Python Artificial Intelligence Projects for Beginners : Get up and Running with Artificial Intelligence Using 8 Smart and Exciting AI Applications

Build smart applications by implementing real-world artificial intelligence projects Key Features 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 Book 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 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 Who this book is 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 Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you Contents......Page 3 Preface......Page 4 1 Build Prediction Models......Page 7 Classification overview and evaluation techniques......Page 8 Evaluation......Page 9 Decision trees......Page 12 Common APIs for scikit-learn classifiers......Page 16 Prediction involving decision trees and student performance data......Page 17 Summary......Page 25 Random forests......Page 26 Predicting bird species with random forests......Page 28 Making a confusion matrix for the data......Page 37 Summary......Page 47 3 Comment Classification......Page 48 Text classification......Page 49 Machine learning techniques......Page 50 Bag of words......Page 51 Detecting YouTube comment spam......Page 53 Word2Vec models......Page 65 Doc2Vec......Page 67 Document vector......Page 68 Detecting positive or negative sentiments in user reviews......Page 69 Summary......Page 78 Understanding neural networks......Page 79 Feed-forward neural networks......Page 82 Identifying the genre of a song with neural networks......Page 86 Revising the spam detector to use neural networks......Page 97 Summary......Page 105 5 Deep Learning......Page 106 Convolutions and pooling......Page 107 Identifying handwritten mathematical symbols with CNNs......Page 115 Revisiting the bird species identifier to use images......Page 133 Summary......Page 144 Index......Page 146 This book demonstrates AI projects in Python covering modern techniques that make up the world of Artificial Intelligence. You will come across a variety of real-world projects on classifying data, text processing techniques, deep learning and neural networks