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Artificial intelligence: Beginners to Expert

De (autor): Pon Mahesh

Coperta cărții 'Artificial intelligence: Beginners to Expert - Pon Mahesh'
Artificial intelligence: Beginners to Expert

De (autor): Pon Mahesh

Artificial Intelligence for beginners. Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem-solving "In this book, I'll be covering all the domains and the concepts involved under the umbrella of artificial intelligence, and I will also be showing you a couple of use cases and practical implementations by using Python. So, there's a lot to cover in this session. Following topics are covered: History Of AI Demand For AI What Is Artificial Intelligence? AI Applications Types Of AI Programming Languages For AI Introduction To Machine Learning Need For Machine Learning What Is Machine Learning? Machine Learning Definitions Machine Learning Process Types Of Machine Learning Supervised Learning Unsupervised Learning Reinforcement Learning Supervised vs Unsupervised vs Reinforcement Learning Types Of Problems Solved Using Machine Learning Supervised Learning Algorithms Linear Regression Linear Regression Demo Logistic Regression Decision Tree Random Forest Naive Bayes K Nearest Neighbour (KNN) Support Vector Machine (SVM) Demo (Classification Algorithms) Unsupervised Learning Algorithms K-means Clustering Demo (Unsupervised Learning) Reinforcement Learning Demo (Reinforcement Learning) AI vs Machine Learning vs Deep Learning Limitations Of Machine Learning Introduction To Deep Learning How Deep Learning Works? What Is Deep Learning? Deep Learning Use Case Single Layer Perceptron Multi-Layer Perceptron (ANN) Backpropagation Training A Neural Network Limitations Of Feed Forward Network Recurrent Neural Networks Convolutional Neural Networks Demo (Deep Learning) Natural Language Processing What Is Text Mining? What Is NLP? Applications Of NLP Terminologies In NLP NLP DemoMachine Learning Masters Program
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Artificial Intelligence for beginners. Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem-solving "In this book, I'll be covering all the domains and the concepts involved under the umbrella of artificial intelligence, and I will also be showing you a couple of use cases and practical implementations by using Python. So, there's a lot to cover in this session. Following topics are covered: History Of AI Demand For AI What Is Artificial Intelligence? AI Applications Types Of AI Programming Languages For AI Introduction To Machine Learning Need For Machine Learning What Is Machine Learning? Machine Learning Definitions Machine Learning Process Types Of Machine Learning Supervised Learning Unsupervised Learning Reinforcement Learning Supervised vs Unsupervised vs Reinforcement Learning Types Of Problems Solved Using Machine Learning Supervised Learning Algorithms Linear Regression Linear Regression Demo Logistic Regression Decision Tree Random Forest Naive Bayes K Nearest Neighbour (KNN) Support Vector Machine (SVM) Demo (Classification Algorithms) Unsupervised Learning Algorithms K-means Clustering Demo (Unsupervised Learning) Reinforcement Learning Demo (Reinforcement Learning) AI vs Machine Learning vs Deep Learning Limitations Of Machine Learning Introduction To Deep Learning How Deep Learning Works? What Is Deep Learning? Deep Learning Use Case Single Layer Perceptron Multi-Layer Perceptron (ANN) Backpropagation Training A Neural Network Limitations Of Feed Forward Network Recurrent Neural Networks Convolutional Neural Networks Demo (Deep Learning) Natural Language Processing What Is Text Mining? What Is NLP? Applications Of NLP Terminologies In NLP NLP DemoMachine Learning Masters Program
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