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Title Artificial intelligence with Python: your complete guide to building intelligent apps using Python 3.x and TensorFlow 2. — Second edition.
Creators Artasanchez Alberto
Other creators Joshi Prateek
Collection Электронные книги зарубежных издательств ; Общая коллекция
Subjects Python (Computer program language) ; Artificial intelligence — Data processing. ; Application software — Development. ; EBSCO eBooks
Document type Other
File type PDF
Language English
Rights Доступ по паролю из сети Интернет (чтение, печать, копирование)
Record key on1139766927
Record create date 2/10/2020

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  • Cover
  • Copyright
  • Packt Page
  • Contributors
  • Table of Contents
  • Preface
  • Chapter 1: Introduction to Artificial Intelligence
    • What is AI?
    • Why do we need to study AI?
    • Branches of AI
    • The five tribes of machine learning
    • Defining intelligence using the Turing test
    • Making machines think like humans
    • Building rational agents
    • General Problem Solver
      • Solving a problem with GPS
    • Building an intelligent agent
      • Types of models
    • Installing Python 3
      • Installing on Ubuntu
      • Installing on Mac OS X
      • Installing on Windows
    • Installing packages
    • Loading data
    • Summary
  • Chapter 2: Fundamental Use Cases for Artificial Intelligence
    • Representative AI use cases
    • Digital personal assistants and chatbots
    • Personal chauffeur
    • Shipping and warehouse management
    • Human health
    • Knowledge search
    • Recommendation systems
    • The smart home
    • Gaming
    • Movie making
    • Underwriting and deal analysis
    • Data cleansing and transformation
    • Summary
    • References
  • Chapter 3: Machine Learning Pipelines
    • What is a machine learning pipeline?
    • Problem definition
    • Data ingestion
    • Data preparation
      • Missing values
      • Duplicate records or values
      • Feature scaling
      • Inconsistent values
      • Inconsistent date formatting
    • Data segregation
    • Model training
      • Candidate model evaluation and selection
      • Model deployment
      • Performance monitoring
        • Model performance
        • Operational performance
        • Total cost of ownership (TCO)
        • Service performance
    • Summary
  • Chapter 4: Feature Selection and Feature Engineering
    • Feature selection
      • Feature importance
      • Univariate selection
      • Correlation heatmaps
        • Wrapper-based methods
        • Filter-based methods
        • Embedded methods
    • Feature engineering
      • Imputation
    • Outlier management
    • One-hot encoding
    • Log transform
    • Scaling
    • Date manipulation
    • Summary
  • Chapter 5: Classification and Regression Using Supervised Learning
    • Supervised versus unsupervised learning
    • What is classification?
    • Preprocessing data
      • Binarization
      • Mean removal
      • Scaling
      • Normalization
    • Label encoding
    • Logistic regression classifiers
    • The Naïve Bayes classifier
    • Confusion matrixes
    • Support Vector Machines
    • Classifying income data using Support Vector Machines
    • What is regression?
    • Building a single-variable regressor
    • Building a multivariable regressor
    • Estimating housing prices using a Support Vector Regressor
    • Summary
  • Chapter 6: Predictive Analytics with Ensemble Learning
    • What are decision trees?
      • Building a decision tree classifier
    • What is ensemble learning?
      • Building learning models with ensemble learning
    • What are random forests and extremely random forests?
      • Building random forest and extremely random forest classifiers
      • Estimating the confidence measure of the predictions
    • Dealing with class imbalance
    • Finding optimal training parameters using grid search
    • Computing relative feature importance
    • Predicting traffic using an extremely random forest regressor
    • Summary
  • Chapter 7: Detecting Patterns with Unsupervised Learning
    • What is unsupervised learning?
    • Clustering data with the K-Means algorithm
      • Estimating the number of clusters with the Mean Shift algorithm
      • Estimating the quality of clustering with silhouette scores
    • What are Gaussian Mixture Models?
      • Building a classifier based on Gaussian Mixture Models
    • Finding subgroups in stock market using the Affinity Propagation model
    • Segmenting the market based on shopping patterns
    • Summary
  • Chapter 8: Building Recommender Systems
    • Extracting the nearest neighbors
    • Building a K-nearest neighbors classifier
    • Computing similarity scores
    • Finding similar users using collaborative filtering
    • Building a movie recommendation system
    • Summary
  • Chapter 9: Logic Programming
    • What is logic programming?
    • Understanding the building blocks of logic programming
    • Solving problems using logic programming
    • Installing Python packages
    • Matching mathematical expressions
    • Validating primes
    • Parsing a family tree
    • Analyzing geography
    • Building a puzzle solver
    • Summary
  • Chapter 10: Heuristic Search Techniques
    • Is heuristic search artificial intelligence?
    • What is heuristic search?
      • Uninformed versus informed search
    • Constraint satisfaction problems
    • Local search techniques
      • Simulated annealing
    • Constructing a string using greedy search
    • Solving a problem with constraints
    • Solving the region-coloring problem
    • Building an 8-puzzle solver
    • Building a maze solver
    • Summary
  • Chapter 11: Genetic Algorithms and Genetic Programming
    • The evolutionists tribe
    • Understanding evolutionary and genetic algorithms
    • Fundamental concepts in genetic algorithms
    • Generating a bit pattern with predefined parameters
    • Visualizing the evolution
    • Solving the symbol regression problem
    • Building an intelligent robot controller
    • Genetic programming use cases
    • Summary
    • References
  • Chapter 12: Artificial Intelligence on the Cloud
    • Why are companies migrating to the cloud?
