Introduction to NumPy - A Python Library

March 5, 2020

What is NumPy?

If you are to learn data science or machine learning, you need to know NumPy (numerical python). It is an open-source core library for scientific computing. You can also say it is a library available in python for scientific computing.

Numpy is one of the most useful libraries, especially when you are crunching numbers. Why is it so good? Because you can crunch numbers much faster rather than using python loops, and it has got many built-in functions, and it also works perfectly with matrice multiplication.

NumPy is written in C, which is the low-level language, in turn, makes its processing speed faster. Operations in NumPy are faster because they take advantage of parallelism i.e., single instruction multiple data (SIMD).

It also provides : 

  • an n-dimensional array of objects.
  • provides tools for integrating C, C++.
  • contains useful mathematical functions, linear algebra, Fourier transforms, and random number capabilities, sorting, and shape manipulation.
  • can be used as a multidimensional container for generic data.
  • use matplotlib with NumPy, which is used for data analysis and for making graphs.

Why use NumPy?

It provides an alternative to the regular python list. You can analyze the data much more efficiently than using python lists. It uses less memory space compared to lists so it can work with a vast amount of data. It can also work with SciPy and other Libraries available.

If you want to work with large datasets, there will be some operation you will need to perform, for example, operations on vectors, matrix manipulation, n-dimensional arrays, and regressions as well. In traditional python, these can be non-trivial to execute. With NumPy, they come out-of-the-box, which are already optimized and tested.

Moreover, you can use NumPy lists as NumPy array, and also you use files as NumPy array. With NumPy, it is convenient to work with statistical data, matrix multiplication, reshaping, and other machine learning packages. TensorFlow and scikit-learn use NumPy arrays to compute the matrix multiplication in the back end.

Using NumPy, you can work with image processing and computer graphics as images are represented as multidimensional arrays of numbers and can perform various operations on images using NumPy, such as mirroring the image, rotating, and sharing images.

Getting started 

Before starting with NumPy, you must know python lists. 

Python list is somewhat like an array in other languages; it is a collection of heterogeneous types of values that we can add, delete or manipulate. The elements are enclosed within square brackets[].

You can create a Python list by writing the list name and the elements assigned within square brackets.

Example: t = [2,4,8,45]

Python list has some drawbacks python NumPy overcomes these drawbacks.

For example:

If you try to multiply two lists, it will give an error.

This is because it has no idea how to do this calculation with the list.

To overcome this, you have to multiply the elements of both the lists by accessing them from their index number, which is a lengthy and time-consuming process.

To overcome such type of problems, you have to use NumPy.

For example, using NumPy, you can multiply the elements without writing the index numbers again and again.

How to use NumPy?

There are several IDE’s where you can run python code, for example, pycharm, Jupiter notebook, google colab, etc.

I’ll be using google colab.


You can install NumPy using pip.

After installing, you need to import NumPy.


There are several operations you can perform using NumPy. We will learn each operation one by one in this section.

Creating an array

  • You can create and enter elements directly.

Here you just need to write an array name and assign the elements.

  • You can also use the list as an array.

In this example, I made a list “a” and assigned that list as an input to the NumPy array “z.”

  • Create 2D arrays
  • Create zero’s array.

You can create an array of any length with the elements as 0’s.

In this example, I have created an array “a” with a length of 3 elements.

  • Create one’s array.

You can also create an array of any length with the elements as 1’s.

We can also see the type (data type) of the array:

We can also see the type (data type) of the element:

You can check the shape of the array:

  • We can make an array by giving starting point, ending point and number of elements

In this example, 2 is the starting point, 10 is the ending point, 5 is the number of elements. It’s useful while plotting a graph, for example, plotting the x-axis.

  • You can create a random array using random.randint(limit, size) method

Get elements of an array:

  • Get specific elements (using index number) from the array.
  • To get elements from within a range.
  • To get the last element.

Mathematical operations with NumPy:

  • Addition: you can simply add elements of two arrays.

You can just add to each element:

Similarly, you can multiply 10 with each element:

  • Multiplication: you can multiply two arrays.
  • Dot product: you can find the dot product of two arrays using the “@” symbol.
  • Sort: you can sort an array:
  • To get the values of array less than a number say 3,

Here, you will get the values true or false. If the value is less than 3 you will get true, else false.

  • To get the values of array greater than a number say 3
  • If you want an array which is greater than 3
  • Photos with NumPy:
  • you can read a photo as a NumPy array:
  • You can check the type of picture:
  • You can check the shape of the image by passing the array name.

Here, 165 is the number of rows in an image, 220 is the number of columns in a picture, and 3 is the color of the image with the 3 channels RGB(red, green, blue). 

  • To view an image:
  • To make the image upside down:

Here first colon (:) is the starting point second colon (:) is the stop point, and -1 is the step. -1 is for backward and +1 is for forwarding.

  • To mirror an image
  • To crop an image, you have to provide a range
  • You can also take every other row or column.
  • You can also perform mathematical functions on the image array.

Such as for :

Sin: print(np.sin(img))

Sum: print(np.sum(img))

Product: print(

Minimum: print(np.min(img))

Maximum: print(np.max(img))

Argmax: print(np.argmax(img))

Argmin: print(np.argmin(img))

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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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