Book Image

Python: Advanced Guide to Artificial Intelligence

By : Giuseppe Bonaccorso, Rajalingappaa Shanmugamani
Book Image

Python: Advanced Guide to Artificial Intelligence

By: Giuseppe Bonaccorso, Rajalingappaa Shanmugamani

Overview of this book

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
Table of Contents (31 chapters)
Title Page
About Packt
Tensor Processing Units


Now that you have learned how to create a basic regression model with TensorFlow, let's try to run it on example datasets from different domains. The dataset that we generated as an example dataset is univariate, namely, the target was dependent only on one feature.


Most of the datasets, in reality, are multivariate. To emphasize a little more, the target depends on multiple variables or features, thus the regression model is called multi-regression or multidimensional regression.

We first start with the most popular Boston dataset. This dataset contains 13 attributes of 506 houses in Boston such as the average number of rooms per dwelling, nitric oxide concentration, weighted distances to five Boston employment centers, and so on. The target is the median value of owner-occupied homes. Let's dive into exploring a regression model for this dataset.

Load the dataset from the sklearn library and look at its description: