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Python Artificial Intelligence Projects for Beginners
Python Artificial Intelligence Projects for Beginners

Python Artificial Intelligence Projects for Beginners: Get up and running with Artificial Intelligence using 8 smart and exciting AI applications

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Profile Icon Dr. Joshua Eckroth
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Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.5 (6 Ratings)
Paperback Jul 2018 162 pages 1st Edition
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Arrow left icon
Profile Icon Dr. Joshua Eckroth
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$19.99 per month
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.5 (6 Ratings)
Paperback Jul 2018 162 pages 1st Edition
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$9.99 $21.99
Paperback
$26.99
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Free Trial
Renews at $19.99p/m
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Python Artificial Intelligence Projects for Beginners

Prediction with Random Forests

In this chapter, we're going to look at classification techniques with random forests. We're going to use scikit-learn, just like we did in the previous chapter. We're going to look at examples of predicting bird species from descriptive attributes and then use a confusion matrix on them.

 

Here's a detailed list of the topics:

  • Classification and techniques for evaluation
  • Predicting bird species with random forests
  • Confusion matrix

Random forests

Random forests are extensions of decision trees and are a kind of ensemble method. 

Ensemble methods can achieve high accuracy by building several classifiers and running a each one independently. When a classifier makes a decision, you can make use of the most common and the average decision. If we use the most common method, it is called voting.

Here's a diagram depicting the ensemble method:

You can think of each classifier as being specialized for a unique perspective on the data. Each classifier may be a different type. For example, you can combine a decision tree and a logistic regression and a neural net, or the classifiers may be the same type but trained on different parts or subsets of the training data.

A random forest is a collection or ensemble of decision trees. Each tree is trained on a random subset of the attributes, as shown in...

Predicting bird species with random forests

Here we will be using random forests to predict a bird's species. We will use the Caltech-UC San Diego dataset (http://www.vision.caltech.edu/visipedia/CUB-200-2011.html), which contains about 12,000 photos of birds from 200 different species. Here we are not going to look at the pictures because that would need a convolutional neural network (CNN) and this will be covered in later chapters. CNNs can handle pictures much better than a random forest. Instead, we will be using attributes of the birds such as size, shape, and color.

Here are just some of the species in the dataset:

Some, such as the American Crow and the Fish Crow, are almost indistinguishable, at least visually. The attributes for each photo, such as color and size, have actually been labeled by humans. Caltech and UCSD used human workers on Amazon's Mechanical...

Summary

In this chapter, we learned about random forests and classify bird species . Later, we discussed the confusion matrix and different graphs that gave us output based on random trees, decision trees, and SVM.

In the next chapter, we'll go look at comment classification using bag-of-words models and Word2Vec models.

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Key benefits

  • Explore a variety of AI projects with Python
  • Get well-versed with different types of neural networks and popular deep learning algorithms
  • Leverage popular Python deep learning libraries for your AI projects

Description

Artificial Intelligence (AI) is the newest technology that’s being employed among varied businesses, industries, and sectors. Python Artificial Intelligence Projects for Beginners demonstrates AI projects in Python, covering modern techniques that make up the world of Artificial Intelligence. This book begins with helping you to build your first prediction model using the popular Python library, scikit-learn. You will understand how to build a classifier using an effective machine learning technique, random forest, and decision trees. With exciting projects on predicting bird species, analyzing student performance data, song genre identification, and spam detection, you will learn the fundamentals and various algorithms and techniques that foster the development of these smart applications. In the concluding chapters, you will also understand deep learning and neural network mechanisms through these projects with the help of the Keras library. By the end of this book, you will be confident in building your own AI projects with Python and be ready to take on more advanced projects as you progress

Who is this book for?

Python Artificial Intelligence Projects for Beginners is for Python developers who want to take their first step into the world of Artificial Intelligence using easy-to-follow projects. Basic working knowledge of Python programming is expected so that you’re able to play around with code

What you will learn

  • Build a prediction model using decision trees and random forest
  • Use neural networks, decision trees, and random forests for classification
  • Detect YouTube comment spam with a bag-of-words and random forests
  • Identify handwritten mathematical symbols with convolutional neural networks
  • Revise the bird species identifier to use images
  • Learn to detect positive and negative sentiment in user reviews

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Publication date : Jul 31, 2018
Length: 162 pages
Edition : 1st
Language : English
ISBN-13 : 9781789539462
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Product Details

Publication date : Jul 31, 2018
Length: 162 pages
Edition : 1st
Language : English
ISBN-13 : 9781789539462
Vendor :
Google
Category :
Languages :

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Table of Contents

6 Chapters
Building Your Own Prediction Models Chevron down icon Chevron up icon
Prediction with Random Forests Chevron down icon Chevron up icon
Applications for Comment Classification Chevron down icon Chevron up icon
Neural Networks Chevron down icon Chevron up icon
Deep Learning Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.5
(6 Ratings)
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4 star 16.7%
3 star 33.3%
2 star 33.3%
1 star 16.7%
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kenneth larson Jun 24, 2024
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
I did fine until Ch. 2 when things started not to work in Colab. I have now switched to AI tutorials on the net from the English guy from "TensorFlow ML Zero to Hero". All the examples work. I am now on simple vision AI problems. The IBM series from "Martin" (also English) was good too, and the best on backpropagation was DeepLizard -- she did a great job, tho I still need to go thru all the math -- I should be able to understand SOME of it; I have a masters in math. :-) Python has been no problem, but the classes and functions are GREAT for AI. So, the book was a nice introduction but NOT what I was hoping for -- I expected more working code and examples.
Feefo Verified review Feefo
scout3801 Nov 14, 2019
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
It's confusing, needs heavy editing. References to data sets previously used that are not mentioned anywhere in the book, weird grammar that sets you down the wrong path while reading, a continuation of things that throw you off.But... interesting subject, and I thought the author did a decent job of keeping it interesting, and relevant.
Amazon Verified review Amazon
Nicholas A Nardi Sep 20, 2018
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Some good information...requires lots of research...
Amazon Verified review Amazon
Nancy Ramirez Aug 01, 2020
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
I tried to give this book a chance by ignoring the strangely worded sentences, but even then the author skips over content with just one or two lines on it when it deserves more attention. Two stars because you can figure things out with the code, but it's not worth it.
Amazon Verified review Amazon
Booksadored Jun 26, 2022
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
Good book intention. LOTS of typos. Needs a good proofreader and clean up for the next release. Too many things missing or broken. I tried using it for training students, but there are too many datasets missing or links to datasets that are missing. Jupyter Notebook examples in the book are not referenced to the actual files that go along with each chapter. It needed me to do a lot of work to update packages to get everything actually working. I may check back here to report specific errors when I am finished fixing things. Don't hold your breath.
Amazon Verified review Amazon
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