Machines are getting smarter day by day. They can automatically find repeated patterns with basic data observations, and make informed decisions without any human interference.
Machine learning’s exponential growth is largely driven by various open-source tools which make it much easier for Python developers to familiarize themselves with this language and adapt accordingly. For a long while now, Python has become a charmer to data scientists.
In the early years, people used to execute Machine Learning activities by coding all the algorithms and mathematical and statistical method manually. This made the process slow, frustrating and time consuming. But in the modern days, different python libraries, frameworks, and modules have made it very simple and efficient compared to the older days. Today, Python is one of the most successful programming languages for this role and it has surpassed much of the industry’s languages, one explanation is its extensive collection of libraries.
Python owns a wide collection of data types and data structures. But nevertheless, it wasn’t designed for Machine Learning per say. Numpy is a library that handles data, particularly one that helps us to manage large multidimensional arrays along with a huge collection of mathematical operations.
Numpy is not only a library known for its multidimensional data processing capabilities. It is also recognized for its execution speed and ability to vectorise. It offers the functionality of MATLAB style and thus needs some preparation before you can get confident. It is also a core dependence for other commonly used libraries, such as pandas, matplotlib, etc.
TensorFlow is an end-to-end python machine learning library to run numerical high-end computations. TensorFlow can accommodate deep image recognition neural networks, handwritten digit identification, recurrent neural networks, NLP (Natural Language Processing), term embedding and PDE (Partial Differential Equation).
TensorFlow Python offers excellent architecture support to allow fast computation deployments over a wide range of platforms, such as desktops, servers and mobile devices.
Abstraction is TensorFlow Python’s main appeal towards machine learning and AI projects. This feature allows developers to focus on the application’s comprehensive rationale rather than dealing with the tedious details of implementation algorithms. With such a library, python developers can now leverage AI and ML efficiently to create unique, responsive applications that respond to user inputs including facial or voice speech.
Theano is another fantastic computational framework for computing multidimensional arrays that comes in handy. Theano integrates closely with Numpy, which can handle data-intensive computations relative to a typical CPU.
While the library has similarities with Tensorflow, in terms of fitting into production environments, leaves much to be desired.
Theano is a popular python library used to efficiently describe, evaluate and optimize mathematical expressions concerning multi-dimensional arrays. It is done by optimizing CPU and GPU utilization. It is widely used to identify and detect different types of errors for unit-testing and self-verification. Theano is a very multifunctional library that has long been used in large-scale computationally intensive scientific projects but is easy and open enough for people to use it for their own projects.
Keras is a leading open-source Python library written to build neural networks and projects of machine learning. It can run on Deeplearning4j, MXNet, Microsoft Cognitive Toolkit (CNTK), TensorFlow or Theano. It provides nearly all standalone modules including optimizers, neural layers, functions for activation, schemes for initialization, cost functions, and regularization schemes. It makes adding new modules quick much like adding new functions and classes. Seeing that the model is already specified in the code, you do not need to provide separate config files for the model.
Keras makes designing and developing a neural network easy for beginners in machine learning. Keras Python also addresses convolution neural networks. It requires normalization algorithms, optimizer layers, and activation layers. Rather than being an end-to-end Python machine learning library, Keras works as a user-friendly, extensible interface that improves modularity and total expressiveness.
Pandas is an open-source Python package offering high-performance, easy-to-use data models and data analysis tools for the Python programming for the labeled data. Pandas stands for Python Data Analysis Library.
Pandas is a handy tool for munging or wrangling data. This is programmed to manipulate, read, compile, and visualize data quickly and efficiently.
Pandas take data into a CSV or TSV file or SQL database and create a Python object called a data frame with rows and columns. The data framework, say Excel or SPSS, is very similar to a table in statistical software.
Developed on top of NumPy, the SciPy library is a set of subpackages that help to solve the simplest statistical analysis-related problems. The SciPy library is used to process the array elements defined using the NumPy library, thus it is often used to compute mathematical equations that cannot be achieved using NumPy.
Scipy works alongside NumPy arrays to offer a framework that delivers numerous mathematical approaches such as numerical integration and optimization. It has a sub-package collection which can be used for vector quantization, Fourier transformation, integration, interpolation, etc.
Scipy presents a complete stack of Linear Algebra functions used for more complex computations such as clustering using the k-means algorithm, and so on. Moreover, it supports signal processing, data structures and numerical algorithms, creating sparse matrices, etc.