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AI - MACHINE LEARNING.docx

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The document appears to be an assignment on artificial intelligence for a group of 11 students at NACABS Polytechnic in Akwanga, Nasarawa State. It provides an introduction to AI, including definitions of AI, its goals and components. It also discusses applications of AI in various fields such as healthcare, gaming, finance, data security, social media, travel and transport, and the automotive industry. The document aims to educate students on fundamental concepts and applications of artificial intelligence.

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NACABS POLYTECHNIC AKWANGA, NASARAWA STATE. GROUP 1 1. Owoseni Zion Segun -NPAK/CSC/ND/O21/ 2. David Yohanna -NPAK/CSC/ND/O21/0392 3. Gift Danjuma -NPAK/CSC/ND/O21/0400 4. Dorcas Bulus -NPAK/CSC/ND/O21/0422 5. Ayika Blessing Sunday -NPAK/CSC/ND/O21/ 6. Matthew Habila -NPAK/CSC/ND/O21/ 7. Laraba Garba Mbo -NPAK/CSC/ND/O21/0410 8. Kenneth Kater -NPAK/CSC/ND/O21/0438 9. Kwoku Ede Abdulsalam -NPAK/CSC/ND/O21/0418 10.Godi Mahadik -NPAK/CSC/ND/O21/0405 11.Douglas Emmanuel Ego -NPAK/CSC/ND/O21/0398 ASSIGNMENT ARTIFICIAL INTELLIGENCE - Machine Learning -
ARTIFICIAL INTELLIGENCE The Artificial Intelligence tutorial provides an introduction to AI which will help you to understand the concepts behind Artificial Intelligence. In this tutorial, we have also discussed various popular topics such as History of AI, applications of AI, deep learning, machine learning, natural language processing, Reinforcement learning, Q-learning, Intelligent agents, Various search algorithms, etc. Our AI tutorial is prepared from an elementary level so you can easily understand the complete tutorial from basic concepts to the high-level concepts. What is Artificial Intelligence (AI)? In today's world, technology is growing very fast, and we are getting in touch with different new technologies day by day. Here, one of the booming technologies of computer science is Artificial Intelligence which is ready to create a new revolution in the world by making intelligent machines. The Artificial Intelligence is now all around us. It is currently working with a variety of subfields, ranging from general to specific, such as self-driving cars, playing chess, proving theorems, playing music, Painting, etc. Artificial Intelligence is composed of two words Artificial and Intelligence, where Artificial defines "man-made," and intelligence defines "thinking power", hence AI means "a man-made thinking power." So, we can define AI as: "It is a branch of computer science by which we can create intelligent machines which can behave like a human, think like humans, and able to make decisions." Artificial Intelligence exists when a machine can have human based skills such as learning, reasoning, and solving problems
With Artificial Intelligence you do not need to pre-program a machine to do some work, despite that you can create a machine with programmed algorithms which can work with own intelligence, and that is the awesomeness of AI. It is believed that AI is not a new technology, and some people says that as per Greek myth, there were Mechanical men in early days which can work and behave like humans. Why Artificial Intelligence? Before Learning about Artificial Intelligence, we should know that what is the importance of AI and why should we learn it. Following are some main reasons to learn about AI: o With the help of AI, you can create such software or devices which can solve real-world problems very easily and with accuracy such as health issues, marketing, traffic issues, etc. o With the help of AI, you can create your personal virtual Assistant, such as Cortana, Google Assistant, Siri, etc. o With the help of AI, you can build such Robots which can work in an environment where survival of humans can be at risk. o AI opens a path for other new technologies, new devices, and new Opportunities. Goals of Artificial Intelligence Following are the main goals of Artificial Intelligence: 1. Replicate human intelligence 2. Solve Knowledge-intensive tasks 3. An intelligent connection of perception and action 4. Building a machine which can perform tasks that requires human intelligence such as: o Proving a theorem o Playing chess o Plan some surgical operation o Driving a car in traffic 5. Creating some system which can exhibit intelligent behavior, learn new things by itself, demonstrate, explain, and can advise to its user.
What Comprises to Artificial Intelligence? Artificial Intelligence is not just a part of computer science even it's so vast and requires lots of other factors which can contribute to it. To create the AI first we should know that how intelligence is composed, so the Intelligence is an intangible part of our brain which is a combination of Reasoning, learning, problem-solving perception, language understanding, etc. To achieve the above factors for a machine or software Artificial Intelligence requires the following discipline: o Mathematics o Biology o Psychology o Sociology o Computer Science o Neurons Study o Statistics Advantages of Artificial Intelligence Following are some main advantages of Artificial Intelligence: o High Accuracy with less errors: AI machines or systems are prone to less errors and high accuracy as it takes decisions as per pre-experience or information. o High-Speed: AI systems can be of very high-speed and fast-decision making, because of that AI systems can beat a chess champion in the Chess game. o High reliability: AI machines are highly reliable and can perform the same action multiple times with high accuracy. o Useful for risky areas: AI machines can be helpful in situations such as defusing a bomb, exploring the ocean floor, where to employ a human can be risky. o Digital Assistant: AI can be very useful to provide digital assistant to the users such as AI technology is currently used by various E-commerce websites to show the products as per customer requirement.
