This thesis discusses machine learning algorithms and types of artificial intelligence. Machine learning is a branch of artificial intelligence that includes methods, or algorithms, for automatically creating models from data. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. In ML, different algorithms help to solve problems. Machine learning algorithms include supervised reinforced and unsupervised algorithms. Alternatively, Artificial intelligence refers to the simulation of human intelligence processes by computer systems. The actions of AI include self-correction, reasoning, and learning. The basis of the main types of artificial intelligence is on how well they can replicate human-like activities. In conclusion, AI is the bridge between human intelligence and machine learning. Therefore, there is an increase in AI applications across different sectors.
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TYPES OF ARTIFICIAL INTELLIGENCE AND APPLICATIONS OF AI
The various types of artificial intelligence include Weak AI, designed and trained for a specific task. Strong AI is a system with generalized human cognitive abilities. Also, reactive machines use past experiences to determine future actions. Fourthly, limited memory uses past experiences to inform future decisions, while The Theory of mind entails imitating the human brain. Lastly, Self-awareness AI systems have a sense of self. There is a range of AL applications in various sectors, such as in healthcare, education, and entertainment industries. In conclusion, AI has many great applications that are changing the world of technology. While creating an AI system that is generally as intelligent as humans remains a dream, Machine Learning algorithms already allows the computer to outperform us in computations, pattern recognition, and anomaly detection.
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TYPES OF MACHINE LEARNING ALGORITHMS AND COMPONENTS OF MACHINE LEARNING
There are four types of machine learning algorithms. First, the Supervise algorithm can apply what has been learned in the past to new data using labeled examples to predict future events. In contrast, the application of unsupervised algorithms is when the information used to train is neither classified nor labeled. Also, Semi-supervised algorithms fall in between supervised and unsupervised algorithms since they use both labeled and unlabeled data for training. These systems offer improved learning accuracy. Lastly, Reinforcement algorithms interact with a dynamic environment that provides feedback in terms of rewards and punishments. The different types of artificial intelligence apply these technologies. The components of machine learning are vital for these algorithms. They include datasets and deep learning. These components and other algorithms innovations have made a perfect environment for new AI applications and services to grow.
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