Python is a simple, open-source, general-purpose programming language developed in 1991. It is highly popular with developers due to its irresistible features and extensive libraries to deal with a wide array of data. The Python language has been growing for the past two decades and has turned into one of the essential skills to work in the IT space. Individuals working in any data-related field must learn Python as it opens the door to ample high-paying job opportunities. In fact, many training providers now offer a Python Data Science course to help professionals learn this powerful language.
One of the greatest strengths of the python language is its rich libraries. It provides highly popular modules such as Pandas, Numpy for data processing and data analysis. Scipy module can be used for various scientific calculations such as differentiation, integration, and so on. All three modules can be used for all essential operations to transform the data to arrive at a logical conclusion. Matplotlib and Seaborn can be used to create trendy colorful visualizations to communicate findings to stakeholders. Python has rich modules to process images which is handy for computer vision. Some of the popular uses of Python modules in Artificial Intelligence include scikit-learn, Keras, and TensorFlow.
The first and foremost task in data analysis after collecting data is the cleaning and preparing the data for further assessment. More commonly termed as data munging. It consumes more than 50% of data experts’ time to prepare the data. To master data munging, one needs to use good tools such as Pandas, which provides the handiest classes for data cleaning. Pandas stand for Python Data Analysis Library, and it is an open-source Python package. It provides highly efficient, user-friendly data structures and analysis classes primarily for the labeled data. Pandas allow one to load, prepare, and manipulate all types of structured data. Pandas define data as Series and DataFrames, which help one visualize data efficiently and manipulate it as per requirement. DataFrame is a Python object with rows and columns and is very similar to an excel table. Pandas are often used with other data science and machine learning libraries which makes it very critical to learn. One willing to work on machine learning and deep learning is highly recommended to master the Pandas libraries. Some cool things you can achieve with Pandas are
- Indexing, sorting, merging, manipulating data frames.
- Handle missing, erroneous, or inconsistent data
- Apply statistical tool for better understanding of data
- Visualize data
Numpy is another open-source, highly popular Python library that stands for Numerical Python. It is a powerful Python library and is widely used in the field of science and engineering. Numpy has, over the years, become the gold standard for working with numerical or scientific data in Python. NumPy caters to various users, from beginners to experienced researchers working on scientific and industrial research. NumPy is primarily used for multi-dimensional array and matrix data structures. Multi-dimensional arrays defined in Numpy are quite robust compared to lists. NumPy is extensively used in conjunction with other modules such as TensorFlow, Pandas, SciPy, Matplotlib, scikit-learn, etc. Some excellent use of Numpy
- Numpy can represent images, sound waves, and other binary streams as an array of real numbers.
- Perform statistics or mathematics routines
It is an open-source Python library used for working on scientific and engineering problems. SciPy can be thought of as an extension to the Numpy library as it uses the Numpy array as the basic data structure for solving mathematical problems. SciPy library contains all the linear algebra functions required in data science. It enables developers to process and visualize the data with a wide variety of handy commands. Apart from linear algebra, It consists of classes for optimization, integration, and statistics. Help on each function definition is readily available over the internet due to effective and comprehensive documentation. Some notable applications of SciPy are:
- Image or signal processing
- Fourier transforms,
- Integration and differentiation
- Optimization algorithms
Scikit-learn, also known as Sklearn, is an extremely rich and handy Python module used for machine learning. This module emphasizes modeling data rather than data processing, such as loading, manipulating, and summarizing data. It is recommended that once the data is cleaned and processed with modules such as Pandas or NumPy, scikit-learn is used to develop machine learning models. This module is primarily scripted in Python and built upon NumPy, SciPy, and Matplotlib. This module provides various machine learning and statistical tools that can be used, namely classification, regression, clustering, cross-validation, logistics regression, etc. Some highlights are
- Cross-validation – provides methods to assess the performance of supervised models on unseen data.
- Dimensionality Reduction – To reduce the number of attributes
- Feature extraction – Primarily used for defining attributes in images and text
Deep learning, a subset of machine learning, started to gain popularity with handling massive amounts of data. Neural networks work as the backbone of deep learning algorithms. As usual, Google realized the potential of deep neural networks for improving its services and started to develop the library in collaboration with the Brain Team. This library was named TensorFlow and is now part of every Google application such as search engine, translation, recommendation, etc. Some are working on machine learning principles.
TensorFlow is a flexible, open-source platform primarily developed to create applications working on machine learning concepts. TensorFlow allows users to construct a data flow of operations required to perform on the multidimensional array input (known as Tensor) entering at one end to achieve the output at the other end. This framework allows researchers to work in the tendon on various AI models. TensorFlow allows users to perform a variety of operations parallelly, training multiple neural networks for efficient and scalable models.
Keras is primarily handy for developing deep learning models. It enables developers to create neural networks easily. Keras is built upon and uses TensorFlow and Theano internally. Some of the best utilities for compiling models, processing data-sets, visualization of graphs, and much more. Keras models are relatively slow in comparison to other machine learning libraries due creation of a computational graph at the backend. The good thing about Keras is that all the models in Keras are portable.