[체험판] Artificial Intelligence with Python

▶Book Description
Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you’ll learn about various algorithms that can be used to build Artificial Intelligence applications.
During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that’s based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide!

▶What You Will Learn
⦁ Realize different classification and regression techniques
⦁ Understand the concept of clustering and how to use it to automatically segment data
⦁ See how to build an intelligent recommender system
⦁ Understand logic programming and how to use it
⦁ Build automatic speech recognition systems
⦁ Understand the basics of heuristic search and genetic programming
⦁ Develop games using Artificial Intelligence
⦁ Learn how reinforcement learning works
⦁ Discover how to build intelligent applications centered on images, text, and time series data
⦁ See how to use deep learning algorithms and build applications based on it

▶Key Features
⦁ Step into the amazing world of intelligent apps using this comprehensive guide
⦁ Enter the world of Artificial Intelligence, explore it, and create your own applications
⦁ Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time

▶Who This Book Is For
This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks.

▶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.