9 Python Libraries for Data Science and Artificial Intelligence

December 30, 2020

Python has been a game-changer for software developers and data specialists for a while now. The more you dive into automation, intelligence insights, and streamlining process tasks, proficient knowledge and practice of Python language emerge as a great skill to have. As per Python developer survey 2018, it was found that about 84% of developers prefer Python as their main language. 

If you are a developer or data scientist, you might be aware of open-source Python libraries that can be used in data science to make your Python data tasks easier. 

The list of Python libraries or packages is quite big. The usage of these libraries is spread across several domains. But in this article, I have curated a list of the 9 most popular Python libraries that are useful for data science and machine learning tasks.

1. Pandas

Pandas is an open-source Python library created for fast, flexible, high-performance, easy-to-use, and expressive data structures. It was designed to help data scientists work easy and intuitive for both labeled and relational data. It is highly stable and uses a series (one-dimensional like list)and data frames (two-dimensional like a table with multiple columns) data structure. It is a must-have library for quick and easy data manipulation, data wrangling or munging, and data visualization.

2. NumPy

NumPy, as the name suggests, is an ideal library to process basic and advanced multi-dimensional array operations, generating random numbers, and handling linear algebra. It empowers TensorFlow and other machine learning platform operations internally. It is a general-purpose array processing library that is distributed under a BSD license. It makes array operations easy by processing arrays with the same data type values. Proficient knowledge and understanding of NumPy can help you in making a good presence in the artificial learning or data science domain.

3. SciPy

SciPy is the other popular Python library used by data scientists, researchers for efficient mathematical operations like optimization, fast Fourier transform, image processing, and optimization, and linear algebra. It was designed to work with NumPy array objects and is a part of SciPy Stack that includes tools like Pandas, Matplotlib tools, etc. SciPy uses the multi-dimensional array data structure provided by the NumPy library for array manipulation subroutines. If you have just begun your journey as a data scientist, the SciPy library will guide you through the numerical computation concepts.

4. Matplotlib

Matplotlib library is a standard data visualization library used for generating two-dimensional graphs and diagrams. Matplotlib is one of the useful libraries in data science projects that helps in generating scatterplots, non-Cartesian coordinates graphs, bar charts, histograms, error charts by writing only a few lines of code. Python today is competing with advanced tools like MATLAB or Mathematica because of this data visualization library. It is user-friendly and provides an object-oriented API to help developers in embedding graphs and plots into their programs or applications.

5. Scikit Learn

Came into existence as Google Summer of Code Project, Scikit Learn has become one of the most popular libraries for data mining and data analysis tasks. It was built on the top of Numpy and SciPy libraries for specific machine learning functionalities such as classification, image processing, regression, model selection, pre-processing, customer segmentation, dimensionality, clustering, etc. It offers a wide range of machine learning algorithms (both supervised and unsupervised) via a consistent interface in Python. Unlike NumPy and Pandas, it focuses only on modeling data.

6. TensorFlow

Developed by the Google Brain team, TensorFlow is a popular computational framework for deep learning and machine learning. It is an artificial intelligence library that allows the easy deployment of machine learning applications and facilitates the deep learning models development. It helps data scientists or developers to work with artificial neural networks that need to manage large data sets. Its use is not limited to only scientific computation rather; it is widely used in speech recognition, object identification, classification, face recognition, video detection, etc.

7. Theano

Theano is the other useful library to perform computing operations for large multi-dimensional arrays. It is similar to TensorFlow but not that efficient. It is tightly integrated with the NumPy library and shares a similar interface. It uses GPU based infrastructure that processes operations in faster and quicker ways than CPU. It can perform 140 times faster computation than CPU. Due to in-built unit-testing and validation tools, Theano automatically avoids errors and bugs when processing exponential functions.  

8. Keras

Keras is one of the most powerful, user-friendly neural network Python libraries used in machine learning for building and training deep neural network code. It runs on top of TensorFlow, Theano, and Microsoft integrated CNTK (Microsoft Cognitive Toolkit) to serve as a backend. Keras offers high-level APIs to help developers working with images and text a lot easier. Keras is your best option if you are dealing with deep learning libraries for your work. Keras allows you to perform tasks such as computing loss functions, determine percentage accuracy, etc.

9. PyTorch

PyTorch is one of the largest machine learning libraries that is used in designing dynamic computational graphs, calculate automatic gradients, and fast tensor computations. It offers several tools that support deep learning, machine learning, computer vision, and natural language processing. It is based on the open-source C implemented Torch library with a wrapper in Lua. It provides a cloud-based environment to allow easy scaling of resources in testing or deployment.

For more information, check out the GitHub PyTorch page.


Python offers a lot of other tools helpful in the data science and machine learning domain, which makes it so popular and a must-have asset. Python has a big community of developers wherein developers create their libraries and expose them to general audiences later for their benefit. According to the PlaTo Survey report given by AIM, around 53.3% of data scientists prefer Python over other languages. Python has more than 137000 libraries that are used across multiple domains. To stay on the subject, we have listed only the top 9 libraries that are used the most in the data science and machine learning domain. For more blogs on data science and cloud computing, checkout E2E Networks website. Also if you are interested in taking a GPU server trial feel free to reach out to me @ 7795560646.