o Useful as a public utility: AI can be very useful for public utilities such as a self-driving car which can make our journey safer and hassle-free, facial recognition for security purpose, Natural language processing to communicate with the human in human-language, etc. Disadvantages of Artificial Intelligence Every technology has some disadvantages, and the same goes for Artificial intelligence. Being so advantageous technology still, it has some disadvantages which we need to keep in our mind while creating an AI system. Following are the disadvantages of AI: o High Cost: The hardware and software requirement of AI is very costly as it requires lots of maintenance to meet current world requirements. o Can't think out of the box: Even we are making smarter machines with AI, but still they cannot work out of the box, as the robot will only do that work for which they are trained, or programmed. o No feelings and emotions: AI machines can be an outstanding performer, but still it does not have the feeling so it cannot make any kind of emotional attachment with human, and may sometime be harmful for users if the proper care is not taken. o Increase dependency on machines: With the increment of technology, people are getting more dependent on devices and hence they are losing their mental capabilities. o No Original Creativity: As humans are so creative and can imagine some new ideas but still AI machines cannot beat this power of human intelligence and cannot be creative and imaginative. Prerequisite Before learning about Artificial Intelligence, you must have the fundamental knowledge of following so that you can understand the concepts easily: o Any computer language such as C, C++, Java, Python, etc. (knowledge of Python will be an advantage) o Knowledge of essential Mathematics such as derivatives, probability theory, etc. Application of AI
Artificial Intelligence has various applications in today's society. It is becoming essential for today's time because it can solve complex problems with an efficient way in multiple industries, such as Healthcare, entertainment, finance, education, etc. AI is making our daily life more comfortable and fast. Following are some sectors which have the application of Artificial Intelligence: 1. AI in Astronomy o 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 o 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. o 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 o AI can be used for gaming purpose. The AI machines can play strategic games like chess, where the machine needs to think of a large number of possible places. 4. AI in Finance o 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. 5. AI in Data Security o 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 o 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 o 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 o 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. o Various Industries are currently working for developing self-driven cars which can make your journey more safe and secure. 9. AI in Robotics: o 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. o 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 o 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 o 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.
12. AI in E-commerce o 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: o AI can automate grading so that the tutor can have more time to teach. AI chatbot can communicate with students as a teaching assistant. o 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. History of Artificial Intelligence Artificial Intelligence is not a new word and not a new technology for researchers. This technology is much older than you would imagine. Even there are the myths of Mechanical men in Ancient Greek and Egyptian Myths. Following are some milestones in the history of AI which defines the journey from the AI generation to till date development. Types of Artificial Intelligence: Artificial Intelligence can be divided in various types, there are mainly two types of main categorization which are based on capabilities and based on functionally of AI. Following
is flow diagram which explain the types of AI. AI type-1: Based on Capabilities 1. Weak AI or Narrow AI: o Narrow AI is a type of AI which is able to perform a dedicated task with intelligence. The most common and currently available AI is Narrow AI in the world of Artificial Intelligence. o Narrow AI cannot perform beyond its field or limitations, as it is only trained for one specific task. Hence it is also termed as weak AI. Narrow AI can fail in unpredictable ways if it goes beyond its limits. o Apple Siriis a good example of Narrow AI, but it operates with a limited pre-defined range of functions. o IBM's Watson super-computer also comes under Narrow AI, as it uses an Expert system approach combined with Machine learning and natural language processing. o Some Examples of Narrow AI are playing chess, purchasing suggestions on e-commerce site, self-driving cars, speech recognition, and image recognition. 2. General AI: o General AI is a type of intelligence which could perform any intellectual task with efficiency like a human. o The idea behind the general AI to make such a system which could be smarter and think like a human by its own. o Currently, there is no such system exist which could come under general AI and can perform any task as perfect as a human. o The worldwide researchers are now focused on developing machines with General AI. o As systems with general AI are still under research, and it will take lots of efforts and time to develop such systems. 3. Super AI: o Super AI is a level of Intelligence of Systems at which machines could surpass human intelligence, and can perform any task better than human with cognitive properties. It is an outcome of general AI. o Some key characteristics of strong AI include capability include the ability to think, to reason, solve the puzzle, make judgments, plan, learn, and communicate by its own.
o Super AI is still a hypothetical concept of Artificial Intelligence. Development of such systems in real is still world changing task. Artificial Intelligence type-2: Based on functionality 1. Reactive Machines o Purely reactive machines are the most basic types of Artificial Intelligence. o Such AI systems do not store memories or past experiences for future actions. o These machines only focus on current scenarios and react on it as per possible best action. o IBM's Deep Blue system is an example of reactive machines. o Google's AlphaGo is also an example of reactive machines. 2. Limited Memory o Limited memory machines can store past experiences or some data for a short period of time. o These machines can use stored data for a limited time period only. o Self-driving cars are one of the best examples of Limited Memory systems. These cars can store recent speed of nearby cars, the distance of other cars, speed limit, and other information to navigate the road. 3. Theory of Mind o Theory of Mind AI should understand the human emotions, people, beliefs, and be able to interact socially like humans. o This type of AI machines is still not developed, but researchers are making lots of efforts and improvement for developing such AI machines. 4. Self-Awareness o Self-awareness AI is the future of Artificial Intelligence. These machines will be super intelligent, and will have their own consciousness, sentiments, and self-awareness. o These machines will be smarter than human mind. o Self-Awareness AI does not exist in reality still and it is a hypothetical concept.
MACHINE LEARNING Machine learning is a growing technology which enables computers to learn automatically from past data. Machine learning uses various algorithms for building mathematical models and making predictions using historical data or information. Currently, it is being used for various tasks such as image recognition, speech recognition, email filtering, Facebook auto-tagging, recommender system, and many more. This machine learning tutorial gives you an introduction to machine learning along with the wide range of machine learning techniques such as Supervised, Unsupervised, and Reinforcement learning. You will learn about regression and classification models, clustering methods, hidden Markov models, and various sequential models. What is Machine Learning In the real world, we are surrounded by humans who can learn everything from their experiences with their learning capability, and we have computers or machines which work on our instructions. But can a machine also learn from experiences or past data like a human does? So here comes the role of Machine Learning. Machine Learning is said as a subset of artificial intelligence that is mainly concerned with the development of algorithms which allow a computer to learn from the data and past experiences on their own. The term machine learning was first introduced by Arthur Samuel in 1959. We can define it in a summarized way as: “Machine learning enables a machine to automatically learn from data, improve performance from experiences, and predict things without being explicitly programmed.” With the help of sample historical data, which is known as training data, machine learning algorithms build a mathematical model that helps in making predictions or decisions without being explicitly programmed. Machine learning brings computer science and statistics together for creating predictive models. Machine learning constructs or uses the algorithms that learn from historical data. The more we will provide the information, the higher will be the performance. A machine has the ability to learn if it can improve its performance by gaining more data.
