Artificial intelligence and machine learning fundamentals: develop real-world applications powered by the latest AI advances
Intro -- Preface -- Principles of Artificial Intelligence -- Introduction -- How does AI Solve Real World Problems? -- Diversity of Disciplines -- Fields and Applications of Artificial Intelligence -- Simulating Intelligence - The Turing Test -- AI Tools and Learning Models -- Classification and Pre...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt
December 2018
|
| Schlagworte: | |
| Zusammenfassung: | Intro -- Preface -- Principles of Artificial Intelligence -- Introduction -- How does AI Solve Real World Problems? -- Diversity of Disciplines -- Fields and Applications of Artificial Intelligence -- Simulating Intelligence - The Turing Test -- AI Tools and Learning Models -- Classification and Prediction -- Learning Models -- The Role of Python in Artificial Intelligence -- Why is Python Dominant in Machine Learning, Data Science, and AI? -- Anaconda in Python -- Python Libraries for Artificial Intelligence -- A Brief Introduction to the NumPy Library -- Exercise 1: Matrix Operations Using NumPy -- Python for Game AI -- Intelligent Agents in Games -- Breadth First Search and Depth First Search -- Exploring the State Space of a Game -- Exercise 2: Estimating the Number of Possible States in Tic-Tac-Toe Game -- Exercise 3: Creating an AI Randomly -- Activity 1: Generating All Possible Sequences of Steps in a Tic-Tac-Toe Game -- Summary -- AI with Search Techniques and Games -- Introduction -- Exercise 4: Teaching the Agent to Win -- Activity 2: Teaching the Agent to Realize Situations When It Defends Against Losses -- Activity 3: Fixing the First and Second Moves of the AI to Make it Invincible -- Heuristics -- Uninformed and Informed Search -- Creating Heuristics -- Admissible and Non-Admissible Heuristics -- Heuristic Evaluation -- Exercise 5: Tic-Tac-Toe Static Evaluation with a Heuristic Function -- Using Heuristics for an Informed Search -- Types of Heuristics -- Pathfinding with the A* Algorithm -- Exercise 6: Finding the Shortest Path to Reach a Goal -- Exercise 7: Finding the Shortest Path Using BFS -- Introducing the A* Algorithm -- A* Search in Practice Using the simpleai Library -- Game AI with the Minmax Algorithm and Alpha-Beta Pruning -- Search Algorithms for Turn-Based Multiplayer Games -- The Minmax Algorithm Optimizing the Minmax Algorithm with Alpha-Beta Pruning -- DRYing up the Minmax Algorithm - The NegaMax Algorithm -- Using the EasyAI Library -- Activity 4: Connect Four -- Summary -- Regression -- Introduction -- Linear Regression with One Variable -- What Is Regression? -- Features and Labels -- Feature Scaling -- Cross-Validation with Training and Test Data -- Fitting a Model on Data with scikit-learn -- Linear Regression Using NumPy Arrays -- Fitting a Model Using NumPy Polyfit -- Predicting Values with Linear Regression -- Activity 5: Predicting Population -- Linear Regression with Multiple Variables -- Multiple Linear Regression -- The Process of Linear Regression -- Importing Data from Data Sources -- Loading Stock Prices with Yahoo Finance -- Loading Files with pandas -- Loading Stock Prices with Quandl -- Exercise 8: Using Quandl to Load Stock Prices -- Preparing Data for Prediction -- Performing and Validating Linear Regression -- Predicting the Future -- Polynomial and Support Vector Regression -- Polynomial Regression with One Variable -- Exercise 9: 1st, 2nd, and 3rd Degree Polynomial Regression -- Polynomial Regression with Multiple Variables -- Support Vector Regression -- Support Vector Machines with a 3 Degree Polynomial Kernel -- Activity 6: Stock Price Prediction with Quadratic and Cubic Linear Polynomial Regression with Multiple Variables -- Summary -- Classification -- Introduction -- The Fundamentals of Classification -- Exercise 10: Loading Datasets -- Data Preprocessing -- Exercise 11: Pre-Processing Data -- Minmax Scaling of the Goal Column -- Identifying Features and Labels -- Cross-Validation with scikit-learn -- Activity 7: Preparing Credit Data for Classification -- The k-nearest neighbor Classifier -- Introducing the K-Nearest Neighbor Algorithm -- Distance Functions Exercise 12: Illustrating the K-nearest Neighbor Classifier Algorithm -- Exercise 13: k-nearest Neighbor Classification in scikit-learn -- Exercise 14: Prediction with the k-nearest neighbors classifier -- Parameterization of the k-nearest neighbor Classifier in scikit-learn -- Activity 8: Increasing the Accuracy of Credit Scoring -- Classification with Support Vector Machines -- What are Support Vector Machine Classifiers? -- Understanding Support Vector Machines -- Support Vector Machines in scikit-learn -- Parameters of the scikit-learn SVM -- Activity 9: Support Vector Machine Optimization in scikit-learn -- Summary -- Using Trees for Predictive Analysis -- Introduction to Decision Trees -- Entropy -- Exercise 15: Calculating the Entropy -- Information Gain -- Gini Impurity -- Exit Condition -- Building Decision Tree Classifiers using scikit-learn -- Evaluating the Performance of Classifiers -- Exercise 16: Precision and Recall -- Exercise 17: Calculating the F1 Score -- Confusion Matrix -- Exercise 18: Confusion Matrix -- Activity 10: Car Data Classification -- Random Forest Classifier -- Constructing a Random Forest -- Random Forest Classification Using scikit-learn -- Parameterization of the random forest classifier -- Feature Importance -- Extremely Randomized Trees -- Activity 11: Random Forest Classification for Your Car Rental Company -- Summary -- Clustering -- Introduction to Clustering -- Defining the Clustering Problem -- Clustering Approaches -- Clustering Algorithms Supported by scikit-learn -- The k-means Algorithm -- Exercise 19: k-means in scikit-learn -- Parameterization of the k-means Algorithm in scikit-learn -- Exercise 20: Retrieving the Center Points and the Labels -- k-means Clustering of Sales Data -- Activity 12: k-means Clustering of Sales Data -- Mean Shift Algorithm -- Exercise 21: Illustrating Mean Shift in 2D |
| Beschreibung: | vi, 305 Seiten Illustrationen |
| ISBN: | 9781789801651 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV045567382 | ||
| 003 | DE-604 | ||
| 005 | 20190626 | ||
| 007 | t | ||
| 008 | 190424s2018 a||| |||| 00||| eng d | ||
| 020 | |a 9781789801651 |9 978-1-78980-165-1 | ||
| 035 | |a (OCoLC)1104932833 | ||
| 035 | |a (DE-599)BVBBV045567382 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-91G | ||
| 084 | |a DAT 700f |2 stub | ||
| 084 | |a DAT 708f |2 stub | ||
| 100 | 1 | |a Nagy, Zsolt |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Artificial intelligence and machine learning fundamentals |b develop real-world applications powered by the latest AI advances |c Zsolt Nagy |
| 264 | 1 | |a Birmingham |b Packt |c December 2018 | |
| 300 | |a vi, 305 Seiten |b Illustrationen | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 520 | 3 | |a Intro -- Preface -- Principles of Artificial Intelligence -- Introduction -- How does AI Solve Real World Problems? -- Diversity of Disciplines -- Fields and Applications of Artificial Intelligence -- Simulating Intelligence - The Turing Test -- AI Tools and Learning Models -- Classification and Prediction -- Learning Models -- The Role of Python in Artificial Intelligence -- Why is Python Dominant in Machine Learning, Data Science, and AI? -- Anaconda in Python -- Python Libraries for Artificial Intelligence -- A Brief Introduction to the NumPy Library -- Exercise 1: Matrix Operations Using NumPy -- Python for Game AI -- Intelligent Agents in Games -- Breadth First Search and Depth First Search -- Exploring the State Space of a Game -- Exercise 2: Estimating the Number of Possible States in Tic-Tac-Toe Game -- Exercise 3: Creating an AI Randomly -- Activity 1: Generating All Possible Sequences of Steps in a Tic-Tac-Toe Game -- Summary -- AI with Search Techniques and Games -- Introduction -- Exercise 4: Teaching the Agent to Win -- Activity 2: Teaching the Agent to Realize Situations When It Defends Against Losses -- Activity 3: Fixing the First and Second Moves of the AI to Make it Invincible -- Heuristics -- Uninformed and Informed Search -- Creating Heuristics -- Admissible and Non-Admissible Heuristics -- Heuristic Evaluation -- Exercise 5: Tic-Tac-Toe Static Evaluation with a Heuristic Function -- Using Heuristics for an Informed Search -- Types of Heuristics -- Pathfinding with the A* Algorithm -- Exercise 6: Finding the Shortest Path to Reach a Goal -- Exercise 7: Finding the Shortest Path Using BFS -- Introducing the A* Algorithm -- A* Search in Practice Using the simpleai Library -- Game AI with the Minmax Algorithm and Alpha-Beta Pruning -- Search Algorithms for Turn-Based Multiplayer Games -- The Minmax Algorithm | |
