Erstwhile Windows and Office maker has transformed its business into a digital conglomerate with a number of growth engines.

Microsoft — Revenue Engines ( Picture by author)

The beauty of Microsoft business is its strong position in both Business to Consumer (B2C) and Business to Business (B2B) marketplace. It has a number of growth engines which are gaining market share and the overall market in most of these areas are growing at a fast pace.

Office 365: Office products are so deeply ingrained in the business and personal user’s mindshare that it is nearly impossible to encroach its market share. Freeware like open office and Apple office bundle is trying to gain over office from many years but still, they have not received any significant traction. The…

Inside AI

A balanced recommended diet with the constraint of the tight budget in this pandemic is a challenge. It can be addressed to some extent with linear optimisation model

Photo by Brooke Lark on Unsplash

We all have different sets of goal in our life, and most of the time, there are different approaches to achieve these goals. One of such important goal is to have a balanced diet, and it has additional constraint of the tight budget in this pandemic.

In this article, I will discuss the way we can model our diet requirement with different constraints like budget, energy and fat requirements etc. and get the most optimised diet plan recommendation.

Let us assume that I like to have meat, eggs, broccoli, milk, apple, rice and potatoes. …

Technological disruptions are reshaping the operations across industries. In a few years, the lines between tech and non-tech sectors will be blurred as technology gets infused within the conventional industry sector.

Photo by Umberto on Unsplash

Our daily life is getting more intertwined with technology, and we are living more in the digital world than in the physical world now. Digitalisation, artificial intelligence, internet of things, big data and analytics has leapt from textbooks and academic world to our life in the last five years. These technological trends bolstered by breakthroughs in the semiconductor innovations are redefining all industries.

As we are living and breathing in the digital world, technology is taking centre stage in companies operations. …

Inside AI

The amount of data that is getting available is increasing super exponentially with the advancement of IoT, sensors and storage capability.

Photo by Umberto on Unsplash

The world we all will live in a decade will completely be different, and complex than the current world.

As the world is getting complex, so is the underlying data. One of the challenges of machine learning is the increasing need for computational power and time to process the complex dataset.

Scikit-learn doesn’t support the GPU like Keras or TensorFlow, but we can leverage the multi-core CPU to execute several tasks in parallel.

In this article, we will see the process to accelerate the machine learning and shorten the time for building the model.

We will use the “Volcanoes” dataset…

Inside AI

“One look is worth a thousand words.” These quick data analyses have the potential to improve model prediction accuracy and focussed data pre-processing

Data visualisation based on the code discussed in this article — Image from Author

Data analysis is a vital element of any machine learning workflow. The performance and accuracy of any machine learning model prediction hinge on the data analysis and follow-on appropriate data preprocessing. Every machine learning professional should be adept in data analysis.

In this article, I will discuss four very quick data visualisation techniques which can be achieved with few lines of code and can help to plan the data pre-processing required.

We will be using Indian Liver Patient Dataset from the open ML to learn quick and efficient data visualisation techniques. …

Inside AI

It is an art to develop a generalised machine learning model with the minimum required dimensions and ignoring the lesser important features.

Photo by Thanos Pal on Unsplash

During the survey and data collection step, we do not know which feature/attribute have a strong influence on the output and the ones which do not have that much effect. Due to this, we collect or measure as many logical attributes as possible in this stage.

The machine learning model becomes complex, and also computationally becomes expensive as the number of features in the training dataset increases.

The aim is to develop a trained machine learning model with the minimal required feature and which can predict the data points with acceptable accuracy. We should not oversimplify the model and lose…

Inside AI

Most business cases relate to predicting a minority class event like fraud, detection of alignment, etc. The prediction accuracy of a rare event for a machine learning model trained on imbalanced data is abysmally bad.

Photo by Ammar ElAmir on Unsplash

Out of billions of financial transactions, only a few involve cheating and fraud. Out of millions of cars on roads, only a few break down in the middle of the highway and rest drive fine. If we pay close attention to our daily activities, then a few exception incidents can also be identified. The same skewed data is present in many data points where one or a few couples of classes covers the majority of the cases.

When we provide these imbalanced data points to machine learning algorithms, a few majority classes heavily influence at the cost of ignoring the…

Inside AI

Pandas is a swiss knife available to data scientist and machine learning professionals. 3 advanced powerful methods for real-life projects

Photo by Sid Balachandran and Sai Kiran Anagani on Unsplash

In my view, Pandas and NumPy library together has saved hundreds of hours of programming time and is an invaluable tool for data scientist and machine learning professionals.

It is nearly impossible to be a good hands-on data scientist or machine learning professional without having a good grasp over these two libraries.

Most of the time, raw data available has few blank values, not in the right format, or spread across several different sources with a primary key. …

My journey and career transformation from mechanical engineer to machine learning scientist, and lessons learned

Photo by Nick Morrison on Unsplash

Artificial intelligence, machine learning, deep learning, internet of things etc. is the buzzword from the last few years. If someone in the strategy, business or IT consulting, doesn’t know at least the basics of it then I guess they are pretty much redundant.

To keep up with the waves two years back, I decided to get acquainted with machine learning and deep learning.

Many of us want to make a career change to machine learning/AI or come from non-science background hence find it challenging to gain the requisite knowledge. In this article, I am going to share my journey and…

Inside AI

No need for generic filters provided by Instagram and other services

Images based on the code discussed in this article. The original picture is taken by author

The recent development in smartphone cameras and services like Instagram has generated a lot of traction in digital photography. In the days of film roll cameras, I used to be very selective as film rolls and developing photos was a costly affair.

Today in this article, I will discuss the way we can develop our bespoke photo filters using Python. We do not need to use the generic filters provided by Instagram and other such services. These custom filters will help to put your photos to stand apart from the crowd using the same limited filters.

I have taken the…

Kaushik Choudhury

Kaushik Choudhury is an experienced Supply Chain Strategy and Digital Transformation manager in one of the Big 4 Consulting firm in the UK.

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