How does Machine Learning work A Machine Learning system learns from historical data, builds the prediction models, and whenever it receives new data, predicts the output for it. The accuracy of predicted output depends upon the amount of data, as the huge amount of data helps to build a better model which predicts the output more accurately. Suppose we have a complex problem, where we need to perform some predictions, so instead of writing a code for it, we just need to feed the data to generic algorithms, and with the help of these algorithms, machine builds the logic as per the data and predict the output. Machine learning has changed our way of thinking about the problem. The below block diagram explains the working of Machine Learning algorithm: Features of Machine Learning: o Machine learning uses data to detect various patterns in a given dataset. o It can learn from past data and improve automatically. o It is a data-driven technology. o Machine learning is much similar to data mining as it also deals with the huge amount of the data. Need for Machine Learning The need for machine learning is increasing day by day. The reason behind the need for machine learning is that it is capable of doing tasks that are too complex for a person to implement directly. As a human, we have some limitations as we cannot access the huge amount of data manually, so for this, we need some computer systems and here comes the machine learning to make things easy for us. We can train machine learning algorithms by providing them the huge amount of data and let them explore the data, construct the models, and predict the required output automatically. The performance of the machine learning algorithm depends on the amount of data, and it can be determined by the cost function. With the help of machine learning, we can save both time and money. The importance of machine learning can be easily understood by its uses cases, Currently, machine learning is used in self-driving cars, cyber fraud detection, face recognition, and friend suggestion by Facebook, etc. Various top companies such as Netflix and Amazon have build
machine learning models that are using a vast amount of data to analyze the user interest and recommend product accordingly. Following are some key points which show the importance of Machine Learning: o Rapid increment in the production of data o Solving complex problems, which are difficult for a human o Decision making in various sector including finance o Finding hidden patterns and extracting useful information from data. Classification of Machine Learning At a broad level, machine learning can be classified into three types: 1. Supervised learning 2. Unsupervised learning 3. Reinforcement learning 1) Supervised Learning Supervised learning is a type of machine learning method in which we provide sample labelled data to the machine learning system in order to train it, and on that basis, it predicts the output. The system creates a model using labelled data to understand the datasets and learn about each data, once the training and processing are done then we test the model by providing a sample data to check whether it is predicting the exact output or not. The goal of supervised learning is to map input data with the output data. The supervised learning is based on supervision, and it is the same as when a student learns things in the supervision of the teacher. The example of supervised learning is spam filtering. Supervised learning can be grouped further in two categories of algorithms: o Classification o Regression 2) Unsupervised Learning Unsupervised learning is a learning method in which a machine learns without any supervision.
The training is provided to the machine with the set of data that has not been labelled, classified, or categorized, and the algorithm needs to act on that data without any supervision. The goal of unsupervised learning is to restructure the input data into new features or a group of objects with similar patterns. In unsupervised learning, we don't have a predetermined result. The machine tries to find useful insights from the huge amount of data. It can be further classifieds into two categories of algorithms: o Clustering o Association 3) Reinforcement Learning Reinforcement learning is a feedback-based learning method, in which a learning agent gets a reward for each right action and gets a penalty for each wrong action. The agent learns automatically with these feedbacks and improves its performance. In reinforcement learning, the agent interacts with the environment and explores it. The goal of an agent is to get the most reward points, and hence, it improves its performance. The robotic dog, which automatically learns the movement of his arms, is an example of Reinforcement learning. History of Machine Learning Before some years (about 40-50 years), machine learning was science fiction, but today it is the part of our daily life. Machine learning is making our day-to-day life easy from self-driving cars to Amazon virtual assistant "Alexa". However, the idea behind machine learning is so old and has a long history. Below some milestones are given which have occurred in the history of machine learning: The early history of Machine Learning (Pre-1940): o 1834: In 1834, Charles Babbage, the father of the computer, conceived a device that could be programmed with punch cards. However, the machine was never built, but all modern computers rely on its logical structure. o 1936: In 1936, Alan Turing gave a theory that how a machine can determine and execute a set of instructions.
The era of stored program computers: o 1940: In 1940, the first manually operated computer, "ENIAC" was invented, which was the first electronic general-purpose computer. After that stored program computer such as EDSAC in 1949 and EDVAC in 1951 were invented. o 1943: In 1943, a human neural network was modeled with an electrical circuit. In 1950, the scientists started applying their idea to work and analyzed how human neurons might work. Computer machinery and intelligence: o 1950: In 1950, Alan Turing published a seminal paper, "Computer Machinery and Intelligence," on the topic of artificial intelligence. In his paper, he asked, "Can machines think?" Machine intelligence in Games: o 1952: Arthur Samuel, who was the pioneer of machine learning, created a program that helped an IBM computer to play a checkers game. It performed better more it played. o 1959: In 1959, the term "Machine Learning" was first coined by Arthur Samuel. The first "AI" winter: o The duration of 1974 to 1980 was the tough time for AI and ML researchers, and this duration was called as AI winter. o In this duration, failure of machine translation occurred, and people had reduced their interest from AI, which led to reduced funding by the government to the researches. Machine Learning from theory to reality o 1959: In 1959, the first neural network was applied to a real-world problem to remove echoes over phone lines using an adaptive filter. o 1985: In 1985, Terry Sejnowski and Charles Rosenberg invented a neural network NETtalk, which was able to teach itself how to correctly pronounce 20,000 words in one week. o 1997: The IBM's Deep blue intelligent computer won the chess game against the chess expert Garry Kasparov, and it became the first computer which had beaten a human chess expert. Machine Learning at 21st century o 2006: In the year 2006, computer scientist Geoffrey Hinton has given a new name to neural net research as "deep learning," and nowadays, it has become one of the most trending technologies. o 2012: In 2012, Google created a deep neural network which learned to recognize the image of humans and cats in YouTube videos.
