Machine Learning and AI Bootcamp includes fundamental concepts of Machine Learning and AI systems and also neural networks and deep learning. The following topics will be covered: Learning by fitting a model to data; Optimizing a cost function; Handling, cleaning, and preparing data; Selecting and engineering features; Selecting a model and tuning hyperparameters using cross-validation, underfitting and overfitting; Linear and Polynomial Regression, Logistic Regression, k-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forests, and Ensemble methods; Building and training neural nets using TensorFlow; and Understanding feedforward neural nets, convolutional nets, recurrent nets, long short-term memory nets, and autoencoders and techniques for training deep neural nets.