Maninda Edirisooriya

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Lecture - 10 Transformer Model, Motivation to Transformers, Principles, and Design of Transformer model

Lecture 11 - Advance Learning Techniques

Lecture 9 - Deep Sequence Models, Learn Recurrent Neural Networks (RNN), GRU and LSTM networks and their architecture.

Extra Lecture - Support Vector Machines (SVM), a lecture in subject module Statistical & Machine Learning

Lecture 11 - KNN and Clustering, a lecture in subject module Statistical & Machine Learning

Lecture 10 - Model Testing and Evaluation, a lecture in subject module Statistical & Machine Learning

Lecture 9 - Decision Trees and Ensemble Methods, a lecture in subject module Statistical & Machine Learning

Lecture 8 - Feature Engineering and Optimization, a lecture in subject module Statistical & Machine Learning

Lecture 7 - Bias, Variance and Regularization, a lecture in subject module Statistical & Machine Learning

Lecture 6 - Logistic Regression, a lecture in subject module Statistical & Machine Learning

Lecture 5 - Gradient Descent, a lecture in subject module Statistical & Machine Learning

Lecture 4 - Linear Regression, a lecture in subject module Statistical & Machine Learning

Lecture 3 - Exploratory Data Analytics (EDA), a lecture in subject module Statistical & Machine Learning

Lecture 2 - Introduction to Machine Learning, a lecture in subject module Statistical & Machine Learning

Analyzing the effectiveness of mobile and web channels using WSO2 BAM