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First Order Logic, Inference, Unification, Chaining & Learning in Artificial Intelligence

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Clustering in Machine Learning: Methods, Distance Metrics, K-Means, Hierarchical & Density-Based Techniques

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Naive Bayes Classifier: Model Assumptions, Probability Estimation, M-Estimates, Feature Selection & Mutual Information

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Support Vector Machines (SVM) and Radial Basis Function (RBF) Networks in Machine Learning

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Feature Reduction & Dimensionality Reduction in Machine Learning: PCA and LDA Explained

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Knowledge Representation and Reasoning in Artificial Intelligence (AI)

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Python Basics for Beginners: Data Structures, Loops, Functions, NumPy, Pandas, Matplotlib & Seaborn

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