o 2014: In 2014, the Chabot "Eugen Goostman" cleared the Turing Test. It was the first Chabot who convinced the 33% of human judges that it was not a machine. o 2014: DeepFace was a deep neural network created by Facebook, and they claimed that it could recognize a person with the same precision as a human can do. o 2016: AlphaGo beat the world's number second player Lee sedol at Go game. In 2017 it beat the number one player of this game Ke Jie. o 2017: In 2017, the Alphabet's Jigsaw team built an intelligent system that was able to learn the online trolling. It used to read millions of comments of different websites to learn to stop online trolling. Machine Learning at present: Now machine learning has got a great advancement in its research, and it is present everywhere around us, such as self-driving cars, Amazon Alexa, Catboats, recommender system, and many more. It includes Supervised, unsupervised, and reinforcement learning with clustering, classification, decision tree, SVM algorithms, etc. Modern machine learning models can be used for making various predictions, including weather prediction, disease prediction, stock market analysis, etc. Prerequisites Before learning machine learning, you must have the basic knowledge of followings so that you can easily understand the concepts of machine learning: o Fundamental knowledge of probability and linear algebra. o The ability to code in any computer language, especially in Python language. o Knowledge of Calculus, especially derivatives of single variable and multivariate functions. Applications of Machine learning Machine learning is a buzzword for today's technology, and it is growing very rapidly day by day. We are using machine learning in our daily life even without knowing it such as Google Maps, Google assistant, Alexa, etc. Below are some most trending real- world applications of Machine Learning:
1. Image Recognition: Image recognition is one of the most common applications of machine learning. It is used to identify objects, persons, places, digital images, etc. The popular use case of image recognition and face detection is, Automatic friend tagging suggestion: Facebook provides us a feature of auto friend tagging suggestion. Whenever we upload a photo with our Facebook friends, then we automatically get a tagging suggestion with name, and the technology behind this is machine learning's face detection and recognition algorithm. It is based on the Facebook project named "Deep Face," which is responsible for face recognition and person identification in the picture. 2. Speech Recognition While using Google, we get an option of "Search by voice," it comes under speech recognition, and it's a popular application of machine learning. Speech recognition is a process of converting voice instructions into text, and it is also known as "Speech to text", or "Computer speech recognition." At present, machine learning algorithms are widely used by various applications of speech recognition. Google assistant, Siri, Cortana, and Alexa are using speech recognition technology to follow the voice instructions. 3. Traffic prediction: If we want to visit a new place, we take help of Google Maps, which shows us the correct path with the shortest route and predicts the traffic conditions. It predicts the traffic conditions such as whether traffic is cleared, slow-moving, or heavily congested with the help of two ways: o Real Time location of the vehicle form Google Map app and sensors o Average time has taken on past days at the same time. Everyone who is using Google Map is helping this app to make it better. It takes information from the user and sends back to its database to improve the performance. 4. Product recommendations: Machine learning is widely used by various e-commerce and entertainment companies such as Amazon, Netflix, etc., for product recommendation to the user. Whenever we search for some product on Amazon, then we started getting an advertisement for the same product while internet surfing on the same browser and this is because of machine learning.
Google understands the user interest using various machine learning algorithms and suggests the product as per customer interest. As similar, when we use Netflix, we find some recommendations for entertainment series, movies, etc., and this is also done with the help of machine learning. 5. Self-driving cars: One of the most exciting applications of machine learning is self-driving cars. Machine learning plays a significant role in self-driving cars. Tesla, the most popular car manufacturing company is working on self-driving car. It is using unsupervised learning method to train the car models to detect people and objects while driving. 6. Email Spam and Malware Filtering: Whenever we receive a new email, it is filtered automatically as important, normal, and spam. We always receive an important mail in our inbox with the important symbol and spam emails in our spam box, and the technology behind this is Machine learning. Below are some spam filters used by Gmail: o Content Filter o Header filter o General blacklists filter o Rules-based filters o Permission filters Some machine learning algorithms such as Multi-Layer Perceptron, Decision tree, and Naïve Bayes classifier are used for email spam filtering and malware detection. 7. Virtual Personal Assistant: We have various virtual personal assistants such as Google assistant, Alexa, Cortana, Siri. As the name suggests, they help us in finding the information using our voice instruction. These assistants can help us in various ways just by our voice instructions such as Play music, call someone, Open an email, Scheduling an appointment, etc. These virtual assistants use machine learning algorithms as an important part. These assistant record our voice instructions, send it over the server on a cloud, and decode it using ML algorithms and act accordingly.
8. Online Fraud Detection: Machine learning is making our online transaction safe and secure by detecting fraud transaction. Whenever we perform some online transaction, there may be various ways that a fraudulent transaction can take place such as fake accounts, fake ids, and steal money in the middle of a transaction. So, to detect this, Feed Forward Neural network helps us by checking whether it is a genuine transaction or a fraud transaction. For each genuine transaction, the output is converted into some hash values, and these values become the input for the next round. For each genuine transaction, there is a specific pattern which gets change for the fraud transaction hence, it detects it and makes our online transactions more secure. 9. Stock Market trading: Machine learning is widely used in stock market trading. In the stock market, there is always a risk of up and downs in shares, so for this machine learning's long short-term memory neural network is used for the prediction of stock market trends. 10. Medical Diagnosis: In medical science, machine learning is used for diseases diagnoses. With this, medical technology is growing very fast and able to build 3D models that can predict the exact position of lesions in the brain. It helps in finding brain tumors and other brain-related diseases easily. 11. Automatic Language Translation: Nowadays, if we visit a new place and we are not aware of the language then it is not a problem at all, as for this also machine learning helps us by converting the text into our known languages. Google's GNMT (Google Neural Machine Translation) provide this feature, which is a Neural Machine Learning that translates the text into our familiar language, and it called as automatic translation. The technology behind the automatic translation is a sequence-to-sequence learning algorithm, which is used with image recognition and translates the text from one language to another language.

