Machine learning is what you get when you want the program to learn its tasks on its own. Learn to evolve without specific directions from its creator after the program has been initiated. The program begins with observations or training data where they look for patterns and takes decisions about the results based on that. This process continues with the growing amount of data it gets from these processes of testing and trials. The program tries to improve its results over every testing cycle. That’s machine learning. And what’s a better programming language for that than Python?
Python is a preferred language for machine learning because it supports the developer through the process. It’s simpler to learn, has high data holding capacity and can interact with almost all third party languages and platforms. Another bonus that comes with Python is its frameworks. These frameworks act as foundations for software development. It’s a tool to help the programmers to create software without having to start from scratches.
Now, seeing how complex a concept Machine learning and artificial intelligence is to pull off, its only right that they get the best type of platform to be worked on. Due to it allowing almost all third party elements, it has many open source libraries. This makes a complicated discipline like Machine learning a little bit more manageable. So here are some of the best ones that help in the development cycle of Machine Learning while using Python.
TensorFlow
TensorFlow is an open-source library for numerical computation and large scale machine learning. It uses data flow graphs to carry out these processes.in its process the data batches, known as tenors, are processed by a series of algorithms that are defined by graphs. These data flows through the system represented by a graph or a series of nodes that are being processed. The mathematics of the thing is not written in however as that part is written in C++ binaries. However, that library made in C++ can be used in Python due to the flexibility of the Python system.
TensorFlow’s biggest asset to the development process is its abstraction. The developer are able to focus on the overall logic of the application instead of the minor details. All those things like algorithm implementation and hitching of output function from one to input of another and such things are taken care by Tensor behind the scene.
Keras
Keras is a popular framework when dealing with deep learning. It’s a framework designed to help developers work with MI technology more conveniently. As in it allows them to perform various experimental project enabling them to run their ideas more efficiently. It is, after all, created to be user friendly. Kersa is a simple, flexible and powerful API designed for humans, not machines.
PyTorch
PyTorch is a scientific computing framework preferred for deep learning. Its main attribute is its speed and the extent of its flexibility. It also provides two high-level features, the tensor computation along with a strong GPU acceleration support and it also has a deep neutral network building ability on a tape-based autograd system.
PyTorch uses multiple backends for different processing units and different functional features. It allows for easy deployment in constrained systems. There’s always the best to use features and its extensibility with shared backend with C++.
Scikit-learn
Scikit-learn is used for bringing the machine learning element to the production system. It provides both supervised and unsupervised learning algorithms through a consistent Python interface. It builds on the features of pre-existing packages the NumPy, SciPy and matplotlib.
This framework is more focused on data modelling. So it deals with features like Classification which categories where objects belong. Then there’s Regression which continuously predicts continuous-valued attributes associated with the said objects. Then there is other stuff like Clustering, dimension reduction and a few others.
Pandas
Pandas is a data analysis library used for data manipulation and analysis.
The framework comes into play before the data training process. It acts as a home base for data. It cleans, transforms and analyses data. It gives an understanding of the dataset before you do other complex visualization of it.
Panda, however, requires a high-level knowledge, at least that of a software engineer to be able to be understood in a general context. It enables the user to work with complex data operations with fewer commands. It comes with several built-in methods to perform data manipulation operations.
Theano
Theano is a framework made for numerical computations. Fast numerical computations. One of the critical foundational libraries for deep learning, it’s a compiler for mathematical expressions for Python. As the central concept behind several Machine learning and AI applications is the tricks and methods used to manipulate the computation of mathematical expression, a framework that specializes in the field is a significant asset in the programming platform.
Theano defines, optimizes and evaluates mathematical expressions. It can deal with computation with an extensive neural system.
NumPy
NumPy, i.e. Numerical Python is a framework that’s a linear algebra developed in Python. A lot of other frameworks involved in machine learning rely on this library to some extent. It basically deals with complex algebraic functions as one would guess from the former statements. The package is used for scientific computation used in handling sound waves, images and similar binary functions.
NumPy contains a multi-dimensional array and matrix data structure. This allows for a wide variety of use in mathematical operations. It guarantees an efficient calculation with said data types and gives a supply of a large amount of high-levelled mathematical functions that operate on arrays and matrixes.
MXNet
MXNet is another framework widely fit in the world of deep learning. It is used as a starting ground for deep learning, i.e. used by a novice in the field. It supports a multiple of GPUs and has a fast problem-solving ability which is a vital criterion for beginners in the field of deep learning.
MXNet is very scalable and is portable from one platform to another. These two features combined make for a flexible application.
Seaborn
Seaborn is a framework for statistic graphics. It helps the programmer to learn and explore the data by plotting the data in an understandable visual setting which allows in the understanding department.
Seaborn is dataset oriented and examines the relationship between the multiple variables it contains. It contains specialized support for using categorical variables for observations and aggregate statistics. Its primary focus is the visualization of the data.
Spark MLlib
Spark MLlib is a framework focused on scaling the computation in machine learning. The tools it offers are designed to make the practical machine learning process more scalable and easy to use. It is fast and a general engine used for large scale data processing. That’s literally how its developers described it.
It runs on memory, i.e. RAM making it faster than previous approaches to the work of big data. The use of RAM makes it run more quickly on the disc. Then there’s the next feature that allows it to be run in multiple things including running SQL, data pipelining, working graphs and other such functions.