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 network 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.