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AI - MACHINE LEARNING.docx

  • 1. NACABS POLYTECHNIC AKWANGA, NASARAWA STATE. GROUP 1 1. Owoseni Zion Segun -NPAK/CSC/ND/O21/ 2. David Yohanna -NPAK/CSC/ND/O21/0392 3. Gift Danjuma -NPAK/CSC/ND/O21/0400 4. Dorcas Bulus -NPAK/CSC/ND/O21/0422 5. Ayika Blessing Sunday -NPAK/CSC/ND/O21/ 6. Matthew Habila -NPAK/CSC/ND/O21/ 7. Laraba Garba Mbo -NPAK/CSC/ND/O21/0410 8. Kenneth Kater -NPAK/CSC/ND/O21/0438 9. Kwoku Ede Abdulsalam -NPAK/CSC/ND/O21/0418 10.Godi Mahadik -NPAK/CSC/ND/O21/0405 11.Douglas Emmanuel Ego -NPAK/CSC/ND/O21/0398 ASSIGNMENT ARTIFICIAL INTELLIGENCE - Machine Learning -
  • 2. ARTIFICIAL INTELLIGENCE The Artificial Intelligence tutorial provides an introduction to AI which will help you to understand the concepts behind Artificial Intelligence. In this tutorial, we have also discussed various popular topics such as History of AI, applications of AI, deep learning, machine learning, natural language processing, Reinforcement learning, Q-learning, Intelligent agents, Various search algorithms, etc. Our AI tutorial is prepared from an elementary level so you can easily understand the complete tutorial from basic concepts to the high-level concepts. What is Artificial Intelligence (AI)? In today's world, technology is growing very fast, and we are getting in touch with different new technologies day by day. Here, one of the booming technologies of computer science is Artificial Intelligence which is ready to create a new revolution in the world by making intelligent machines. The Artificial Intelligence is now all around us. It is currently working with a variety of subfields, ranging from general to specific, such as self-driving cars, playing chess, proving theorems, playing music, Painting, etc. Artificial Intelligence is composed of two words Artificial and Intelligence, where Artificial defines "man-made," and intelligence defines "thinking power", hence AI means "a man-made thinking power." So, we can define AI as: "It is a branch of computer science by which we can create intelligent machines which can behave like a human, think like humans, and able to make decisions." Artificial Intelligence exists when a machine can have human based skills such as learning, reasoning, and solving problems
  • 3. With Artificial Intelligence you do not need to pre-program a machine to do some work, despite that you can create a machine with programmed algorithms which can work with own intelligence, and that is the awesomeness of AI. It is believed that AI is not a new technology, and some people says that as per Greek myth, there were Mechanical men in early days which can work and behave like humans. Why Artificial Intelligence? Before Learning about Artificial Intelligence, we should know that what is the importance of AI and why should we learn it. Following are some main reasons to learn about AI: o With the help of AI, you can create such software or devices which can solve real-world problems very easily and with accuracy such as health issues, marketing, traffic issues, etc. o With the help of AI, you can create your personal virtual Assistant, such as Cortana, Google Assistant, Siri, etc. o With the help of AI, you can build such Robots which can work in an environment where survival of humans can be at risk. o AI opens a path for other new technologies, new devices, and new Opportunities. Goals of Artificial Intelligence Following are the main goals of Artificial Intelligence: 1. Replicate human intelligence 2. Solve Knowledge-intensive tasks 3. An intelligent connection of perception and action 4. Building a machine which can perform tasks that requires human intelligence such as: o Proving a theorem o Playing chess o Plan some surgical operation o Driving a car in traffic 5. Creating some system which can exhibit intelligent behavior, learn new things by itself, demonstrate, explain, and can advise to its user.
  • 4. What Comprises to Artificial Intelligence? Artificial Intelligence is not just a part of computer science even it's so vast and requires lots of other factors which can contribute to it. To create the AI first we should know that how intelligence is composed, so the Intelligence is an intangible part of our brain which is a combination of Reasoning, learning, problem-solving perception, language understanding, etc. To achieve the above factors for a machine or software Artificial Intelligence requires the following discipline: o Mathematics o Biology o Psychology o Sociology o Computer Science o Neurons Study o Statistics Advantages of Artificial Intelligence Following are some main advantages of Artificial Intelligence: o High Accuracy with less errors: AI machines or systems are prone to less errors and high accuracy as it takes decisions as per pre-experience or information. o High-Speed: AI systems can be of very high-speed and fast-decision making, because of that AI systems can beat a chess champion in the Chess game. o High reliability: AI machines are highly reliable and can perform the same action multiple times with high accuracy. o Useful for risky areas: AI machines can be helpful in situations such as defusing a bomb, exploring the ocean floor, where to employ a human can be risky. o Digital Assistant: AI can be very useful to provide digital assistant to the users such as AI technology is currently used by various E-commerce websites to show the products as per customer requirement.
  • 5. o Useful as a public utility: AI can be very useful for public utilities such as a self-driving car which can make our journey safer and hassle-free, facial recognition for security purpose, Natural language processing to communicate with the human in human-language, etc. Disadvantages of Artificial Intelligence Every technology has some disadvantages, and the same goes for Artificial intelligence. Being so advantageous technology still, it has some disadvantages which we need to keep in our mind while creating an AI system. Following are the disadvantages of AI: o High Cost: The hardware and software requirement of AI is very costly as it requires lots of maintenance to meet current world requirements. o Can't think out of the box: Even we are making smarter machines with AI, but still they cannot work out of the box, as the robot will only do that work for which they are trained, or programmed. o No feelings and emotions: AI machines can be an outstanding performer, but still it does not have the feeling so it cannot make any kind of emotional attachment with human, and may sometime be harmful for users if the proper care is not taken. o Increase dependency on machines: With the increment of technology, people are getting more dependent on devices and hence they are losing their mental capabilities. o No Original Creativity: As humans are so creative and can imagine some new ideas but still AI machines cannot beat this power of human intelligence and cannot be creative and imaginative. Prerequisite Before learning about Artificial Intelligence, you must have the fundamental knowledge of following so that you can understand the concepts easily: o Any computer language such as C, C++, Java, Python, etc. (knowledge of Python will be an advantage) o Knowledge of essential Mathematics such as derivatives, probability theory, etc. Application of AI
  • 6. Artificial Intelligence has various applications in today's society. It is becoming essential for today's time because it can solve complex problems with an efficient way in multiple industries, such as Healthcare, entertainment, finance, education, etc. AI is making our daily life more comfortable and fast. Following are some sectors which have the application of Artificial Intelligence: 1. AI in Astronomy o 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 o 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. o 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 o AI can be used for gaming purpose. The AI machines can play strategic games like chess, where the machine needs to think of a large number of possible places. 4. AI in Finance o 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. 5. AI in Data Security o 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.
