Artificial Intelligence and Machine Learning Fundamentals
Develop real-world applications powered by the latest AI advances
Zsolt Nagy
- 330 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Artificial Intelligence and Machine Learning Fundamentals
Develop real-world applications powered by the latest AI advances
Zsolt Nagy
About This Book
Create AI applications in Python and lay the foundations for your career in data science
Key Features
- Practical examples that explain key machine learning algorithms
- Explore neural networks in detail with interesting examples
- Master core AI concepts with engaging activities
Book Description
Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples.
As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law.
By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills!
What you will learn
- Understand the importance, principles, and fields of AI
- Implement basic artificial intelligence concepts with Python
- Apply regression and classification concepts to real-world problems
- Perform predictive analysis using decision trees and random forests
- Carry out clustering using the k-means and mean shift algorithms
- Understand the fundamentals of deep learning via practical examples
Who this book is for
Artificial Intelligence and Machine Learning Fundamentals is for software developers and data scientists who want to enrich their projects with machine learning. You do not need any prior experience in AI. However, it's recommended that you have knowledge of high school-level mathematics and at least one programming language (preferably Python).
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Appendix
About
Chapter 1: Principles of AI
Activity 1: Generating All Possible Sequences of Steps in the tic-tac-toe Game
- Create a function that maps the all_moves_from_board function on each element of a list of boards. This way, we will have all of the nodes of a decision tree in each depth:def all_moves_from_board(board, sign):move_list = []for i, v in enumerate(board):if v == EMPTY_SIGN:move_list.append(board[:i] + sign + board[i+1:])return move_list
- The decision tree starts with [ EMPTY_SIGN * 9 ], and expands after each move:all_moves_from_board_list( [ EMPTY_SIGN * 9 ], AI_SIGN )
- The output is as follows:['X........','.X.......','..X......','...X.....','....X....','.....X...','......X..','.......X.','........X']['XO.......','X.O......','X..O.....','X...O....','X....O...','X.....O..','X......O.',....'......OX.','.......XO','O.......X','.O......X','..O.....X','...O....X','....O...X','.....O..X','......O.X','.......OX']
- Let's create a filter_wins function that takes the ended games out from the list of moves and appends them in an array containing the board states won by the AI player and the opponent player:def filter_wins(move_list, ai_wins, opponent_wins):for board in move_list:won_by = game_won_by(board)if won_by == AI_SIGN:ai_wins.append(board)move_list.remove(board)elif won_by == OPPONENT_SIGN:opponent_wins.append(board)move_list.remove(board)
- In this function, the three lists can be considered as reference types. This means that the function does not return a value, instead but it manipulating these three lists without returning them.
- Let's finish this section. Then with a count_possibilities function that prints the number of decision tree leaves that ended with a draw, won by the first player, and won by the second player:def count_possibilities():board = EMPTY_SIGN * 9move_list = [board]ai_wins = []opponent_wins = []for i in range(9):print('step ' + str(i) + '. Moves: ' + \ str(len(move_list)))sign = AI_SIGN if i % 2 == 0 else OPPONENT_SIGNmove_list = all_moves_from_board_list(move_list, sign)filter_wins(move_list, ai_wins, opponent_wins)print('First player wins: ' + str(len(ai_wins)))print('Second player wins: ' + str(len(opponent_wins)))print('Draw', str(len(move_list)))print('Total', str(len(ai_wins) + len(opponent_wins) + \ len(move_list)))
- We have up to 9 steps in each state. In the 0th, 2nd, 4th, 6th, and 8th iteration, the AI player moves. In all other iterations, the opponent moves. We create all possible moves in all steps and take out the ended games from the move list.
- Then execute the number of possibilities to experience the combinatoric explosion.count_possibilities()
- The output is as follows:step 0. Moves: 1step 1. Moves: 9step 2. Moves: 72step 3. Moves: 504step 4. Moves: 3024step 5. Moves: 13680step 6. Moves: 49...