10 PYTHON 🐍 libraries for machine learning.

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1. NumPy (Numerical Python)

- The most powerful feature of NumPy is the n-dimensional array.

- It contains basic linear algebra functions, Fourier transforms, and tools for integration with other low-level languages.

Ref: https://t.co/XY13ILXwSN
2. SciPy (Scientific Python)

- SciPy is built on NumPy.

- It is one of the most useful libraries for a variety of high-level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization, and Sparse matrices.

Ref: https://t.co/ALTFqM2VUo
3. Matplotlib

- Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

- You can also use Latex commands to add math to your plot.

- Matplotlib makes hard things possible.

Ref: https://t.co/zodOo2WzGx
4. Pandas

- Pandas is for structured data operations and manipulations.

- It is extensively used for data munging and preparation.

- Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage.

Ref: https://t.co/IFzikVHht4
5. Scikit Learn

- Built on NumPy, SciPy, and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction.

Ref: https://t.co/TCaQXPvKkk
6. Statsmodels

- Statsmodels for statistical modeling.

- Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests.

Ref: https://t.co/5CXswFvpPx
7. Seaborn

- Seaborn for statistical data visualization.

- Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib.

- Seaborn aims to make visualization a central part of exploring.

Ref: https://t.co/cSxJlr09mq
8. Blaze

- Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets.

- It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc.

Ref: https://t.co/5NhpM0reaH
9. Scrapy

- Scrapy for web crawling.

- It is a very useful framework for getting specific patterns of data.

- It has the capability to start at a website home URL and then dig through web-pages within the website to gather information.

Ref: https://t.co/iEYIazAd2B
10. SymPy

- SymPy for symbolic computation.

- It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics, and quantum physics.

- Use for formatting the result of the computations as LaTeX code.

Ref : https://t.co/hesVmRJLVj
Additional libraries, you might need:

- OS for Operating system and file operations.

- Networkx for graph-based data manipulations.

- Regular expressions for finding patterns in text data.

- BeautifulSoup for scrapping the web.

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4. रौद्रमा नवा भीतिर्मा: His anger will cause new fears in his foes.

5.विजिटरीपुरु धीरधारा, कलोथरा शिखरा कठोरा: This is a complex expression seen only in Indic language poetry. The poet is stating that Shivudu is experiencing the intensity of climbing a tough peak, and likening

it to the feeling in a hard battle, when you see your enemy defeated, and blood flowing like a rivulet. This is classical Veera rasa.

6.कुलकु थारथिलीथा गम्भीरा, जाया विराट वीरा: His rough body itself is like a sharp weapon (because he is determined to win). Hail this complete

hero of the world.

7.विलयगागनथाला भिकारा, गरज्जद्धरा गारा: The hero is destructive in the air/sky as well (because he can leap at an enemy from a great height). He can defeat the enemy (simply) with his fearsome roar of war.