  • 7. 6. AI in Social Media o 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 o 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 o 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. o Various Industries are currently working for developing self-driven cars which can make your journey more safe and secure. 9. AI in Robotics: o 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. o 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 o 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 o 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.
  • 8. 12. AI in E-commerce o 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: o AI can automate grading so that the tutor can have more time to teach. AI chatbot can communicate with students as a teaching assistant. o 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. History of Artificial Intelligence Artificial Intelligence is not a new word and not a new technology for researchers. This technology is much older than you would imagine. Even there are the myths of Mechanical men in Ancient Greek and Egyptian Myths. Following are some milestones in the history of AI which defines the journey from the AI generation to till date development. Types of Artificial Intelligence: Artificial Intelligence can be divided in various types, there are mainly two types of main categorization which are based on capabilities and based on functionally of AI. Following
  • 9. is flow diagram which explain the types of AI. AI type-1: Based on Capabilities 1. Weak AI or Narrow AI: o Narrow AI is a type of AI which is able to perform a dedicated task with intelligence. The most common and currently available AI is Narrow AI in the world of Artificial Intelligence. o Narrow AI cannot perform beyond its field or limitations, as it is only trained for one specific task. Hence it is also termed as weak AI. Narrow AI can fail in unpredictable ways if it goes beyond its limits. o Apple Siriis a good example of Narrow AI, but it operates with a limited pre-defined range of functions. o IBM's Watson super-computer also comes under Narrow AI, as it uses an Expert system approach combined with Machine learning and natural language processing. o Some Examples of Narrow AI are playing chess, purchasing suggestions on e-commerce site, self-driving cars, speech recognition, and image recognition. 2. General AI: o General AI is a type of intelligence which could perform any intellectual task with efficiency like a human. o The idea behind the general AI to make such a system which could be smarter and think like a human by its own. o Currently, there is no such system exist which could come under general AI and can perform any task as perfect as a human. o The worldwide researchers are now focused on developing machines with General AI. o As systems with general AI are still under research, and it will take lots of efforts and time to develop such systems. 3. Super AI: o Super AI is a level of Intelligence of Systems at which machines could surpass human intelligence, and can perform any task better than human with cognitive properties. It is an outcome of general AI. o Some key characteristics of strong AI include capability include the ability to think, to reason, solve the puzzle, make judgments, plan, learn, and communicate by its own.
  • 10. o Super AI is still a hypothetical concept of Artificial Intelligence. Development of such systems in real is still world changing task. Artificial Intelligence type-2: Based on functionality 1. Reactive Machines o Purely reactive machines are the most basic types of Artificial Intelligence. o Such AI systems do not store memories or past experiences for future actions. o These machines only focus on current scenarios and react on it as per possible best action. o IBM's Deep Blue system is an example of reactive machines. o Google's AlphaGo is also an example of reactive machines. 2. Limited Memory o Limited memory machines can store past experiences or some data for a short period of time. o These machines can use stored data for a limited time period only. o Self-driving cars are one of the best examples of Limited Memory systems. These cars can store recent speed of nearby cars, the distance of other cars, speed limit, and other information to navigate the road. 3. Theory of Mind o Theory of Mind AI should understand the human emotions, people, beliefs, and be able to interact socially like humans. o This type of AI machines is still not developed, but researchers are making lots of efforts and improvement for developing such AI machines. 4. Self-Awareness o Self-awareness AI is the future of Artificial Intelligence. These machines will be super intelligent, and will have their own consciousness, sentiments, and self-awareness. o These machines will be smarter than human mind. o Self-Awareness AI does not exist in reality still and it is a hypothetical concept.
  • 11. MACHINE LEARNING Machine learning is a growing technology which enables computers to learn automatically from past data. Machine learning uses various algorithms for building mathematical models and making predictions using historical data or information. Currently, it is being used for various tasks such as image recognition, speech recognition, email filtering, Facebook auto-tagging, recommender system, and many more. This machine learning tutorial gives you an introduction to machine learning along with the wide range of machine learning techniques such as Supervised, Unsupervised, and Reinforcement learning. You will learn about regression and classification models, clustering methods, hidden Markov models, and various sequential models. What is Machine Learning In the real world, we are surrounded by humans who can learn everything from their experiences with their learning capability, and we have computers or machines which work on our instructions. But can a machine also learn from experiences or past data like a human does? So here comes the role of Machine Learning. Machine Learning is said as a subset of artificial intelligence that is mainly concerned with the development of algorithms which allow a computer to learn from the data and past experiences on their own. The term machine learning was first introduced by Arthur Samuel in 1959. We can define it in a summarized way as: “Machine learning enables a machine to automatically learn from data, improve performance from experiences, and predict things without being explicitly programmed.” With the help of sample historical data, which is known as training data, machine learning algorithms build a mathematical model that helps in making predictions or decisions without being explicitly programmed. Machine learning brings computer science and statistics together for creating predictive models. Machine learning constructs or uses the algorithms that learn from historical data. The more we will provide the information, the higher will be the performance. A machine has the ability to learn if it can improve its performance by gaining more data.
