Remember those days when people had to code full algorithms for Machine Learning?

Gone are those days!

Thanks to Python and it’s libraries, modules, and frameworks - it has become the most preferred language for much easier & convenient Machine Learning algorithm implementations.

But, why Python?

Well, it’s a no brainer. Python helps developers to be more productive from development to deployment and maintenance. Python syntax is very simple and high level when compared to Java, C, and C++, therefore applications can be built with fewer lines of code. In fact, Python is popularly known as a beginner's language because of its simplicity!

But one of the biggest reasons why Python is considered as the most preferred language for Machine Learning is Python’s extensive set of libraries!

Python’s Prebuilt Libraries 

Python has hundreds of pre-built libraries to implement various Machine Learning and Deep Learning algorithms. So every time you want to run an algorithm on a data set, all you have to do is install and load the necessary packages, sometimes, just with a single command!

Obviously, the simplicity of Python has attracted many developers to build libraries for Machine learning and Data Science.
Let’s have a look at the examples of these amazing pre-built libraries like Scikit Learn, TensorFlow, ELI5, Pytorch without any further ado.

1. PyTorch (from the stables of Facebook!)

Pytorch is an open-source, Python-based scientific computing package that is used to implement Deep Learning techniques and Neural Networks on large datasets. This library has been built by Facebook, they mainly use this for developing neural networks that help in various tasks such as face recognition and auto-tagging.
What does it offer?
It offers a variety of libraries and tools that support Computer Vision, Natural Language Processing (NLP), and many more Machine Learning applications.
It is also widely used in Deep Learning research and is very customizable. 

Key features of PyTorch?
PyTorch provides easy to use APIs to integrate with other data science and Machine Learning frameworks. Like NumPy, PyTorch provides multi-dimensional arrays called Tensors, that unlike NumPy, can even be used on a GPU. Not only can it be used to model large-scale neural networks it also provides an interface, with more than 200+ mathematical operations for statistical analysis.

Plus, PyTorch can create Dynamic Computation Graphs that build-up dynamic graphs at every point of code execution. These graphs help in time-series analysis while forecasting data points such as Sales in real-time.
Want to learn PyTorch?
Here you go: https://pytorch.org/tutorials/

2. Scikit Learn

One of the most useful Python libraries, Scikit-learn is the best library for data modeling and model evaluation. It comes with tons and tons of functions for the sole purpose of creating a Model. 
What does it make so special? 
It contains all the Supervised and Unsupervised Machine Learning algorithms and it also comes with well-defined functions for Ensemble Learning and Boosting Machine Learning. It’s used for data-mining and data analysis, which makes it a great tool for starting out with Machine Learning.

It comes with the support of various algorithms such as Classification, Regression, Clustering, Dimensionality Reduction, Model Selection & Preprocessing
Key features of Scikit?
SciKit provides a set of standard datasets to help you get started with Machine Learning. For example, the famous Iris dataset and the Boston House Prices dataset are a part of the Scikit-learn library.

SciKit has in-built methods to carry out both Supervised and Unsupervised Machine Learning, including solving, clustering, classification, regression, and anomaly detection problems. SciKit also comes with in-built functions for feature extraction and feature selection which help in identifying the significant attributes in the data. It provides methods to perform cross-validation for estimating the performance of the model and also comes with functions for parameter tuning in order to improve the model performance.
Here you go: http://scikit-learn.org/stable/tutorial/index.html

3. TensorFlow (The revolution is here!)

Offered by Google - Tensorflow is one of the top libraries today for working with Machine Learning on Python. It’s a scalable open-source machine learning library that is really fast and flexible. It is a symbolic math library that is used for building strong and precise neural networks.

Here’s a visual working example of Tensorflow:
What makes it one of the best?
Machine Learning models can be created and trained using this library - not only on computers but also on mobile devices and servers by using TensorFlow Lite and TensorFlow Serving.

It has an intuitive multiplatform programming interface that is highly-scaleable over a vast domain of fields.
TensorFlow shines in these Machine Learning & Deep Learning algos
Handling deep neural networks, Natural Language Processing and Partial Differential Equation is where TensorFlow excels. Also does really good in Abstraction capabilities, Image, Text, and Speech recognition and being able to effortlessly collaborate with ideas and code.
Want to get your hands on TensorFlow?
Here you go: https://www.tensorflow.org/tutorials

4. ELI5

ELI5 is another visualization library that is useful for debugging machine learning models and explaining the predictions that have been produced. It works with all the most common Python Machine Learning libraries including Scikit-learn, XGBoost, and Keras.
What does it offer? 
Improving the performance of Machine Learning models is the main focus of the ELI5 library. ELI5 is a Python package which helps to debug machine learning classifiers and explain their predictions. It provides support for the machine learning frameworks and packages like XGBoost, CATBoost, LightGBM, Keras.
What are some of its key features?
ELI5 provides integration with the Scikit-learn package to express feature importances and explain predictions of decision trees and tree-based ensembles. It’s able to analyze and explain the predictions made by XGBClassifier, XGBRegressor, LGBMClassifier, LGBMRegressor, CatBoostClassifier, CatBoostRegressor, and CatBoost. It provides support for implementing several algorithms in order to inspect black-box models which include the TextExplainer module that allows you to explain predictions made by text classifiers. It helps in analyzing weights and predictions of the scikit-learn General Linear Models (GLM) which include the linear regressors and classifiers.
Want to get your hands on ELI5?
Here you go: https://eli5.readthedocs.io/en/0.2/index.html

5. Spark MLlib (Apache Spark’s scalable Machine Learning library)

Spark MLlib is a module on top of Spark Core that provides Machine Learning primitives as APIs. The base computing framework from Spark is a huge benefit. On top of this, MLlib provides most of the popular Machine Learning and statistical algorithms.
This greatly simplifies the task of working on a large-scale Machine Learning project.
What tools does Spark MLlib offer?
Where does SparkLib show it’s magic?
SparkLib is often preferred for Regressions, Clustering, Optimizations and Dimensional Reduction besides Classification & Feature Extraction . 
Want to get your hands on Spark Mlib?
Here you go: https://spark.apache.org/mllib
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