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What is Machine Learning

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Definition of  Machine Learning and Introduction Concepts of Machine Learning Introduction What is machine learning ? History of Machine Learning Benefits of Machine Learning Advantages of Machine Learning Disadvantages of Machine Learning   Machine Learning  Applications Well-posed learning problem Designing a learning system Perspectives and issues in machine learning  Applications of Machine Learning Machine Learning Lifecycle Types of Machine Learning What is Machine Learning? Well-posed learning problem Designing a learning system Perspectives and issues in machine learning  Applications of Machine Learning Machine Learning Lifecycle Types of Machine Learning What is machine learning?  Machine learning (ML) is a subfield of artificial intelligence (AI) that focuses on creating algorithms that can learn from and make predictions or decisions based on data. It is a rapidly growing field that has transformed various industries and has the potential to rev...
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What is Bayes Theorem

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Bayesian Theorem and Concept Learning  Bayesian learning Topics Introduction Bayes theorem Concept learning Maximum Likelihood and least squared error hypotheses Maximum likelihood hypotheses for predicting probabilities Minimum description length principle, Bayes optimal classifier, Gibs algorithm, Naïve Bayes classifier, an example: learning to classify text,  Bayesian belief networks, the EM algorithm. What is Bayesian Learning? Bayesian learning is a type of machine learning that uses Bayesian probability theory to make predictions and decisions based on data.
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What is Unsupervised Learning

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  Clustering and  Principal Component Analysis Unsupervised Learning  Concepts: Clustering Algorithms (K-Means, Hierarchical Clustering) Principal Component Analysis (PCA) Anomaly Detection Model Evaluation and Selection Model Performance Metrics Cross-Validation Techniques Hyperparameter Tuning Model Selection Techniques What is Unsupervised Learning? Unsupervised learning is a type of machine learning where the algorithm is trained on unlabeled data to identify hidden patterns or structures.  Unsupervised learning is a machine learning technique where the goal is to discover patterns or relationships in data without any labelled information. The data is unlabeled, and the algorithm must find structure within the data on its own. Clustering is a common unsupervised learning technique used to group similar data points together.
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What is Analytical Machine Learning

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Analytical  and  Explanation-based learning  with domain theories  Analytical Learning Concepts Introduction Learning with perfect domain theories: PROLOG-EBG Explanation-based learning Explanation-based learning of search control knowledge Analytical Learning Definition :  Analytical learning is a type of machine learning that uses statistical and mathematical techniques to analyze and make predictions based on data.
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Machine Learning Sets of Rules

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Sequential Covering Algorithms and  Learning sets of First-Order rules:  Set of Rules :  Learning a set of rules is a type of machine learning that focuses on discovering patterns or rules that explain the data.  Introduction Sequential covering algorithms Learning First-Order rules Learning sets of First-Order rules: FOIL Summary Introduction  Learning sets of rules is a type of machine learning that involves learning a set of rules from data that can be used to make predictions or classifications. One popular approach to learning sets of rules is through the use of sequential covering algorithms.
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What is Computational machine learning

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Computational Machine Learning and  learning an approximately correct hypothesis Contents of Computational Learning Introduction Probably learning an approximately correct hypothesis Sample complexity for finite hypothesis space Sample complexity for infinite hypothesis space The mistake-bound model of learning Computational Learning Theory:  Computational learning theory is a field of machine learning that studies the mathematical properties and limitations of learning algorithms.
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What is Instance-Based Learning

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k-Nearest Neighbor Learning and  Locally Weighted Regression  Instance-Based Learning Topics Introduction k -Nearest Neighbor Learning Locally Weighted Regression Radial Basis Functions Case-Based Reasoning Remarks on Lazy and Eager Learning. What is Instance-based Learning? Instance-based learning is a type of machine learning that uses a lazy learning approach, where the algorithm stores the training examples and makes predictions based on similarity measures between the new instance and the stored instances.  Introduction Instance-Based Learning is a type of machine learning where the algorithm is given a dataset, and it learns by memorizing the instances in the dataset. The algorithm uses these instances to make predictions on new, unseen instances. The key idea behind instance-based learning is that the function approximator only makes predictions based on the similarity between the new instance and the instances in the dataset.
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