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.

More from Machine learning

With hard work and determination, anyone can learn to code.

Here’s a list of my favorites resources if you’re learning to code in 2021.

👇

1. freeCodeCamp.

I’d suggest picking one of the projects in the curriculum to tackle and then completing the lessons on syntax when you get stuck. This way you know *why* you’re learning what you’re learning, and you're building things

2.
https://t.co/7XC50GlIaa is a hidden gem. Things I love about it:

1) You can see the most upvoted solutions so you can read really good code

2) You can ask questions in the discussion section if you're stuck, and people often answer. Free

3. https://t.co/V9gcXqqLN6 and https://t.co/KbEYGL21iE

On stackoverflow you can find answers to almost every problem you encounter. On GitHub you can read so much great code. You can build so much just from using these two resources and a blank text editor.

4. https://t.co/xX2J00fSrT @eggheadio specifically for frontend dev.

Their tutorials are designed to maximize your time, so you never feel overwhelmed by a 14-hour course. Also, the amount of prep they put into making great courses is unlike any other online course I've seen.

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#ज्योतिष_विज्ञान #मंत्र_विज्ञान

ज्योतिषाचार्य अक्सर ग्रहों के दुष्प्रभाव के समाधान के लिए मंत्र जप, अनुष्ठान इत्यादि बताते हैं।

व्यक्ति के जन्म के समय ग्रहों की स्थिति ही उसकी कुंडली बन जाती है जैसे कि फ़ोटो खींच लिया हो और एडिट करना सम्भव नही है। इसे ही "लग्न" कुंडली कहते हैं।


लग्न के समय ग्रहों की इस स्थिति से ही जीवन भर आपको किस ग्रह की ऊर्जा कैसे प्रभावित करेगी का निर्धारिण होता है। साथ साथ दशाएँ, गोचर इत्यादि चलते हैं पर लग्न कुंडली का रोल सबसे महत्वपूर्ण है।


पृथ्वी से अरबों खरबों दूर ये ग्रह अपनी ऊर्जा से पृथ्वी/व्यक्ति को प्रभावित करते हैं जैसे हमारे सबसे निकट ग्रह चंद्रमा जोकि जल का कारक है पृथ्वी और शरीर के जलतत्व पर पूर्ण प्रभाव रखता है।
पूर्णिमा में उछाल मारता समुद्र का जल इसकी ऊर्जा के प्रभाव को दिखाता है।


अमावस्या में ऊर्जा का स्तर कम होने पर वही समुद्र शांत होकर पीछे चला जाता है। जिसे ज्वार-भाटा कहते हैं। इसी तरह अन्य ग्रहों की ऊर्जा के प्रभाव होते हैं जिन्हें यहां समझाना संभव नहीं।
चंद्रमा की ये ऊर्जा शरीर को (अगर खराब है) water retention, बैचेनी, नींद न आना आदि लक्षण दिखाती है


मंत्र क्या हैं-
मंत्र इन ऊर्जाओं के सटीक प्रयोग करने के पासवर्ड हैं। जिनके जप से संबंधित ग्रह की ऊर्जा को जातक की ऊर्जा से कनेक्ट करके उन ग्रहों के दुष्प्रभाव को कम किया और शुभ प्रभाव को बढ़ाया जाता है।