| 520 | 3 | |a Optimizing the Minmax Algorithm with Alpha-Beta Pruning -- DRYing up the Minmax Algorithm - The NegaMax Algorithm -- Using the EasyAI Library -- Activity 4: Connect Four -- Summary -- Regression -- Introduction -- Linear Regression with One Variable -- What Is Regression? -- Features and Labels -- Feature Scaling -- Cross-Validation with Training and Test Data -- Fitting a Model on Data with scikit-learn -- Linear Regression Using NumPy Arrays -- Fitting a Model Using NumPy Polyfit -- Predicting Values with Linear Regression -- Activity 5: Predicting Population -- Linear Regression with Multiple Variables -- Multiple Linear Regression -- The Process of Linear Regression -- Importing Data from Data Sources -- Loading Stock Prices with Yahoo Finance -- Loading Files with pandas -- Loading Stock Prices with Quandl -- Exercise 8: Using Quandl to Load Stock Prices -- Preparing Data for Prediction -- Performing and Validating Linear Regression -- Predicting the Future -- Polynomial and Support Vector Regression -- Polynomial Regression with One Variable -- Exercise 9: 1st, 2nd, and 3rd Degree Polynomial Regression -- Polynomial Regression with Multiple Variables -- Support Vector Regression -- Support Vector Machines with a 3 Degree Polynomial Kernel -- Activity 6: Stock Price Prediction with Quadratic and Cubic Linear Polynomial Regression with Multiple Variables -- Summary -- Classification -- Introduction -- The Fundamentals of Classification -- Exercise 10: Loading Datasets -- Data Preprocessing -- Exercise 11: Pre-Processing Data -- Minmax Scaling of the Goal Column -- Identifying Features and Labels -- Cross-Validation with scikit-learn -- Activity 7: Preparing Credit Data for Classification -- The k-nearest neighbor Classifier -- Introducing the K-Nearest Neighbor Algorithm -- Distance Functions | |
| 520 | 3 | |a Exercise 12: Illustrating the K-nearest Neighbor Classifier Algorithm -- Exercise 13: k-nearest Neighbor Classification in scikit-learn -- Exercise 14: Prediction with the k-nearest neighbors classifier -- Parameterization of the k-nearest neighbor Classifier in scikit-learn -- Activity 8: Increasing the Accuracy of Credit Scoring -- Classification with Support Vector Machines -- What are Support Vector Machine Classifiers? -- Understanding Support Vector Machines -- Support Vector Machines in scikit-learn -- Parameters of the scikit-learn SVM -- Activity 9: Support Vector Machine Optimization in scikit-learn -- Summary -- Using Trees for Predictive Analysis -- Introduction to Decision Trees -- Entropy -- Exercise 15: Calculating the Entropy -- Information Gain -- Gini Impurity -- Exit Condition -- Building Decision Tree Classifiers using scikit-learn -- Evaluating the Performance of Classifiers -- Exercise 16: Precision and Recall -- Exercise 17: Calculating the F1 Score -- Confusion Matrix -- Exercise 18: Confusion Matrix -- Activity 10: Car Data Classification -- Random Forest Classifier -- Constructing a Random Forest -- Random Forest Classification Using scikit-learn -- Parameterization of the random forest classifier -- Feature Importance -- Extremely Randomized Trees -- Activity 11: Random Forest Classification for Your Car Rental Company -- Summary -- Clustering -- Introduction to Clustering -- Defining the Clustering Problem -- Clustering Approaches -- Clustering Algorithms Supported by scikit-learn -- The k-means Algorithm -- Exercise 19: k-means in scikit-learn -- Parameterization of the k-means Algorithm in scikit-learn -- Exercise 20: Retrieving the Center Points and the Labels -- k-means Clustering of Sales Data -- Activity 12: k-means Clustering of Sales Data -- Mean Shift Algorithm -- Exercise 21: Illustrating Mean Shift in 2D | |
