Python Advanced Guide to Artificial Intelligence (1 ed)

Demystify the complexity of machine learning techniques and create evolving, clever solutions to 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 TensorFlow 1.x, such as distributed TensorFlow with TF clusters and deploying 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:

• 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.

Who this book is for:

This learning path is for data scientists, machine learning engineers, and artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model.

You will encounter the advanced intricacies and complex use cases of deep learning and AI. Basic knowledge of Python programming and some understanding of machine learning concepts are required to get the most out of this learning path.