Machine Learning || 10 best python libraries for Machine learning

What is Machine learning?

Machine learning is an artificial intelligence (AI) technique where a computer system learns without being explicitly programmed. In simple terms, machine learning is how we teach our computers to learn on their own.
 

How does Machine Learning work?

There are two ways that machines can learn; they can either use supervised methods or unsupervised methods. Supervised methods require input data that already have labels. Unsupervised methods do not need any labels at all. To make a network work, it must receive many examples to understand what each label means and how they relate to one another. A neural network is created so a computer can create its own rules and relationships based on these examples.
 

When to Use Machine Learning?

Machine learning can be applied to anything. You don't always know what problem you're trying to solve before you start! Examples could be forecasting air traffic patterns, predicting stock prices, classifying images, identifying spam emails, understanding human speech, translating languages, or even playing chess.
 

Implementing Machine Learning Algorithms:

You can implement algorithms using software libraries like Scikit-Learn, TensorFlow, and PyTorch. If you are working with Python, Pandas and NumPy help parse data and perform computations. Other libraries exist for Java, C++, R, MATLAB, Julia, Go, and others.
 

Where to Learn More About ML?

The National Science Foundation hosts a website called NSF Digital Library that contains scientific articles about topics related to AI. YouTube has great videos on machine learning. There's also an online course on Coursera called Machine Learning Crash Course. Finally, Khan Academy offers free tutorials.
 

Python Libraries used in machine learning:

1. scikit-learn:

scikit-learn is a powerful library for machine learning. You can use it to perform many different statistical tasks such as classification, regression, clustering, dimensionality reduction, feature selection and anomaly detection. scikit-Learn makes it really easy to get started with ML thanks to its simple API. There's no need to worry about how to set up a model or getting training data - just load some examples, iterate over them and train!
 

2. pytorch:

PyTorch is a high performance deep learning framework, based on Graphical Processing Units (GPU). PyTorch provides interfaces between neural networks and TensorFlow/Theano/CNTK. It includes builtin models such as LSTM, CNN and RNN, and the ability to build your own models using modules such as Sequential, Functional, Autoencoder and Residual Networks.
 

3. pylearn2:

Pylearn2 is a Python package for doing machine learning research with Neural Networks, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM) Networks. Pylearn2 is designed to make Deep Learning accessible to researchers who have little experience programming, not only providing pre-built algorithms but also teaching people how to implement their own.
 

4. Keras:

Keras is a high level neural network API implemented in Python. It is inspired by Torch and Theano and shares their core philosophy of simplicity and flexibility. Keras enables developers to easily define architectures and combine layers in order to build complex AI models.
 

5. scikit-image:

scikit-image is a Python module that contains image processing routines. Many open source packages depend on this module, including OpenCV. If you want to understand images in computer vision, you should learn what they look like and how to find out if a picture was taken at a certain place and under certain conditions. An example would be identifying whether an image shows a flower or grass, or even checking if it was taken indoors or outdoors. In addition, you can try to detect faces, text, edges, corners, etc.
 

6. tensorflow:

Tensorflow is an open-source software library for numerical computation using data flow graphs. Tensors represent mathematical objects such as numbers, matrices, vectors, and higher dimensional arrays; operations describe how these tensors are combined. Operations can operate on multiple inputs simultaneously, producing output tensors. These input and output tensors may then serve as input to further operations. Tensors are often represented graphically via diagrams called graphs. Nodes represent individual elements of a tensor and links represent arithmetic operations performed on those elements. These tensors are used for supervised and unsupervised machine learning tasks.
 

7. keras-gcn:

keras-gcn is a lightweight and fast implementation of Google’s CaffeNet and Alexnet architectures for object recognition. It works well for both image and video applications. It consists of two parts Keras backend and gcn frontend. Keras backend uses a layer normalization technique and supports multi GPU configurations. gcn frontend wraps the standard caffe net converter.
 

8. PyBrain:

PyBrain is a Python library for artificial neural networks and connectionist systems. It offers many features, including connectionism, backpropagation, reinforcement learning, genetic programming, evolutionary algorithms, and more. It has been developed since 2003 by the Max Planck Institute for Biological Cybernetics in Spandau, Germany.  
 

9. numpy:

Numpy is a library that provides vectorized operations over arrays. It includes linear algebra, integration, optimization, Fourier transforms, random number generation, data structures, special functions, and much more. Numpy supports both 1-D and 2-D arrays, contains its own version of Python lists, and is supported by Python versions 2.4 - 3.8.
 

10. scipy:

 Scipy is a scientific computing framework for python. It includes numerical methods (e.g., linear algebra, curve fitting), statistics tools (e.g., statistical tests, distributions), signal processing, optimisation, and visualization. Scipy is supported by Python versions 3.0 - 3.9.
 

11. pandas:

Pandas is a python package designed for manipulating tabular data. It includes extensive collection of data analysis and manipulation routines. Pandas also provides a high performance implementation of core NumPy functionality using native C extensions. Pandas is supported by Python versions 4.0 - 5.3.
 

12. statsmodels:

Statsmodels is a free open source library for performing statistical analyses. It consists of two parts: Statistics and Modeling. Statistics handles things like regression, density estimation, hypothesis testing, and time series. Modeling includes models such as linear regressions, generalized linear models, nonlinear regressions, and survival analysis.