Demystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problems Key Features * Master supervised, unsupervised, and semi-supervised ML algorithms and their implementation * Build deep learning models for object detection, image classification, similarity learning, and more * Build, deploy, and scale end-to-end deep neural network models in a production environmentBook DescriptionThis Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras