![After launching your IPython console, you can simply import NumPy as
follows:
import numpy as np
we can define vectors and matrices
np.array([1,3,5,7])
The output is as follows:
array([1, 3, 5, 7])
We can declare a matrix using the following syntax:
A = np.mat([[1,2],[3,3]])
The output is as follows:
matrix([[1, 2],[3, 3]])
The array method creates an array data structure, while mat creates a matrix.](https://image.slidesharecdn.com/unit1aiml-240312095402-5ac596e8/85/INTRODUCTION-TO-ARTIFICIAL-INTELLIGENCE-AND-MACHINE-LEARNING-52-320.jpg)
![Addition in matrices:
A + A
The output is as follows:
matrix([[2, 4], [6, 6]])
Subtraction in matrices:
A - A
The output is as follows:
matrix([[0, 0],[0, 0]])
Multiplication in matrices
A * A
The output is as follows:
matrix([[ 7, 8], [12, 15]])
Matrix addition and subtraction works cell by cell.
Matrix multiplication works according to linear algebra rules.](https://image.slidesharecdn.com/unit1aiml-240312095402-5ac596e8/85/INTRODUCTION-TO-ARTIFICIAL-INTELLIGENCE-AND-MACHINE-LEARNING-53-320.jpg)
![• Another frequent matrix operation is the determinant
of the matrix.
• The determinant is a number associated with square
matrices. Calculating the determinant using NumPy is
easy:
np.linalg.det( A )
• The output is -3.0000000000000004.
• the determinant can be calculated as 1*3 – 2*3 = -3.
• We can also transpose a matrix, like so:
np.matrix.transpose(A)
• The output is as follows: matrix([[1, 3],[2, 3]])
• When calculating the transpose of a matrix, we flip its
values over its main diagonal.](https://image.slidesharecdn.com/unit1aiml-240312095402-5ac596e8/85/INTRODUCTION-TO-ARTIFICIAL-INTELLIGENCE-AND-MACHINE-LEARNING-54-320.jpg)
INTRODUCTION TO ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
- 2. Click here to Download e-Book Unit 1: Principles of Artificial Intelligence - Introduction - Solving Real World Problems using AI - 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 - AI and Python - Anaconda in Python - Python Libraries for Artificial Intelligence - Introduction to the NumPy Library.
- 3. AI-ARTIFICIAL INTELLIGENCE • Artificial Intelligence (AI) is a science that's used to construct intelligence using hardware and software solutions. • It is inspired by reverse engineering, for example, in the way that neurons work in the human brain
- 10. Machine Learning • Machine learning is a term that is often confused with artificial intelligence. It originates from the 1950s, and it was first defined by Arthur Lee Samuel in 1959. • Machine learning is a field of study concerned with giving computers the ability to learn without being explicitly programmed. • Machine learning is one way to achieve artificial intelligence. • You can have artificial intelligence without machine learning.
- 11. How does AI Solve Real World Problems? • Artificial intelligence automates human intelligence based on the way human brain processes information. • Whenever we solve a problem or interact with people, we go through a process. Whenever we limit the scope of a problem or interaction, this process can often be modeled and automated.
- 12. AI makes computers appear to think like humans. Sometimes, it feels like AI knows what we need. Just think about the personalized coupons you receive after shopping online. By the end of this book, you will understand that to choose the most successful products, you need to be shown how to maximize your purchases – this is a relatively simple task. However, it is also so efficient, that we often think that computers "know" what we need.
- 13. AI is performed by computers that are executing low-level instructions. Even though a solution may appear to be intelligent, we write code, just like with any other software solutions. Even if we are simulating neurons, simple machine code and computer hardware executes the "thinking" process. Most AI applications have one primary objective. When we interact with an AI application, it seems human-like because it can restrict a problem domain to a primary objective. Therefore, we get a chance to break down complex processes and simulate intelligence with the help of low-level computer instructions.
- 14. AI may stimulate human senses and thinking processes for specialized fields. We must simulate human senses and thoughts, and sometimes trick AI into believing that we are interacting with another human. In special cases, we can even enhance our own senses. Similarly, when we interact with a chatbot, we expect the bot to understand us. We expect the chatbot or even a voice recognition system to provide a computer-human interface that fulfills our expectations. In order to meet these expectations, computers need to emulate the human thought processes.
- 15. Diversity of Disciplines • The AI agent needs to process and sense what is around it in order to drive the car. • Without understanding the physics of moving objects, driving the car in a normal environment would be an almost impossible, not to mention deadly, task. • In order to create a usable AI solution, different disciplines are involved. For example: • Robotics: To move objects in space Algorithm
- 16. • Theory: To construct efficient algorithms • Statistics:To derive useful results, predict the future, and analyze the past • Psychology: To model how the human brain works • Software Engineering: To create maintainable solutions that endure the test of time • Computer Science or Computer Programming: To implement our software Solutions in practice.
- 17. • Mathematics: To perform complex Mathematical operations • Control Theory: To create feedForward and feedback systems • Information Theory: To represent, encode, decode, and compress information • Graph Theory: To model and optimize different points in space and to represent hierarchies • Physics: To model the real world Computer Graphics and Image Processing to display and process images and movies • Computer Graphics and Image Processing: to display and process images and movies
- 18. Fields and Applications of Artificial Intelligence • Simulation of Human Behavior Humans have five basic senses simply divided into visual, auditory, kinesthetic, olfactory, and gustatory. However, for the purposes of understanding how to create intelligent machines, we can separate disciplines as follows: • Listening and speaking • Understanding language • Remembering things • Thinking • Seeing • Moving
- 19. Applications
- 20. 1. AI in Astronomy ● Artificial Intelligence can be very useful to solve complex universe problems. AI technology can be helpful for understanding the universe such as how it works, origin, etc. 2. AI in Healthcare ● In the last, five to ten years, AI becoming more advantageous for the healthcare industry and going to have a significant impact on this industry. ● Healthcare Industries are applying AI to make a better and faster diagnosis than humans. AI can help doctors with diagnoses and can inform when patients are worsening so that medical help can reach to the patient before hospitalization. 3. AI in Gaming ● AI can be used for gaming purpose. The AI machines can play strategy games like chess, where the machine needs to think of a large number of possible places. 4. AI in Finance ● AI and finance industries are the best matches for each other. The finance industry is implementing automation, chatbot, adaptive intelligence, algorithm trading, and machine learning into financial processes.
- 21. 5. AI in Data Security ● The security of data is crucial for every company and cyber-attacks are growing very rapidly in the digital world. AI can be used to make your data more safe and secure. Some examples such as AEG bot, AI2 Platform,are used to determine software bug and cyber-attacks in a better way. 6. AI in Social Media ● Social Media sites such as Facebook, Twitter, and Snapchat contain billions of user profiles, which need to be stored and managed in a very efficient way. AI can organize and manage massive amounts of data. AI can analyze lots of data to identify the latest trends, hashtag, and requirement of different users. 7. AI in Travel & Transport ● AI is becoming highly demanding for travel industries. AI is capable of doing various travel related works such as from making travel arrangement to suggesting the hotels, flights, and best routes to the customers. Travel industries are using AI-powered chatbots which can make human-like interaction with customers for better and fast response. 8. AI in Automotive Industry ● Some Automotive industries are using AI to provide virtual assistant to their user for better performance. Such as Tesla has introduced TeslaBot, an intelligent virtual assistant. ● Various Industries are currently working for developing self-driven cars which can make your journey more safe and secure.
- 22. 9. AI in Robotics: ● Artificial Intelligence has a remarkable role in Robotics. Usually, general robots are programmed such that they can perform some repetitive task, but with the help of AI, we can create intelligent robots which can perform tasks with their own experiences without pre-programmed. ● Humanoid Robots are best examples for AI in robotics, recently the intelligent Humanoid robot named as Erica and Sophia has been developed which can talk and behave like humans. 10. AI in Entertainment ● We are currently using some AI based applications in our daily life with some entertainment services such as Netflix or Amazon. With the help of ML/AI algorithms, these services show the recommendations for programs or shows. 11. AI in Agriculture ● Agriculture is an area which requires various resources, labor, money, and time for best result. Now a day's agriculture is becoming digital, and AI is emerging in this field. Agriculture is applying AI as agriculture robotics, solid and crop monitoring, predictive analysis. AI in agriculture can be very helpful for farmers.
- 23. 12. AI in E-commerce ● AI is providing a competitive edge to the e-commerce industry, and it is becoming more demanding in the e-commerce business. AI is helping shoppers to discover associated products with recommended size, color, or even brand. 13. AI in education: ● AI can automate grading so that the tutor can have more time to teach. AI chatbot can communicate with students as a teaching assistant. ● AI in the future can be work as a personal virtual tutor for students, which will be accessible easily at any time and any place.
- 28. AI Platform
- 31. Simulating Intelligence – The Turing Test • Alan Turing, the inventor of the Turing machine • The Turing Test is a method of inquiry in artificial intelligence (AI) for determining whether or not a computer is capable of thinking like a human being.
- 32. • Imagine a game of three players having two humans and one computer, an interrogator(as a human) is isolated from the other two players. • The interrogator’s job is to try and figure out which one is human and which one is a computer by asking questions from both of them. To make things harder computer is trying to make the interrogator guess wrongly. • In other words, computers would try to be indistinguishable from humans as much as possible.
- 33. What disciplines do we need to pass the Turing test? • Understand a spoken language using NLP • Expert on things Passing the Turing test is very hard.if this is not enough, we can advance to the Total Turing Test, which also includes movement and vision.
- 34. AI Tools and Learning Models Intelligent Agents When solving AI problems, we create an actor in the environment that can gather data from its surroundings and influence its surroundings. This actor is called an intelligent agent. An intelligent agent: • Is autonomous • Observes its surroundings through sensors • Acts in its environment using actuators • Directs its activities toward achieving goals
- 35. AI Tools and Learning Models AI Learning Models: Knowledge-Based Classification Factoring its representation of knowledge, AI learning models can be classified in two main types: inductive and deductive. —Inductive Learning: This type of AI learning model is based on inferring a general rule from datasets of input-output pairs.. Algorithms such as knowledge based inductive learning(KBIL) are a great example of this type of AI learning technique. KBIL focused on finding inductive hypotheses on a dataset with the help of background information. —Deductive Learning: This type of AI learning technique starts with the series of rules and infers new rules that are more efficient in the context of a specific AI algorithm. Explanation- Based Learning(EBL) and Relevance-0Based Learning(RBL) are examples examples o f deductive techniques. EBL extracts general rules from examples by “generalizing” the explanation. RBL focuses on identifying attributes and deductive generalizations from simple example.
- 36. AI Learning Models: Feedback-Based Classification Based on the feedback characteristics, AI learning models can be classified as supervised, unsupervised, semi- supervised or reinforced. —Unsupervised Learning: Unsupervised models focus on learning a pattern in the input data without any external feedback. Clustering is a classic example of unsupervised learning models.
- 37. —Supervised Learning: Supervised learning models use external feedback to learning functions that map inputs to output observations. In those models the external environment acts as a “teacher” of the AI algorithms. Linear regression —Semi-supervised Learning: Semi-Supervised learning uses a set of curated, labeled data and tries to infer new labels/attributes on new data sets. Semi- Supervised learning models are a solid middle ground between supervised and unsupervised models. —Reinforcement Learning: Reinforcement learning models use opposite dynamics such as rewards and punishment to “reinforce” different types of knowledge. This type of learning technique is becoming really popular in modern AI solutions.