    • The top cloud providers
    • Amazon Web Services (AWS)
      • Amazon SageMaker
      • Alexa, Lex, and Polly – conversational gents
      • Amazon Comprehend – natural language processing
      • Amazon Rekognition – image and video
      • Amazon Translate
      • Amazon machine learning
      • Amazon Transcribe – transcription
      • Amazon Textract – document analysis
    • Microsoft Azure
      • Microsoft Azure Machine Learning Studio
      • Azure Machine Learning Service
      • Azure Cognitive Services
    • Google Cloud Platform (GCP)
      • AI Hub
      • Google Cloud AI Building Blocks
    • Summary
  • Chapter 13: Building Games with Artificial Intelligence
    • Using search algorithms in games
    • Combinatorial search
      • The Minimax algorithm
      • Alpha-Beta pruning
      • The Negamax algorithm
    • Installing the easyAI library
    • Building a bot to play Last Coin Standing
    • Building a bot to play Tic-Tac-Toe
    • Building two bots to play Connect Four™ against each other
    • Building two bots to play Hexapawn against each other
    • Summary
  • Chapter 14: Building a Speech Recognizer
    • Working with speech signals
    • Visualizing audio signals
    • Transforming audio signals to the frequency domain
    • Generating audio signals
    • Synthesizing tones to generate music
    • Extracting speech features
    • Recognizing spoken words
    • Summary
  • Chapter 15: Natural Language Processing
    • Introduction and installation of packages
    • Tokenizing text data
    • Converting words to their base forms using stemming
    • Converting words to their base forms using lemmatization
    • Dividing text data into chunks
    • Extracting the frequency of terms using the Bag of Words model
    • Building a category predictor
    • Constructing a gender identifier
    • Building a sentiment analyzer
    • Topic modeling using Latent Dirichlet Allocation
    • Summary
  • Chapter 16: Chatbots
    • The future of chatbots
    • Chatbots today
    • Chatbot concepts
    • A well-architected chatbot
    • Chatbot platforms
    • Creating a chatbot using DialogFlow
      • DialogFlow setup
      • Integrating a chatbot into a website using a widget
      • Integrating a chatbot into a website using Python
      • How to set up a webhook in DialogFlow
      • Enabling webhooks for intents
      • Setting up training phrases for an intent
      • Setting up parameters and actions for an intent
      • Building fulfillment responses from a webhook
      • Checking responses from a webhook
    • Summary
  • Chapter 17: Sequential Data and Time Series Analysis
    • Understanding sequential data
    • Handling time series data with Pandas
    • Slicing time series data
    • Operating on time series data
    • Extracting statistics from time series data
    • Generating data using Hidden Markov Models
    • Identifying alphabet sequences with Conditional Random Fields
    • Stock market analysis
    • Summary
  • Chapter 18: Image Recognition
    • Importance of image recognition
    • OpenCV
    • Frame differencing
    • Tracking objects using color spaces
    • Object tracking using background subtraction
    • Building an interactive object tracker using the CAMShift algorithm
    • Optical flow-based tracking
    • Face detection and tracking
      • Using Haar cascades for object detection
      • Using integral images for feature extraction
    • Eye detection and tracking
    • Summary
  • Chapter 19: Neural Networks
    • Introduction to neural networks
      • Building a neural network
      • Training a neural network
    • Building a Perceptron-based classifier
    • Constructing a single-layer neural network
    • Constructing a multi-layer neural network
    • Building a vector quantizer
    • Analyzing sequential data using recurrent neural networks
    • Visualizing characters in an optical character recognition database
    • Building an optical character recognition engine
    • Summary
  • Chapter 20: Deep Learning with Convolutional Neural Networks
    • The basics of Convolutional Neural Networks
    • Architecture of CNNs
      • CNNs vs. perceptron neural networks
    • Types of layers in a CNN
    • Building a perceptron-based linear regressor
    • Building an image classifier using a single-layer neural network
    • Building an image classifier using a Convolutional Neural Network
    • Summary
    • Reference
  • Chapter 21: Recurrent Neural Networks and Other Deep Learning Models
    • The basics of Recurrent Neural Networks
      • Step function
      • Sigmoid function
      • Tanh function
      • ReLU function
    • Architecture of RNNs
    • A aanguage modeling use case
    • Training an RNN
    • Summary
  • Chapter 22: Creating Intelligent Agents with Reinforcement Learning
    • Understanding what it means to learn
    • Reinforcement learning versus supervised learning
    • Real-world examples of reinforcement learning
    • Building blocks of reinforcement learning
    • Creating an environment
    • Building a learning agent
    • Summary
  • Chapter 23: Artificial Intelligence and Big Data
    • Big data basics
      • Crawling
      • Indexing
      • Ranking
      • Worldwide datacenters
      • Distributed lookups
      • Custom software
    • The three V's of big data
      • Volume
      • Velocity
      • Variety
    • Big data and machine learning
      • Apache Hadoop
        • MapReduce
        • Apache Hive
      • Apache Spark
        • Resilient distributed datasets
        • DataFrames
        • SparkSQL
      • Apache Impala
    • NoSQL Databases
      • Types of NoSQL databases
      • Apache Cassandra
      • MongoDB
      • Redis
      • Neo4j
    • Summary
  • Other Books You May Enjoy
  • Index
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