Artificial Intelligence With Python[January 2017]下载

Artificial intelligence is becoming increasingly relevant in the modern world where everything is driven by data and automation. It is used extensively across many fields such as image recognition, robotics, search engines, and self-driving cars. In this book, we will explore various real-world scenarios. We will understand what algorithms to use in a given context and write functional code using this exciting book. We will start by talking about various realms of artificial intelligence. We’ll then move on to discuss more complex algorithms, such as Extremely Random Forests, Hidden Markov Models, Genetic Algorithms, Artificial Neural Networks, and Convolutional Neural Networks, and so on. This book is for Python programmers looking to use artificial intelligence algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be helpful so you can play around with the code. It is also useful to experienced Python programmers who are looking to implement artificial intelligence techniques. You will learn how to make informed decisions about the type of algorithms you need to use and how to implement those algorithms to get the best possible results. If you want to build versatile applications that can make sense of images, text, speech, or some other form of data, this book on artificial intelligence will definitely come to your rescue! What this book covers Chapter 1, Introduction to Artificial Intelligence, teaches you various introductory concepts in artificial intelligence. It talks about applications, branches, and modeling of Artificial Intelligence. It walks the reader through the installation of necessary Python packages. Chapter 2, Classification and Regression Using Supervised Learning, covers various supervised learning techniques for classification and regression. You will learn how to analyze income data and predict housing prices. Chapter 3, Predictive Analytics with Ensemble Learning, explains predictive modeling techniques using Ensemble Learning, particularly focused on Random Forests. We will learn how to apply these techniques to predict traffic on the roads near sports stadiums. Chapter 4, Detecting Patterns with Unsupervised Learning, covers unsupervised learning algorithms including K-means and Mean Shift Clustering. We will learn how to apply these algorithms to stock market data and customer segmentation. Chapter 5, Building Recommender Systems, illustrates algorithms used to build recommendation engines. You will learn how to apply these algorithms to collaborative filtering and movie recommendations. Chapter 6, Logic Programming, covers the building blocks of logic programming. We will see various applications, including expression matching, parsing family trees, and solving puzzles. Chapter 7, Heuristic Search Techniques, shows heuristic search techniques that are used to search the solution space. We will learn about various applications such as simulated annealing, region coloring, and maze solving. Chapter 8, Genetic Algorithms, covers evolutionary algorithms and genetic programming. We will learn about various concepts such as crossover, mutation, and fitness functions. We will then use these concepts to solve the symbol regression problem and build an intelligent robot controller. Chapter 9, Building Games with Artificial Intelligence, teaches you how to build games with artificial intelligence. We will learn how to build various games including Tic Tac Toe, Connect Four, and Hexapawn. Chapter 10, Natural Language Processing, covers techniques used to analyze text data including tokenization, stemming, bag of words, and so on. We will learn how to use these techniques to do sentiment analysis and topic modeling. Chapter 11, Probabilistic Reasoning for Sequential Data, shows you techniques used to analyze time series and sequential data including Hidden Markov models and Conditional Random Fields. We will learn how to apply these techniques to text sequence analysis and stock market predictions. Chapter 12, Building A Speech Recognizer, demonstrates algorithms used to analyze speech data. We will learn how to build speech recognition systems. Chapter 13, Object Detection and Tracking, It covers algorithms related to object detection and tracking in live video. We will learn about various techniques including optical flow, face tracking, and eye tracking. Chapter 14, Artificial Neural Networks, covers algorithms used to build neural networks. We will learn how to build an Optical Character Recognition system using neural networks. Chapter 15, Reinforcement Learning, teaches the techniques used to build reinforcement learning systems. We will learn how to build learning agents that can learn from interacting with the environment. Chapter 16, Deep Learning with Convolutional Neural Networks, covers algorithms used to build deep learning systems using Convolutional Neural Networks. We will learn how to use TensorFlow to build neural networks. We will then use it to build an image classifier using convolutional neural networks.