Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence (AI) have emerged as leading specializations in the fast-developing data science industry. More and more companies are adopting these domains to advance their business prospects, which has led to a rapid increase in demand for software developers, engineers and programmers working in such specializations.
ML is a complex field of data science, involving algorithm and math-intensive areas. It involves extensive use of programming languages and it is also based on complex statistical algorithms. In the early days, developers would have to painstakingly code lengthy algorithms for ML. But now, programming languages have evolved to include comprehensive libraries that have eased many machine learning processes.
While each programming language offers a unique stronghold on certain aspects of ML, perhaps the most popular language in this regard is Python. Python wins unparalleled as the go-to choice for ML because of the availability of extensive libraries, modules and open-source tools. Not only that, but Python also offers some of the best compliance features among all the programming languages used for ML that help developers write time-efficient, quality codes comfortably.
So, what Python libraries can you learn for ML, DL and AI? Read on to find out.
Why Python For Machine Learning and AI?
Before discussing Python libraries and Python modules for ML and AI, it is essential to understand why Python works best for the job. There are several features of Python that make it the obvious choice for ML.
- Python is free and open-source, which makes it developer-friendly.
- It is stocked with an incredible set of exhaustive libraries which aid complex coding processes.
- Python boosts productivity by reducing coding time and fastening debug processes.
- It has a wide range of applications, including ML, soft computing and natural language processing.
- It also works with C and C++ code modules.
Python is one of the easiest programming languages to use, with research suggesting that 48.24% of developers worldwide use Python in 2021. As Python’s features continue to grow, so do its use cases.
Best Python Libraries For ML
The use of Python for ML is a rapidly growing practice adopted by the industry’s top leaders. Python ML libraries and Python modules have become essential to master this discipline and utilize it for the benefit of an enterprise. Here are some of the top Python libraries recommended for every data scientist and developer looking to venture into ML.
The NumPy Python library is used by developers when operating complex mathematical functions on extensive multi-dimensional data. NumPy optimizes speed and productivity by delivering fast computation. This makes NumPy one of the most popular libraries for mathematical and statistical operations. In addition, NumPy has integration features with C, C++ and Fortran codes, making it highly flexible. Thus, it is a trendy choice in the data science community.
Since it is a free Python library with support from an open community of developers, the popularity of SciPy comes as no surprise. Its library offers Python modules for advanced functionalities, such as Fast Fourier transform, image optimization and ODE solving. This library is closely related to NumPy in that it depends on the latter for array manipulation subroutines.
David Cournapeau initially developed the Scikit-learn library in 2007 while participating in the Google Summer of Code project. Built upon NumPy and SciPy, Scikit-learn comes with an extensive range of learning algorithms that can be used for data-mining and data analysis. For ML tasks, Scikit-learn is useful for processes like classification, regression, clustering and model selection.
Offered by Google, TensorFlow is one of the best Python libraries and computational frameworks to use for ML. It makes the creation of ML models easy — not just for beginners but also for professionals. Plus, its flexible architecture allows developers to create ML models on various computational platforms, including CPUs, TPUs and GPUs. In ML, TensorFlow finds its application in handling deep neural networks, natural language processing, abstraction capabilities, and image, text and speech recognition.
Keras is a popular neural network library supported by TensorFlow’s core library. Keras features several foundational tools and elements essential to building a neural network. Some of these are neural layers, activation and cost functions, dropout, pooling, etc. Keras is also backed by a helpful developer community and a dedicated Slack channel, which makes it easy to get relevant and timely support.
PyTorch is a comprehensive, open-source library that supports computer vision, ML and natural language processing. One of the best features of PyTorch is its easy integration with the Python ecosystem, including libraries like NumPy. In addition, its robust framework works with Tensor computing, facilitates the construction of computational graphs on the go, and supports neural networks.
Pandas is a Python data analysis library that is primarily used for data analysis and manipulation. Pandas is one of the best libraries for ML programmers working with time series and structured multi-dimensional data. It is fast becoming the most popular Python library for solving practical, real-world data analysis in most sectors, including engineering, finance and statistics.
NLTK, short for Natural Language Toolkit, is a Python library dealing with natural language processing. Its popularity is due to its simple interfaces and the array of language resources like FrameNet, WordNet and Word2Vec. NLTK simplifies processes like keyword search, classification of texts and voice and handwriting recognition. This makes it a reliable choice among students and industry professionals alike.
Theano is a powerful and robust library that enables the rapid development of some of the most comprehensive ML algorithms. Theano serves as the building block for many popular DL libraries such as Blocks and Lasagne. Theano works with matrix calculations and mathematical expressions. Plus, its built-in unit-testing and validation tools automatically avoid bugs in the process.
Primarily a data-visualization library, Matplotlib is used for 2D plotting to generate high-quality image plots and figures in varied formats. Matplotlib can create histograms, plots, bar charts, etc., with simplified coding. Its user-friendly nature makes it a top choice for programmers looking to embed images into their applications.
The Way Forward
As a Python developer venturing into ML, in-depth knowledge of Python libraries is essential to excel in this complicated domain. Python libraries and Python modules expand the programming language’s capabilities to cater to varied niches in data science and knowing their application certainly makes a programmer’s life easy.
With the boom of ML, DL, and AI-based functioning in big and small companies, ML engineers have ranked among the top demanded professionals across the globe. Moreover, according to salary.com, ML engineers receive an annual package of $121,446 on average. Hence, there is no better time to upskill in ML and grab the multiple opportunities available in the domain.
upGrad offers a Master of Science in Machine Learning and Artificial Intelligence program in conjunction with two of the world’s top-ranking universities — Liverpool John Moores University and IIT Madras. This program, designed for working professionals, grooms students to be industry-ready with practical hands-on workshops and high-performing coaching. The student is imparted with a comprehensive education in the top skills for ML like Python, supervised and unsupervised ML models, and predictive analytics, among others. With features such as career mentorship sessions and personalized industry sessions, upGrad’s program is sure to strengthen your aptitude to secure the best ML jobs in the market.
The ML sector is currently expanding with opportunities galore in the field. This is one of the best career paths for developers and software engineers in the market right now, ensuring high remuneration and long-term stability.
Learn Machine Learning online from the World’s top Universities – Masters, Executive Post Graduate Programs, and Advanced Certificate Program in ML & AI to fast-track your career.
Is ML difficult to learn?
A common misconception is that ML, with all its complexities, is hard to learn. However, the truth is ML is a beginner-friendly field. It may be complex, but with a sound foundation in Python, reputed as the easiest programming language, and an understanding of high school mathematics, ML can be mastered smoothly.
What should be the first step to start a career in ML?
The first step to starting ML is to learn an ML programming language like Python, R, Java, calculus and linear algebra. The next step would be to enroll in a comprehensive ML course and practice, practice, and practice.
What are the eligibility criteria for upGrad’s M.Sc. program?
To enroll in this upGrad program, a candidate must have a Bachelor’s degree with 50% or equivalent graduating marks and two years of prior coding experience. If a candidate has less than two years of experience, they must possess a B.Tech or B.E. qualification with 60% or higher or first-class passing marks.