Artificial Intelligence (AI) is the newest emerging and disruptive technology among varied businesses, industries, and sectors. This book demonstrates AI projects in Python, covering modern techniques that make up the world of AI.
This book begins with building your first prediction model using the popular Python library, scikit-learn. You will understand how to build a classifier using effective machine learning techniques: 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 such smart applications. You will also understand deep learning and the neural network mechanism through these projects with the use of the Keras library.
By the end of this book, you will be confident to build your own AI projects with Python and be ready to take on more advanced content as you go ahead.
This book is for Python developers who want to take their first step in the world of artificial intelligence using easy-to-follow projects. Basic working knowledge of Python programming is expected so that you can play around with the code.
Chapter 1, Building Your Own Prediction Models, introduces classification and techniques for evaluation, and then explains decision trees, followed by a coding project in which a predictor for student performance is built.
Chapter 2, Prediction with Random Forests, looks at random forests and uses them in a coding project for classifying bird species.
Chapter 3, Applications for Comment Classification, introduces text processing and the bag-of-words technique. Then shows how this technique can be used to build a spam detector for YouTube comments. Next, you will learn about the sophisticated Word2Vec model and practice it with a coding project that detects positive and negative product, restaurant, and movie reviews.
Chapter 4, Neural Networks, covers a brief introduction to neural networks, proceeds with feedforward neural networks, and looks at a program to identify the genre of a song with neural networks. Finally, you will revise the spam detector from earlier to make it work with neural networks.
Chapter 5, Deep Learning, discusses deep learning and CNNs. You will practice convolutional neural networks and deep learning with two projects. First, you will build a system that can read handwritten mathematical symbols and then revisit the bird species identifier and change the implementation to use a deep convolutional neural network that is significantly more accurate.
- You need to have a basic understanding of Python and its scientific computing libraries
- Get Jupyter Notebook installed, preferably via Anaconda
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