  • 12. How does Machine Learning work A Machine Learning system learns from historical data, builds the prediction models, and whenever it receives new data, predicts the output for it. The accuracy of predicted output depends upon the amount of data, as the huge amount of data helps to build a better model which predicts the output more accurately. Suppose we have a complex problem, where we need to perform some predictions, so instead of writing a code for it, we just need to feed the data to generic algorithms, and with the help of these algorithms, machine builds the logic as per the data and predict the output. Machine learning has changed our way of thinking about the problem. The below block diagram explains the working of Machine Learning algorithm: Features of Machine Learning: o Machine learning uses data to detect various patterns in a given dataset. o It can learn from past data and improve automatically. o It is a data-driven technology. o Machine learning is much similar to data mining as it also deals with the huge amount of the data. Need for Machine Learning The need for machine learning is increasing day by day. The reason behind the need for machine learning is that it is capable of doing tasks that are too complex for a person to implement directly. As a human, we have some limitations as we cannot access the huge amount of data manually, so for this, we need some computer systems and here comes the machine learning to make things easy for us. We can train machine learning algorithms by providing them the huge amount of data and let them explore the data, construct the models, and predict the required output automatically. The performance of the machine learning algorithm depends on the amount of data, and it can be determined by the cost function. With the help of machine learning, we can save both time and money. The importance of machine learning can be easily understood by its uses cases, Currently, machine learning is used in self-driving cars, cyber fraud detection, face recognition, and friend suggestion by Facebook, etc. Various top companies such as Netflix and Amazon have build
  • 13. machine learning models that are using a vast amount of data to analyze the user interest and recommend product accordingly. Following are some key points which show the importance of Machine Learning: o Rapid increment in the production of data o Solving complex problems, which are difficult for a human o Decision making in various sector including finance o Finding hidden patterns and extracting useful information from data. Classification of Machine Learning At a broad level, machine learning can be classified into three types: 1. Supervised learning 2. Unsupervised learning 3. Reinforcement learning 1) Supervised Learning Supervised learning is a type of machine learning method in which we provide sample labelled data to the machine learning system in order to train it, and on that basis, it predicts the output. The system creates a model using labelled data to understand the datasets and learn about each data, once the training and processing are done then we test the model by providing a sample data to check whether it is predicting the exact output or not. The goal of supervised learning is to map input data with the output data. The supervised learning is based on supervision, and it is the same as when a student learns things in the supervision of the teacher. The example of supervised learning is spam filtering. Supervised learning can be grouped further in two categories of algorithms: o Classification o Regression 2) Unsupervised Learning Unsupervised learning is a learning method in which a machine learns without any supervision.
  • 14. The training is provided to the machine with the set of data that has not been labelled, classified, or categorized, and the algorithm needs to act on that data without any supervision. The goal of unsupervised learning is to restructure the input data into new features or a group of objects with similar patterns. In unsupervised learning, we don't have a predetermined result. The machine tries to find useful insights from the huge amount of data. It can be further classifieds into two categories of algorithms: o Clustering o Association 3) Reinforcement Learning Reinforcement learning is a feedback-based learning method, in which a learning agent gets a reward for each right action and gets a penalty for each wrong action. The agent learns automatically with these feedbacks and improves its performance. In reinforcement learning, the agent interacts with the environment and explores it. The goal of an agent is to get the most reward points, and hence, it improves its performance. The robotic dog, which automatically learns the movement of his arms, is an example of Reinforcement learning. History of Machine Learning Before some years (about 40-50 years), machine learning was science fiction, but today it is the part of our daily life. Machine learning is making our day-to-day life easy from self-driving cars to Amazon virtual assistant "Alexa". However, the idea behind machine learning is so old and has a long history. Below some milestones are given which have occurred in the history of machine learning: The early history of Machine Learning (Pre-1940): o 1834: In 1834, Charles Babbage, the father of the computer, conceived a device that could be programmed with punch cards. However, the machine was never built, but all modern computers rely on its logical structure. o 1936: In 1936, Alan Turing gave a theory that how a machine can determine and execute a set of instructions.
  • 15. The era of stored program computers: o 1940: In 1940, the first manually operated computer, "ENIAC" was invented, which was the first electronic general-purpose computer. After that stored program computer such as EDSAC in 1949 and EDVAC in 1951 were invented. o 1943: In 1943, a human neural network was modeled with an electrical circuit. In 1950, the scientists started applying their idea to work and analyzed how human neurons might work. Computer machinery and intelligence: o 1950: In 1950, Alan Turing published a seminal paper, "Computer Machinery and Intelligence," on the topic of artificial intelligence. In his paper, he asked, "Can machines think?" Machine intelligence in Games: o 1952: Arthur Samuel, who was the pioneer of machine learning, created a program that helped an IBM computer to play a checkers game. It performed better more it played. o 1959: In 1959, the term "Machine Learning" was first coined by Arthur Samuel. The first "AI" winter: o The duration of 1974 to 1980 was the tough time for AI and ML researchers, and this duration was called as AI winter. o In this duration, failure of machine translation occurred, and people had reduced their interest from AI, which led to reduced funding by the government to the researches. Machine Learning from theory to reality o 1959: In 1959, the first neural network was applied to a real-world problem to remove echoes over phone lines using an adaptive filter. o 1985: In 1985, Terry Sejnowski and Charles Rosenberg invented a neural network NETtalk, which was able to teach itself how to correctly pronounce 20,000 words in one week. o 1997: The IBM's Deep blue intelligent computer won the chess game against the chess expert Garry Kasparov, and it became the first computer which had beaten a human chess expert. Machine Learning at 21st century o 2006: In the year 2006, computer scientist Geoffrey Hinton has given a new name to neural net research as "deep learning," and nowadays, it has become one of the most trending technologies. o 2012: In 2012, Google created a deep neural network which learned to recognize the image of humans and cats in YouTube videos.
  • 16. o 2014: In 2014, the Chabot "Eugen Goostman" cleared the Turing Test. It was the first Chabot who convinced the 33% of human judges that it was not a machine. o 2014: DeepFace was a deep neural network created by Facebook, and they claimed that it could recognize a person with the same precision as a human can do. o 2016: AlphaGo beat the world's number second player Lee sedol at Go game. In 2017 it beat the number one player of this game Ke Jie. o 2017: In 2017, the Alphabet's Jigsaw team built an intelligent system that was able to learn the online trolling. It used to read millions of comments of different websites to learn to stop online trolling. Machine Learning at present: Now machine learning has got a great advancement in its research, and it is present everywhere around us, such as self-driving cars, Amazon Alexa, Catboats, recommender system, and many more. It includes Supervised, unsupervised, and reinforcement learning with clustering, classification, decision tree, SVM algorithms, etc. Modern machine learning models can be used for making various predictions, including weather prediction, disease prediction, stock market analysis, etc. Prerequisites Before learning machine learning, you must have the basic knowledge of followings so that you can easily understand the concepts of machine learning: o Fundamental knowledge of probability and linear algebra. o The ability to code in any computer language, especially in Python language. o Knowledge of Calculus, especially derivatives of single variable and multivariate functions. Applications of Machine learning Machine learning is a buzzword for today's technology, and it is growing very rapidly day by day. We are using machine learning in our daily life even without knowing it such as Google Maps, Google assistant, Alexa, etc. Below are some most trending real- world applications of Machine Learning:
  • 17. 1. Image Recognition: Image recognition is one of the most common applications of machine learning. It is used to identify objects, persons, places, digital images, etc. The popular use case of image recognition and face detection is, Automatic friend tagging suggestion: Facebook provides us a feature of auto friend tagging suggestion. Whenever we upload a photo with our Facebook friends, then we automatically get a tagging suggestion with name, and the technology behind this is machine learning's face detection and recognition algorithm. It is based on the Facebook project named "Deep Face," which is responsible for face recognition and person identification in the picture. 2. Speech Recognition While using Google, we get an option of "Search by voice," it comes under speech recognition, and it's a popular application of machine learning. Speech recognition is a process of converting voice instructions into text, and it is also known as "Speech to text", or "Computer speech recognition." At present, machine learning algorithms are widely used by various applications of speech recognition. Google assistant, Siri, Cortana, and Alexa are using speech recognition technology to follow the voice instructions. 3. Traffic prediction: If we want to visit a new place, we take help of Google Maps, which shows us the correct path with the shortest route and predicts the traffic conditions. It predicts the traffic conditions such as whether traffic is cleared, slow-moving, or heavily congested with the help of two ways: o Real Time location of the vehicle form Google Map app and sensors o Average time has taken on past days at the same time. Everyone who is using Google Map is helping this app to make it better. It takes information from the user and sends back to its database to improve the performance. 4. Product recommendations: Machine learning is widely used by various e-commerce and entertainment companies such as Amazon, Netflix, etc., for product recommendation to the user. Whenever we search for some product on Amazon, then we started getting an advertisement for the same product while internet surfing on the same browser and this is because of machine learning.
  • 18. Google understands the user interest using various machine learning algorithms and suggests the product as per customer interest. As similar, when we use Netflix, we find some recommendations for entertainment series, movies, etc., and this is also done with the help of machine learning. 5. Self-driving cars: One of the most exciting applications of machine learning is self-driving cars. Machine learning plays a significant role in self-driving cars. Tesla, the most popular car manufacturing company is working on self-driving car. It is using unsupervised learning method to train the car models to detect people and objects while driving. 6. Email Spam and Malware Filtering: Whenever we receive a new email, it is filtered automatically as important, normal, and spam. We always receive an important mail in our inbox with the important symbol and spam emails in our spam box, and the technology behind this is Machine learning. Below are some spam filters used by Gmail: o Content Filter o Header filter o General blacklists filter o Rules-based filters o Permission filters Some machine learning algorithms such as Multi-Layer Perceptron, Decision tree, and Naïve Bayes classifier are used for email spam filtering and malware detection. 7. Virtual Personal Assistant: We have various virtual personal assistants such as Google assistant, Alexa, Cortana, Siri. As the name suggests, they help us in finding the information using our voice instruction. These assistants can help us in various ways just by our voice instructions such as Play music, call someone, Open an email, Scheduling an appointment, etc. These virtual assistants use machine learning algorithms as an important part. These assistant record our voice instructions, send it over the server on a cloud, and decode it using ML algorithms and act accordingly.
  • 19. 8. Online Fraud Detection: Machine learning is making our online transaction safe and secure by detecting fraud transaction. Whenever we perform some online transaction, there may be various ways that a fraudulent transaction can take place such as fake accounts, fake ids, and steal money in the middle of a transaction. So, to detect this, Feed Forward Neural network helps us by checking whether it is a genuine transaction or a fraud transaction. For each genuine transaction, the output is converted into some hash values, and these values become the input for the next round. For each genuine transaction, there is a specific pattern which gets change for the fraud transaction hence, it detects it and makes our online transactions more secure. 9. Stock Market trading: Machine learning is widely used in stock market trading. In the stock market, there is always a risk of up and downs in shares, so for this machine learning's long short-term memory neural network is used for the prediction of stock market trends. 10. Medical Diagnosis: In medical science, machine learning is used for diseases diagnoses. With this, medical technology is growing very fast and able to build 3D models that can predict the exact position of lesions in the brain. It helps in finding brain tumors and other brain-related diseases easily. 11. Automatic Language Translation: Nowadays, if we visit a new place and we are not aware of the language then it is not a problem at all, as for this also machine learning helps us by converting the text into our known languages. Google's GNMT (Google Neural Machine Translation) provide this feature, which is a Neural Machine Learning that translates the text into our familiar language, and it called as automatic translation. The technology behind the automatic translation is a sequence-to-sequence learning algorithm, which is used with image recognition and translates the text from one language to another language.