Artificial Intelligence (AI)

• Introduction to Artificial Intelligence
• Fundamentals of Artificial Intelligence
• Applications of Artificial Intelligence
• Future of Artificial Intelligence
• Neural Network Introduction (Intuition behind Artificial Intelligence)
• Practical examples (Introduction to Numpy, Pandas and sci- kitlearn)

• Building block for Neural Networks
• Single NN
• Input/output Mapping
• Type Activation Functions
• Neural Network Architecture
• Practical examples (Introduction to ANN APIs and libraries)

• EDA / Data wrangling
• Back Propagation
• Loss Functions
• Hyperparameter Optimization
• Gradient

• Convolutional Neural Network
• Computer Vision real life application
• Overfitting/Underfitting
• NN issues (vanishing gradients etc)
• Model improvement and generalization techniques (Data Augmentation, Dropout, batch Normalization etc.)

• Recurrent Neural Network
• RNN variants (LSTMs etc)
• BI basic idea and importance

Upon completion of this course, participants will be able to:                    

• • Understand the fundamental concepts of Artificial Intelligence (AI), Neural Networks, Deep Learning, Natural Language Processing etc.
  • Build, train, and deploy different types of predictive models with respect to industry best practices and large-scale real- world application requirements.
  • Application of Deep Learning to real-world scenarios such as object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers.
  • Master Deep Learning at scale with accelerated hardware and GPUs.              
  • Use of popular Deep Learning libraries such as Keras, PyTorch, and Tensorflow applied to industry problems.
  • Integrate the AI based predictive models into professional applications for real time predictions.