This course provides a deep dive into the fundamental concepts and practical techniques essential for understanding and implementing machine learning algorithms. Spanning fourteen weeks, the curriculum is structured to progressively build expertise, starting with mathematical foundations, This course provides a deep dive into the fundamental concepts and practical techniques essential for understanding and implementing machine learning algorithms. Spanning fourteen weeks, the curriculum is structured to progressively build expertise, starting with mathematical foundations, and advancing through classical and contemporary machine learning methodologiesand advancing through classical and contemporary machine learning methodologies.
Module 1 | Mathematical Basics 1 – Introduction to Machine Learning, Linear Algebra |
Module 2 | Mathematical Basics 2 - Probability |
Module 3 | Computational Basics – Numerical Computation and Optimization, Introduction to Machine Learning Packages |
Module 4 | Linear and Logistic Regression – Bias/Variance Tradeoff, Regularization, Variants of Gradient Descent, MLE, MAP, Applications |
Module 5 | Neural Networks – Multilayer Perceptron, Backpropagation, Applications |
Module 6 | Convolutional Neural Networks 1 – CNN Operations, CNN Architectures |
Module 7 | Convolutional Neural Networks 2 – Training, Transfer Learning, Applications |
Module 8 | Recurrent Neural Networks (RNN), LSTM, GRU, Applications |
Module 9 | Classical Techniques 1 – Bayesian Regression, Binary Trees, Random Forests, SVM, Naïve Bayes, Applications |
Module 10 | Classical Techniques 2 – k-Means, kNN, GMM, Expectation Maximization, Applications |
Name | Mr.Raman. Raguraman |
Qualifications | Chartered Engineer - Awarded by the Engineering Council UK. M. Eng in Power Systems. Certification in Machine Learning. Certification in Essential Mathematics for Machine Learning. Certification in Deep Learning from IIT Madras |
Department | Faculty of Engineering and Computer Technology |
A Professional Certificate in Machine Learning equips students with knowledge and skills in data analysis, algorithm development, and predictive modeling. Graduates can pursue various roles in data science, artificial intelligence, and analytics across a range of industries. Here are some potential career paths:
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