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June 29, 2022

Project Management for AI-ML-DL Projects

Managing a project properly is one of the factors behind its completion and subsequent success. The same can be said for any artificial intelligence (AI)/machine learning (ML)/deep learning (DL) project. Moreover, efficient management in this segment holds even more prominence as it requires continuous testing before delivering the final product.

An efficient project manager will ensure that there is ample time from the concept to the final product so that a client’s requirements are met without any delays and issues.

How is Project Management Done For AI, ML or DL Projects?

As already established, efficient project management is of great importance in AI/ML/DL projects. So, if you are planning to move into this field as a professional, here are some tips –

  • Identifying the problem-

The first step toward managing an AI project is the identification of the problem. What are we trying to solve or what outcome do we desire? AI is a means to receive the outcome that we desire. Multiple solutions are chosen on which AI solutions are built.

  • Testing whether the solution matches the problem-

After the problem has been identified, then testing the solution is done. We try to find out whether we have chosen the right solution for the problem. At this stage, we can ideally understand how to begin with an artificial intelligence or machine learning or deep learning project. We also need to understand whether customers will pay for this solution to the problem.

AI and ML engineers test this problem-solution fit through various techniques such as the traditional lean approach or the product design sprint. These techniques help us by analysing the solution within the deadline easily.

  • Preparing the data and managing it-

If you have a stable customer base for your AI, ML or DL solutions, then begin the project by collecting data and managing it. We begin by segregating the available data into unstructured and structured forms. It is easy to do the division of data in small and medium companies. It is because the amount of data is less. However, other players who own big businesses have large amounts of data to work on. Data engineers use all the tools and techniques to organise and clean up the data.

  • Choosing the algorithm for the problem-

To keep the blog simple, we will try not to mention the technical side of AI algorithms in the content here. There are different types of algorithms which depend on the type of machine learning technique we employ. If it is the supervised learning model, then the classification helps us in labelling the project and the regression helps us predict the quantity. A data engineer can choose from any of the popular algorithms like the Naïve Bayes classification or the random forest algorithm. If the unsupervised learning model is used, then clustering algorithms are used.

  • Training the algorithm-

For training algorithms, one needs to use various AI techniques, which are done through software developed by programmers. While most of the job is done in Python, nowadays, JavaScript, Java, C++ and Julia are also used. So, a developmental team is set up at this stage. These developers make a minimum threshold that is able to generate the necessary statistics to train the algorithm.  

  • Deployment of the project-

After the project is completed, then we come to its deployment. It can either be deployed on a local server or the Cloud. So, data engineers see if the local GPU or the Cloud GPU are in order. And, then they deploy the code along with the required dashboard to view the analytics.

Final Words-

To sum it up, this is a generic overview of how a project management system should work for AI/ML/DL projects. However, a point to keep in mind here is that this is not a universal process. The particulars will alter according to a specific project. 

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June 29, 2022

Top 7 AI & ML start-ups in Telecom Industry in India

With the multiple technological advancements witnessed by India as a country in the last few years, deep learning, machine learning and artificial intelligence have come across as futuristic technologies that will lead to the improved management of data hungry workloads.


The availability of artificial intelligence and machine learning in almost all industries today, including the telecom industry in India, has helped change the way of operational management for many existing businesses and startups that are the exclusive service providers in India.


In addition to that, the awareness and popularity of cloud GPU servers or other GPU cloud computing mediums have encouraged AI and ML startups in the telecom industry in India to take up their efficiency a notch higher by combining these technologies with cloud computing GPU. Let us look into the 7 AI and ML startups in the telecom industry in India 2022 below.


Top AI and ML Startups in Telecom Industry 

With 5G being the top priority for the majority of companies in the telecom industry in India, the importance of providing network affordability for everyone around the country has become the sole mission. Technologies like artificial intelligence and machine learning are the key digital transformation techniques that can change the way networks rotates in the country. The top startups include the following:


Founded in 2021, Wiom is a telecom startup using various technologies like deep learning and artificial intelligence to create a blockchain-based working model for internet delivery. It is an affordable scalable model that might incorporate GPU cloud servers in the future when data flow increases. 


As one of the companies that are strongly driven by data and unique state-of-the-art solutions for revenue generation and cost optimization, TechVantage is a startup in the telecom industry that betters the user experiences for leading telecom heroes with improved media generation and reach, using GPU cloud online


As one of the strongest performers is the customer analytics solutions, Manthan is a supporting startup in India in the telecom industry. It is an almost business assistant that can help with leveraging deep analytics for improved efficiency. For denser database management, NVIDIA A100 80 GB is one of their top choices. 