- 38. AITools and Learning Models : One of the core tasks for artificial intelligence is learning. Intelligent Agents When solving AI problems, we create an actor in the environment that can gather data from its surroundings and influence its surroundings. This actor is called an intelligent agent. An intelligent agent: Is autonomous Observes its surroundings through sensors Acts in its environment using actuators Directs its activities toward achieving goals
- 40. The Role of Python in Artificial Intelligence • Python is a high-level programming language. • Cross-platform compatible. • Rapid application development. • Main advantage is memory efficiency, as Python can handle large, in-memory databases.
- 41. Anaconda in Python • Anaconda comes with packages, IDEs, data visualization libraries, and high performance tools for parallel computing in one place. • Anaconda hides configuration problems and the complexity of maintaining a stack for data science, machine learning, and artificial intelligence.
- 44. How to install Libraries?
- 46. Use case - Data Analysis
- 47. Python libraries for AI NumPy: NumPy is a computing library for Python. As Python does not come with a built-in array data structure, we have to use a library to model vectors and matrices efficiently. In data science, we need these data structures to perform simple mathematical operations. SciPy: SciPy is an advanced library containing algorithms that are used for data science. It is a great complementary library to NumPy, because it gives you all the advanced algorithms you need, whether it be a linear algebra algorithm, image processing tool, or a matrix operation.
- 48. pandas: pandas provides fast, flexible, and expressive data structures such as one-dimensional series and two-dimensional DataFrames. It efficiently loads, formats, and handles complex tables of different types. scikit-learn: scikit-learn is Python's main machine learning library. It is based on the NumPy and SciPy libraries. scikit-learn provides you with the functionality required to perform both classification and regression, data preprocessing, as well as supervised and unsupervised learning.
- 49. NLTK: NLTK is still worth mentioning, because this library is the main natural language toolkit of Python. You can perform classification, tokenization, stemming, tagging, parsing, semantic reasoning, and many other services using this library. TensorFlow: TensorFlow is Google's neural network library, and it is perfect for implementing deep learning artificial intelligence. The flexible core of TensorFlow can be used to solve a vast variety of numerical computation problems. Some real-world applications of TensorFlow include Google voice recognition and object identification.
- 50. Introduction to the NumPy Library • NumPy is a Python library used for working with arrays. • It also has functions for working in domain of linear algebra, fourier transform, and matrices. • NumPy was created in 2005 by Travis Oliphant. It is an open source project and you can use it freely. • NumPy stands for Numerical Python. • The source code for NumPy is located at this github repository https://github.com/numpy/numpy
- 51. Operations using Numpy Using NumPy, a developer can perform the following operations − ● Mathematical and logical operations on arrays. ● Fourier transforms and routines for shape manipulation. ● Operations related to linear algebra. NumPy has in-built functions for linear algebra and random number generation.
- 52. After launching your IPython console, you can simply import NumPy as follows: import numpy as np we can define vectors and matrices np.array([1,3,5,7]) The output is as follows: array([1, 3, 5, 7]) We can declare a matrix using the following syntax: A = np.mat([[1,2],[3,3]]) The output is as follows: matrix([[1, 2],[3, 3]]) The array method creates an array data structure, while mat creates a matrix.
- 53. Addition in matrices: A + A The output is as follows: matrix([[2, 4], [6, 6]]) Subtraction in matrices: A - A The output is as follows: matrix([[0, 0],[0, 0]]) Multiplication in matrices A * A The output is as follows: matrix([[ 7, 8], [12, 15]]) Matrix addition and subtraction works cell by cell. Matrix multiplication works according to linear algebra rules.
- 54. • Another frequent matrix operation is the determinant of the matrix. • The determinant is a number associated with square matrices. Calculating the determinant using NumPy is easy: np.linalg.det( A ) • The output is -3.0000000000000004. • the determinant can be calculated as 1*3 – 2*3 = -3. • We can also transpose a matrix, like so: np.matrix.transpose(A) • The output is as follows: matrix([[1, 3],[2, 3]]) • When calculating the transpose of a matrix, we flip its values over its main diagonal.