Artificial intelligence, machine learning, deep learning, and data science have reached a peak of popularity. The year 2020 AI and ML year. This will decide the future of machine learning in India, the US, and all over the world. Many universities are focusing on machine learning. The industry is ready for new technologies. Data processing power is available for reasonable prices.
Python is a widely-used programming language for machine learning. Other languages too offer good functionality to manage the machine learning job. But, Python is a simple, yet powerful tool ready with a tonne of libraries, modules to ease machine learning up.
Here are 5 reasons that make Python the most suitable tool for machine learning, data science, or deep learning. Python is one of the most developer-friendly programming languages. You can use existing modules or create your own to perform any specific task.
- Python is simple to learn and implement applications in.
- Python is free and opensource.
- There are a lot of useful libraries for machine learning, deep learning, and data science.
- Less-code, but more productivity
- Widely supported technology: All operating systems support Python so anyone can easily adopt or switch to Python and start using it.
There are so many Machine Learning applications in all areas. Machine Learning’s one of the most useful computer applications these days. Some people comment on the dangers of machine learning, but, the only possible danger I’ve ever seen is in Hollywood movies.
There are many positive uses of machine learning in real-life. The future will prove itself. Machine learning has applications in science, commerce, and even arts.
There are different aspects of developing a learning system, the technologies, the field of usage, and so on.
Applications of Machine Learning
- Image Recognition, Speech Recognition
- Machine Learning Applications in Healthcare
- Personal Assistants
- Traffic Predictions
- Spam Filtering
- Fraud Detection
- Recommendations: Hotel, movies, videos, friends, news, search results, products recommendations
- Stock Markets
- Self-driving cars
Python Machine Learning Libraries
Many businesses are already enjoying the benefits of early applying machine learning in everyday operations. From stock markets to agriculture machine learning has a lot of applications for everything. Here are the top 10 best libraries for machine learning using Python.
#1 Scipy-learn: Python machine learning library Scikit
It’s an opensource Python library for machine learning. It’s a simple yet efficient tool for predictive data analysis. It’s built on top of NumPy, SciPy, and matplotlib. Its applications are classification, regression, clustering, Dimensionality reduction, Comparing, validating, and choosing parameters and models and preprocessing.
Python library provides an easy to use interface for large multi-dimensional arrays and matrices for data manipulation. It provides a huge collection of functions for operating on the large sets of data arranged in these arrays.
NLTK: Natural Language Processing Library; Natural Language Toolkit. NLTK is a leading platform for building Python programs to work with human language data. NLTK includes tokenizing, part-of-speech tagging, stemming, sentiment analysis, and topic segmentation algorithms.
It’s one of the popular opensource Python libraries. Instead of building all tools from scratch, you can use a natural language toolkit. Classification of text data and labeling becomes the piece of a cake.
#4 Spark MLlib
Spark MLlib: Machine Learning Library: MLlib is Apache Spark’s scalable machine learning library, with APIs in Java, Scala, Python, and R. Its goal is to make practical machine learning scalable and easy. It provides tools such as Classification and regression, Basic Statistics, Data Types, and Feature extraction.
MLlib fits into Spark’s APIs and interoperates with NumPy in Python. It has High-quality algorithms, 100x faster than MapReduce. It runs everywhere, Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud, against diverse data sources.
Tensorflow: Opensource Machine Learning Library by Google. It helps in building the models easily. It’s an end-to-end platform that makes it easy for you to build and deploy Machine Learning models.
It provides multiple levels of abstractions to choose a required feature from. You can easily build and train models by using the high-level Keras API, which makes getting started with TensorFlow and machine learning easy.
Keras: Opensource Neural-Network Library. Keras: the Python deep learning API. Exceptionally, simple, flexible, and powerful API for machine learning.
According to the official documentation, Keras is an API for humans and not for machines. It provides simple, easy, and consistent APIs for ultimate users.
Keras with Tensorflow
tf.keras is TensorFlow’s high-level API for building and training deep learning models. It’s used for fast prototyping, state-of-the-art research, and production. It’s modular, user-friendly, comparable, and easy to extend the library.
Theano: Library for Mathematical/Scientific Calculations.
Theano is a Python library that allows defining, optimizing, and evaluating mathematical expressions involving multi-dimensional arrays. It’s developed by the Montreal Institute for Learning Algorithms (MILA), University of Montreal.
Like many other libraries in this post, Theano is built on top of Numpy. It has tightly integrated with Numpy. It’s used in building Deep Learning systems. Theano enjoys the fastest processing speeds.
Learn more about machine learning using Theano.
PyTorch: Machine Learning Library in Python by Facebook.
Opensource Python library for machine learning. An open source machine learning framework that accelerates the path from research prototyping to production deployment. It’s applications in computer vision and natural language processing.
There is a class in package torch.Tensor. PyTorch defines this class called Tensor to store and operate on homogeneous multidimensional rectangular arrays of numbers.
Learn more about machine learning using PyTorch.
Pandas: Data Manipulation and Analysis Library
The free and opensource Python library for data analysis. It’s a fast, powerful, flexible and easy to use open source data analysis and manipulation tool written in Python.
It also has tools for reading and writing data between in-memory data structures and different formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format. It is used everywhere, e.g. finance, neuroscience, economics, statistics, advertising, web analytics, etc.
Learn more about Pandas for Machine Learning here.
MXNet: Deep Learning Library. It’s used for training and deploying the neural networks. A flexible and efficient library for deep learning. MXNet is an open-source deep learning software framework, used to train, and deploy deep neural networks. It’s an open-source deep learning framework suited for flexible research prototyping and production.