Just as NVIDIA is known as a top GPU cloud provider, NetraDyne can be named as a telecom startup, even if not directly. It aims to use artificial intelligence and machine learning to increase road safety which is also a key concern for the telecom providers, for their field team. It assists with fleet management. 

KeyPoint Tech

This AI- and ML-driven startup is all set to combine various technologies to provide improved technology solutions for all devices and platforms. At present, they do not use any available cloud GPU servers but expect to experiment with GPU cloud computing in the future when data inflow increases.



Actively known to resolve customer communication, it is also considered to be a startup in the telecom industry as it facilitates better communication among customers for increased engagement and satisfaction. 


An AI startup in Chennai, Facilio is a facility operation and maintenance solution that aims to improve the machine efficiency needed for network tower management, buildings, machines, etc.


In conclusion, the telecom industry in India is actively looking to improve the services provided to customers to ensure maximum customer satisfaction. From top-class networking solutions to better management of increasing databases using GPU cloud or other GPU online services to manage data hungry workloads efficiently, AI and MI-enabled solutions have taken the telecom industry by storm. Moreover, with the introduction of artificial intelligence and machine learning in this industry, the scope of innovation and improvement is higher than ever before.






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June 29, 2022

Top 7 AI Startups in Education Industry

The evolution of the global education system is an interesting thing to watch. The way this whole sector has transformed in the past decade can make a great case study on how modern technology like artificial intelligence (AI) makes a tangible difference in human life. 

In this evolution, edtech startups have played a pivotal role. And, in this write-up, you will get a chance to learn about some of them. So, read on to explore more.

Top AI Startups in the Education Industry-

Following is a list of education startups that are making a difference in the way this sector is transforming –

  1. Miko

Miko started its operations in 2015 in Mumbai, Maharashtra. Miko has made a companion for children. This companion is a bot which is powered by AI technology. The bot is able to perform an array of functions like talking, responding, educating, providing entertainment, and also understanding a child’s requirements. Additionally, the bot can answer what the child asks. It can also carry out a guided discussion for clarifying any topic to the child. Miko bots are integrated with a companion app which allows parents to control them through their Android and iOS devices. 

  1. iNurture

iNurture was founded in 2005 in Bengaluru, Karnataka. It provides universities assistance with job-oriented UG and PG courses. It offers courses in IT, innovation, marketing leadership, business analytics, financial services, design and new media, and design. One of its popular products is KRACKiN. It is an AI-powered platform which engages students and provides employment with career guidance. 

  1. Verzeo

Verzeo started its operations in 2018 in Bengaluru, Karnataka. It is a platform based on AI and ML. It provides academic programmes involving multi-disciplinary learning that can later culminate in getting an internship. These programmes are in subjects like artificial intelligence, machine learning, digital marketing and robotics.

  1. EnglishEdge 

EnglishEdge was founded in Noida in 2012. EnglishEdge provides courses driven by AI for getting skilled in English. There are several programmes to polish your English skills through courses provided online like professional edge, conversation edge, grammar edge and professional edge. There is also a portable lab for schools using smart classes for teaching the language. 

  1. CollPoll

CollPoll was founded in 2013 in Bengaluru, Karnataka. The platform is mobile- and web-based. CollPoll helps in managing educational institutions. It helps in the management of admission, curriculum, timetable, placement, fees and other features. College or university administrators, faculty and students can share opinions, ideas and information on a central server from their Android and iOS phones.

  1. Thinkster

Thinkster was founded in 2010 in Bengaluru, Karnataka. Thinkster is a program for learning mathematics and it is based on AI. The program is specifically focused on teaching mathematics to K-12 students. Students get a personalised experience as classes are conducted in a one-on-one session with the tutors of mathematics. Teachers can give scores for daily worksheets along with personalised comments for the improvement of students. The platform uses AI to analyse students’ performance. You can access the app through Android and iOS devices.

  1. ByteLearn 

ByteLearn was founded in Noida in 2020. ByteLean is an assistant driven by artificial intelligence which helps mathematics teachers and other coaches to tutor students on its platform. It provides students attention in one-on-one sessions. ByteLearn also helps students with personalised practice sessions.

Key Highlights

  • High demand for AI-powered personalised education, adaptive learning and task automation is steering the market.
  • Several AI segments such as speech and image recognition, machine learning algorithms and natural language processing can radically enhance the learning system with automatic performance assessment, 24x7 tutoring and support and personalised lessons.
  • As per the market reports of P&S Intelligence, the worldwide AI in the education industry has a valuation of $1.1 billion as of 2019.
  • In 2030, it is projected to attain $25.7 billion, indicating a 32.9% CAGR from 2020 to 2030.

Bottom Line

Rising reliability on smart devices, huge spending on AI technologies and edtech and highly developed learning infrastructure are the primary contributors to the growth education sector has witnessed recently. Notably, artificial intelligence in the education sector will expand drastically. However, certain unmapped areas require innovations.

With experienced well-coordinated teams and engaging ideas, AI education startups can achieve great success.

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