This course is the final installation in the series and complements the learnings from ML-I and ML-II. Here, we proceed with the more sophisticated deep learning methods. This course includes convolutional neural networks, recurrent neural networks (such as LSTM and variants), and GANs. In addition, students will familiarize themselves with data augmentation procedures and the use of pre-trained architectures for faster and more accurate results. The course is heavily hands-on and involves multiple business and/or industry-driven real-world projects. The course culminates with students giving a public presentation of original and novel applications of deep learning methods.