Course: Natural Language Processing

Explore how sequential models in Deep Learning can be used to distinguish between reliable and unreliable news stories. The goal of the Fake News Challenge is to explore how artificial intelligence technologies, particularly machine learning and natural language processing, might be leveraged to combat the fake news problem. We believe that these AI technologies hold promise for significantly automating parts of the procedure human fact checkers use today to determine if a story is real or hoax

Skills and Tools

Statistical NLP techniques, Glove, LSTM, Attention Models

Course: Computer Vision

Recognize, identify and classify faces within images using CNN and image recognition algorithms. In this hands-on project, the goal is to build a face recognition system, which includes building a face detector to locate the position of a face in an image and a face identification model to recognize whose face it is by matching it to the existing database of faces.

Skills and Tools

computer Vision, CNN, Transfer Learning, Object detection

Course:Introduction to Neural Network and Deep Learning

SVHN is a real-world image dataset for developing object recognition algorithms with a requirement on data formatting but comes from a significantly harder, unsolved, real-world problem (recognizing digits and numbers in natural scene images). SVHN is obtained from house numbers in Google Street View images. The objective of the project is to learn how to implement a simple image classification pipeline based on the k-Nearest Neighbour and a deep neural network.

Skills and Tools

Deep Learning, Keras, Image Recognition, Neural Networks