Artificial Intelligence
Artificial Intelligence And Machine Learning
Artificial Intelligence and Machine Learning

We are focusing on transformative technologies like computer vision and predictive analysis using machine learning that will create the next quantum gain in customer experience and unit economics of businesses.

Applications of Computer vision are face detection, object detection and tracking, object recognition.
For Computer vision we use tools like OpenCV, Dlib, and Convolutional Neural Networks.

OpenCV (Open Source Computer Vision Library) and Dlib are open source computer vision and machine learning software library, which was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products.

Convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural networks, most commonly applied to analyzing visual imagery. CNNs, like neural networks, are made up of neurons with learnable weights and biases.

Other Machine learning frameworks used are Scikit-learn, Tensorflow,and Keras.

Scikit-learn provides a range of supervised and unsupervised learning algorithms via a consistent interface in Python. This stack includes:

  • NumPy : Base n-dimensional array package
  • SciPy : Fundamental library for scientific computing
  • Matplotlib : Comprehensive 2D/3D plotting
  • IPython : Enhanced interactive console
  • Sympy : Symbolic mathematics
  • Pandas : Data structures and analysis

TensorFlow™ is an open source software library for high performance numerical computation developed by Google. It is a symbolic math library, and is also used for machine learning applications such as neural networks.

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow. Keras allows easy and fast prototyping (through user friendliness, modularity, and extensibility). It supports both convolutional networks and recurrent networks, as well as combinations of the two and runs seamlessly on CPU and GPU.

Predictive Analysis – Advantages
Improve Business Advantage
Staying competitive by having a better understanding of the trends and gaining insights.
Identify New Revenue Opportunities
Companies can check on the historical buying patterns of their customers and make reasonable decisions based on it. It is based on these assumptions that they release promotional offers, discounts and coupons.
Businesses can Take Their Service to a Totally New Level
Business can deliver superior customer experience by analyzing what they would be needing in the near future
It Helps them Understand More About the Users
With unprecedented data in their hands, a marketer or developer can make use of the social value metrics and incorporate the funnel analytics method to influence influencers.
Helps in Detecting Hidden Insights in Customer Data
Can deliver personalized customer experience. Helps companiesidentity customers who have the highest propensity to buy.
All the Large Amounts of Data that’s Coming in — You can Use them all Now
Can now search, retrieve information and make the best use of all the business data.
Understand What Your Customer Wants
you will be able to analyze all the structured (geographic and demographic data) and unstructured (customer inputs from social media) data and forecast customer expectations.
Help make Micro Decisions
This will help them strategize a plan and focus on both the urgent aspects of the business and the ones that may not seem so urgent now, but will eventually turn important in the future.
Identify Areas of Attrition
You can identify the reasons for a customer’s exit and model out others who are planning to leave. If you know this beforehand, you can plan strategies that would help you retain them. With predictive analytics, the advantage is that you can focus on relationship-building.