| 650 | 0 | 7 | |a Künstliche Intelligenz |0 (DE-588)4033447-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Künstliche Intelligenz |0 (DE-588)4033447-8 |D s |
| 689 | 0 | 1 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | |5 DE-604 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-1-78980-920-6 |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-030951023 | ||
Datensatz im Suchindex
| _version_ | 1804179554436120576 |
|---|---|
| any_adam_object | |
| author | Nagy, Zsolt |
| author_facet | Nagy, Zsolt |
| author_role | aut |
| author_sort | Nagy, Zsolt |
| author_variant | z n zn |
| building | Verbundindex |
| bvnumber | BV045567382 |
| classification_tum | DAT 700f DAT 708f |
| ctrlnum | (OCoLC)1104932833 (DE-599)BVBBV045567382 |
| discipline | Informatik |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>06827nam a2200385 c 4500</leader><controlfield tag="001">BV045567382</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20190626 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">190424s2018 a||| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781789801651</subfield><subfield code="9">978-1-78980-165-1</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1104932833</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV045567382</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91G</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">DAT 700f</subfield><subfield code="2">stub</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">DAT 708f</subfield><subfield code="2">stub</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Nagy, Zsolt</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Artificial intelligence and machine learning fundamentals</subfield><subfield code="b">develop real-world applications powered by the latest AI advances</subfield><subfield code="c">Zsolt Nagy</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Birmingham</subfield><subfield code="b">Packt</subfield><subfield code="c">December 2018</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">vi, 305 Seiten</subfield><subfield code="b">Illustrationen</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">Intro -- Preface -- Principles of Artificial Intelligence -- Introduction -- How does AI Solve Real World Problems? -- Diversity of Disciplines -- Fields and Applications of Artificial Intelligence -- Simulating Intelligence - The Turing Test -- AI Tools and Learning Models -- Classification and Prediction -- Learning Models -- The Role of Python in Artificial Intelligence -- Why is Python Dominant in Machine Learning, Data Science, and AI? -- Anaconda in Python -- Python Libraries for Artificial Intelligence -- A Brief Introduction to the NumPy Library -- Exercise 1: Matrix Operations Using NumPy -- Python for Game AI -- Intelligent Agents in Games -- Breadth First Search and Depth First Search -- Exploring the State Space of a Game -- Exercise 2: Estimating the Number of Possible States in Tic-Tac-Toe Game -- Exercise 3: Creating an AI Randomly -- Activity 1: Generating All Possible Sequences of Steps in a Tic-Tac-Toe Game -- Summary -- AI with Search Techniques and Games -- Introduction -- Exercise 4: Teaching the Agent to Win -- Activity 2: Teaching the Agent to Realize Situations When It Defends Against Losses -- Activity 3: Fixing the First and Second Moves of the AI to Make it Invincible -- Heuristics -- Uninformed and Informed Search -- Creating Heuristics -- Admissible and Non-Admissible Heuristics -- Heuristic Evaluation -- Exercise 5: Tic-Tac-Toe Static Evaluation with a Heuristic Function -- Using Heuristics for an Informed Search -- Types of Heuristics -- Pathfinding with the A* Algorithm -- Exercise 6: Finding the Shortest Path to Reach a Goal -- Exercise 7: Finding the Shortest Path Using BFS -- Introducing the A* Algorithm -- A* Search in Practice Using the simpleai Library -- Game AI with the Minmax Algorithm and Alpha-Beta Pruning -- Search Algorithms for Turn-Based