Clearly define AI and Deep Learning Build Convolutional Neural Network on IBM Watson for MNIST and CIFAR 10 Datasets (No coding) Build Supervised and Unsupervised Machine learning Models using IBM Watson (No coding) Test Natural Language Processing (NLP) models using IBM Watson Build VGG like nets, Stateful RNN nets, reuse ResNet50 using Keras Test Reinforcement Learning with Keras and OpenAI Gym Test Recurrent Neural Network (RNN) on Mathworks Learn to code with Python the easy way Test Feed Forward Neural Networks(Classification and Regression) on Tensor Flow simulator and Google Colab Solve popular data sets like MNIST, CIFAR 10, with CNN using Keras Learn a few useful and important application of popular libraries like Numpy, Pandas, Matplotlib Migrate Deep Neural Network models from IBM Watson to run on local your Jupyter notebook Apply Transfer Learning techniques such as Reusing, Retraining with Keras Be able to identify the positive and the negative impact that AI will create
