as an optimizing compiler for evaluating and manipulating mathematical expressions and matrix operations. It is written in the Python programming language. Theano, which is based on NumPy, has a close integration with NumPy and a user interface that is quite similar to NumPy.

With logarithmic and exponential functions, Theano can automatically detect and eliminate mistakes and defects, saving you time and effort. Theano comes with built-in tools for unit testing and validation, which helps to prevent bugs and other issues.

Check website: Theano

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CONCLUSION:

Machine learning from the very day it has started, it has spread widely into a exciting and fascinating field with a variety of applications. ML is also an evolving area of study with an ever-expanding body of knowledge; if you are interested in ML, I have provided some of the links below to let you know where to get started.

HAPPY JOURNEY AHEAD😇.

LINKS FOR TUTORIALS TO GET STARTED:

1. Machine Learning on Udemy[PAID]
2. Complete Machine Learning on Udemy[PAID]
3. Machine Learning Crash Course[FREE]
4. AWS Machine Learning Skill Builder[FREE] 👈 FOR JOB PLACEMENTS

I hope you enjoyed this tutorial. If you have any questions or comments, feel free to drop them in

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Till then stay alive.

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15 Basic Python Libraries for Machine Learning You Need to Know. was originally published in FAUN Publication on Medium, where people are continuing the conversation by highlighting and responding to this story.