Multiplayer Games -- The Minmax Algorithm</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">Optimizing the Minmax Algorithm with Alpha-Beta Pruning -- DRYing up the Minmax Algorithm - The NegaMax Algorithm -- Using the EasyAI Library -- Activity 4: Connect Four -- Summary -- Regression -- Introduction -- Linear Regression with One Variable -- What Is Regression? -- Features and Labels -- Feature Scaling -- Cross-Validation with Training and Test Data -- Fitting a Model on Data with scikit-learn -- Linear Regression Using NumPy Arrays -- Fitting a Model Using NumPy Polyfit -- Predicting Values with Linear Regression -- Activity 5: Predicting Population -- Linear Regression with Multiple Variables -- Multiple Linear Regression -- The Process of Linear Regression -- Importing Data from Data Sources -- Loading Stock Prices with Yahoo Finance -- Loading Files with pandas -- Loading Stock Prices with Quandl -- Exercise 8: Using Quandl to Load Stock Prices -- Preparing Data for Prediction -- Performing and Validating Linear Regression -- Predicting the Future -- Polynomial and Support Vector Regression -- Polynomial Regression with One Variable -- Exercise 9: 1st, 2nd, and 3rd Degree Polynomial Regression -- Polynomial Regression with Multiple Variables -- Support Vector Regression -- Support Vector Machines with a 3 Degree Polynomial Kernel -- Activity 6: Stock Price Prediction with Quadratic and Cubic Linear Polynomial Regression with Multiple Variables -- Summary -- Classification -- Introduction -- The Fundamentals of Classification -- Exercise 10: Loading Datasets -- Data Preprocessing -- Exercise 11: Pre-Processing Data -- Minmax Scaling of the Goal Column -- Identifying Features and Labels -- Cross-Validation with scikit-learn -- Activity 7: Preparing Credit Data for Classification -- The k-nearest neighbor Classifier -- Introducing the K-Nearest Neighbor Algorithm -- Distance Functions</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">Exercise 12: Illustrating the K-nearest Neighbor Classifier Algorithm -- Exercise 13: k-nearest Neighbor Classification in scikit-learn -- Exercise 14: Prediction with the k-nearest neighbors classifier -- Parameterization of the k-nearest neighbor Classifier in scikit-learn -- Activity 8: Increasing the Accuracy of Credit Scoring -- Classification with Support Vector Machines -- What are Support Vector Machine Classifiers? -- Understanding Support Vector Machines -- Support Vector Machines in scikit-learn -- Parameters of the scikit-learn SVM -- Activity 9: Support Vector Machine Optimization in scikit-learn -- Summary -- Using Trees for Predictive Analysis -- Introduction to Decision Trees -- Entropy -- Exercise 15: Calculating the Entropy -- Information Gain -- Gini Impurity -- Exit Condition -- Building Decision Tree Classifiers using scikit-learn -- Evaluating the Performance of Classifiers -- Exercise 16: Precision and Recall -- Exercise 17: Calculating the F1 Score -- Confusion Matrix -- Exercise 18: Confusion Matrix -- Activity 10: Car Data Classification -- Random Forest Classifier -- Constructing a Random Forest -- Random Forest Classification Using scikit-learn -- Parameterization of the random forest classifier -- Feature Importance -- Extremely Randomized Trees -- Activity 11: Random Forest Classification for Your Car Rental Company -- Summary -- Clustering -- Introduction to Clustering -- Defining the Clustering Problem -- Clustering Approaches -- Clustering Algorithms Supported by scikit-learn -- The k-means Algorithm -- Exercise 19: k-means in scikit-learn -- Parameterization of the k-means Algorithm in scikit-learn -- Exercise 20: Retrieving the Center Points and the Labels -- k-means Clustering of Sales Data -- Activity 12: k-means Clustering of Sales Data -- Mean Shift Algorithm -- Exercise 21: Illustrating Mean Shift in 2D</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Künstliche Intelligenz</subfield><subfield code="0">(DE-588)4033447-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Künstliche Intelligenz</subfield><subfield code="0">(DE-588)4033447-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe</subfield><subfield code="z">978-1-78980-920-6</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-030951023</subfield></datafield></record></collection> |
