Machine learning has become ubiquitous in our society and you can use it to implement almost any business solution, but it’s not always easy to find the right toolkit or library that will let you achieve your goals quickly and easily. In this blog, we’ll cover 7 Python libraries that will help your machine learning journey be much easier than before. So, without further ado, let's see each of them one by one...
Scikit-learn
Scikit-learn is an open-source machine learning library that can be used with Python to create a wide variety of tasks, including classification and regression. Since it's compatible with NumPy, sci-kit-learn is easy to implement and use. It can also serve as a framework for other machine learning projects. Since it was created by researchers at CNRS, sci-kit-learn focuses on algorithms that can handle various kinds of data sets—not just images or text but others like time series or structured data as well). Scikit-learn also comes with preprocessing functions for easier data preparation and has many built-in datasets for testing purposes. Overall, its speed makes it one of the most commonly used libraries for analytics and ML work.
Theano
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. The library is written entirely in C++. It can be used for running neural networks as well as general functions. In short, Theano provides a rich infrastructure for performing numerical computations with multi-dimensional arrays efficiently. What makes it stand out from other libraries is its high flexibility.
This library gives us more control over how our calculations are performed and how we specify them to make sure they are done exactly as we expect them to be done. Moreover, it has good documentation and an active community of users that add new features regularly through pull requests on GitHub or patches on BitBucket (which hosts all its development). Overall, if you’re interested in scientific computing or machine learning applications based on Python, then Theano will probably help make your life easier by letting you do what you want quickly without sacrificing the speed or accuracy of results.
TensorFlow
TensorFlow is an open-source software library for numerical computation using data flow graphs. Nodes in a TensorFlow graph represent mathematical operations, while edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for internal use. The system is general enough to be applicable in a wide variety of domains, including computer vision, natural language processing (NLP), speech recognition, and general artificial intelligence (AI).
Keras
Used to build and train neural network models, it’s got a really simple API that makes it easy to work with neural networks. It also runs on top of TensorFlow or Theano, giving you an incredible amount of flexibility as well as speed. Keras has been gaining popularity in recent years due to its intuitive interface, making it easier for beginners than other frameworks such as TensorFlow or Caffe. One great feature is that you can get results faster thanks to its seamless integration with computation graphs. It runs on top of either Theano or TensorFlow - two very popular machine learning libraries – so one more thing for you to learn if you’re getting started into deep learning!
Pandas
The pandas library is used for reading, storing, manipulating, and cleaning up data. It’s particularly useful for analyzing datasets that are too big to be loaded into memory at once. Pandas provide DataFrame objects, which hold many rows of tabular data and can perform aggregations across these values quite quickly. The library has built-in support for statistical analysis and visualization with very little additional setup required. Along with NumPy, pandas form a key part of most scientific computing workflows in Python. There’s a lot to say about pandas; it’s one of my favorite libraries!
Matplotlib
Matplotlib is a plotting library for python that produces publication-quality figures. It’s written to work with NumPy arrays, making it incredibly fast and easy to use. For beginners, matplotlib can be a useful tool as it provides one of the easiest ways to visualize data. However, once you start working with larger data sets and need more customization options and output control, you might want to look into some other libraries such as Seaborn, Bokeh, or Plotly.
Numpy
NumPy provides convenient and fast N-dimensional array manipulation. It is built on top of its scientific computing core, which is also called NumPy. NumPy makes use of its own optimized implementations of basic operations on real and complex numbers, such as arithmetic operations, vectorized math functions, Fourier transform, and random number generation. These high-performance tools allow it to speed up programs that require intensive manipulation of large arrays of data. If you’re interested in learning more about NumPy, check out Numpy's official documentation here.