The course provides the students with practical hands-on experience in data mining and machine learning using open source machine learning libraries such as sci-kit-learn in Python programming language. After completing the course, the students will be able to apply and use various data mining and machine-learning techniques on real-word big/business datasets.
The course provides knowledge of various concepts, techniques, and methods related to data mining, machine learning and deep learning approaches. Furthermore, it introduces :
Basics of Data mining and machine learning
Strengths and weaknesses of Dimensionality Reduction Algorithms: variance thresholds, Correlation Thresholds, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA)
Linear models for regression such as maximum likelihood, sequential learning, regularized least squares
Linear models for classification such as linear classification, logistic regression, support vector machines
Classification models such as probabilistic generative models, probabilistic discriminative models
Deep Learning: deep feed-forward networks, regularization for deep learning, optimization for training deep models, application of deep learning
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