| id | DE-604.BV045567382 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T08:21:43Z |
| institution | BVB |
| isbn | 9781789801651 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030951023 |
| oclc_num | 1104932833 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM |
| owner_facet | DE-91G DE-BY-TUM |
| physical | vi, 305 Seiten Illustrationen |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Packt |
| record_format | marc |
| spelling | Nagy, Zsolt Verfasser aut Artificial intelligence and machine learning fundamentals develop real-world applications powered by the latest AI advances Zsolt Nagy Birmingham Packt December 2018 vi, 305 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Intro -- Preface -- Principles of Artificial Intelligence -- Introduction -- How does AI Solve Real World Problems? -- Diversity of Disciplines -- Fields and Applications of Artificial Intelligence -- Simulating Intelligence - The Turing Test -- AI Tools and Learning Models -- Classification and Prediction -- Learning Models -- The Role of Python in Artificial Intelligence -- Why is Python Dominant in Machine Learning, Data Science, and AI? -- Anaconda in Python -- Python Libraries for Artificial Intelligence -- A Brief Introduction to the NumPy Library -- Exercise 1: Matrix Operations Using NumPy -- Python for Game AI -- Intelligent Agents in Games -- Breadth First Search and Depth First Search -- Exploring the State Space of a Game -- Exercise 2: Estimating the Number of Possible States in Tic-Tac-Toe Game -- Exercise 3: Creating an AI Randomly -- Activity 1: Generating All Possible Sequences of Steps in a Tic-Tac-Toe Game -- Summary -- AI with Search Techniques and Games -- Introduction -- Exercise 4: Teaching the Agent to Win -- Activity 2: Teaching the Agent to Realize Situations When It Defends Against Losses -- Activity 3: Fixing the First and Second Moves of the AI to Make it Invincible -- Heuristics -- Uninformed and Informed Search -- Creating Heuristics -- Admissible and Non-Admissible Heuristics -- Heuristic Evaluation -- Exercise 5: Tic-Tac-Toe Static Evaluation with a Heuristic Function -- Using Heuristics for an Informed Search -- Types of Heuristics -- Pathfinding with the A* Algorithm -- Exercise 6: Finding the Shortest Path to Reach a Goal -- Exercise 7: Finding the Shortest Path Using BFS -- Introducing the A* Algorithm -- A* Search in Practice Using the simpleai Library -- Game AI with the Minmax Algorithm and Alpha-Beta Pruning -- Search Algorithms for Turn-Based Multiplayer Games -- The Minmax Algorithm Optimizing the Minmax Algorithm with Alpha-Beta Pruning -- DRYing up the Minmax Algorithm - The NegaMax Algorithm -- Using the EasyAI Library -- Activity 4: Connect Four -- Summary -- Regression -- Introduction -- Linear Regression with One Variable -- What Is Regression? -- Features and Labels -- Feature Scaling -- Cross-Validation with Training and Test Data -- Fitting a Model on Data with scikit-learn -- Linear Regression Using NumPy Arrays -- Fitting a Model Using NumPy Polyfit -- Predicting Values with Linear Regression -- Activity 5: Predicting Population -- Linear Regression with Multiple Variables -- Multiple Linear Regression -- The Process of Linear Regression -- Importing Data from Data Sources -- Loading Stock Prices with Yahoo Finance -- Loading Files with pandas -- Loading Stock Prices with Quandl -- Exercise 8: Using Quandl to Load Stock Prices -- Preparing Data for Prediction -- Performing and Validating Linear Regression -- Predicting the Future -- Polynomial and Support Vector Regression -- Polynomial Regression with One Variable -- Exercise 9: 1st, 2nd, and 3rd Degree Polynomial Regression -- Polynomial Regression with Multiple Variables -- Support Vector Regression -- Support Vector Machines with a 3 Degree Polynomial Kernel -- Activity 6: Stock Price Prediction with Quadratic and Cubic Linear Polynomial Regression with Multiple Variables -- Summary -- Classification -- Introduction -- The Fundamentals of Classification -- Exercise 10: Loading Datasets -- Data Preprocessing -- Exercise 11: Pre-Processing Data -- Minmax Scaling of the Goal Column -- Identifying Features and Labels -- Cross-Validation with scikit-learn -- Activity 7: Preparing Credit Data for Classification -- The k-nearest neighbor Classifier -- Introducing the K-Nearest Neighbor Algorithm -- Distance Functions Exercise 12: Illustrating the K-nearest Neighbor Classifier Algorithm -- Exercise 13: k-nearest Neighbor Classification in scikit-learn -- Exercise 14: Prediction with the k-nearest neighbors classifier -- Parameterization of the k-nearest neighbor Classifier in scikit-learn -- Activity 8: Increasing the Accuracy of Credit Scoring -- Classification with Support Vector Machines -- What are Support Vector Machine Classifiers? -- Understanding Support Vector Machines -- Support Vector Machines in scikit-learn -- Parameters of the scikit-learn SVM -- Activity 9: Support Vector Machine Optimization in scikit-learn -- Summary -- Using Trees for Predictive Analysis -- Introduction to Decision Trees -- Entropy -- Exercise 15: Calculating the Entropy -- Information Gain -- Gini Impurity -- Exit Condition -- Building Decision Tree Classifiers using scikit-learn -- Evaluating the Performance of Classifiers -- Exercise 16: Precision and Recall -- Exercise 17: Calculating the F1 Score -- Confusion Matrix -- Exercise 18: Confusion Matrix -- Activity 10: Car Data Classification -- Random Forest Classifier -- Constructing a Random Forest -- Random Forest Classification Using scikit-learn -- Parameterization of the random forest classifier -- Feature Importance -- Extremely Randomized Trees -- Activity 11: Random Forest Classification for Your Car Rental Company -- Summary -- Clustering -- Introduction to Clustering -- Defining the Clustering Problem -- Clustering Approaches -- Clustering Algorithms Supported by scikit-learn -- The k-means Algorithm -- Exercise 19: k-means in scikit-learn -- Parameterization of the k-means Algorithm in scikit-learn -- Exercise 20: Retrieving the Center Points and the Labels -- k-means Clustering of Sales Data -- Activity 12: k-means Clustering of Sales Data -- Mean Shift Algorithm -- Exercise 21: Illustrating Mean Shift in 2D Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Erscheint auch als Online-Ausgabe 978-1-78980-920-6 |
| spellingShingle | Nagy, Zsolt Artificial intelligence and machine learning fundamentals develop real-world applications powered by the latest AI advances Künstliche Intelligenz (DE-588)4033447-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4193754-5 |
| title | Artificial intelligence and machine learning fundamentals develop real-world applications powered by the latest AI advances |
| title_auth | Artificial intelligence and machine learning fundamentals develop real-world applications powered by the latest AI advances |
| title_exact_search | Artificial intelligence and machine learning fundamentals develop real-world applications powered by the latest AI advances |
| title_full | Artificial intelligence and machine learning fundamentals develop real-world applications powered by the latest AI advances Zsolt Nagy |
| title_fullStr | Artificial intelligence and machine learning fundamentals develop real-world applications powered by the latest AI advances Zsolt Nagy |
| title_full_unstemmed | Artificial intelligence and machine learning fundamentals develop real-world applications powered by the latest AI advances Zsolt Nagy |
| title_short | Artificial intelligence and machine learning fundamentals |
| title_sort | artificial intelligence and machine learning fundamentals develop real world applications powered by the latest ai advances |
| title_sub | develop real-world applications powered by the latest AI advances |
| topic | Künstliche Intelligenz (DE-588)4033447-8 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Künstliche Intelligenz Maschinelles Lernen |
| work_keys_str_mv | AT nagyzsolt artificialintelligenceandmachinelearningfundamentalsdeveloprealworldapplicationspoweredbythelatestaiadvances |