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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 environment

Book Description

This 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 to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more.

By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems

This Learning Path includes content from the following Packt products:

• Mastering Machine Learning Algorithms by Giuseppe Bonaccorso
• Mastering TensorFlow 1.x by Armando Fandango
• Deep Learning for Computer Vision by Rajalingappaa Shanmugamani

What you will learn

• Explore how an ML model can be trained, optimized, and evaluated
• Work with Autoencoders and Generative Adversarial Networks
• Explore the most important Reinforcement Learning techniques
• Build end-to-end deep learning (CNN, RNN, and Autoencoders) models
• Define and train a model for image and video classification
• Deploy your deep learning models and optimize them for high performance