Machine Learning in Software Engineering
( 25 Modules )

Module #1
Introduction to Machine Learning
Overview of machine learning, types of machine learning, and its applications in software engineering

Module #2
Mathematical Foundations of Machine Learning
Linear algebra, calculus, probability, and statistics for machine learning

Module #3
Python for Machine Learning
Introduction to Python, NumPy, Pandas, and scikit-learn for machine learning

Module #4
Supervised Learning
Introduction to supervised learning, regression, and classification

Module #5
Linear Regression
Simple and multiple linear regression, cost function, and gradient descent

Module #6
Logistic Regression
Logistic regression, sigmoid function, and binary classification

Module #7
Decision Trees
Introduction to decision trees, CART algorithm, and tree pruning

Module #8
Ensemble Learning
Introduction to ensemble learning, random forests, and boosting

Module #9
Unsupervised Learning
Introduction to unsupervised learning, clustering, and dimensionality reduction

Module #10
K-Means Clustering
K-means algorithm, clustering evaluation metrics, and applications

Module #11
Principal Component Analysis (PCA)
Introduction to PCA, dimensionality reduction, and feature extraction

Module #12
Reinforcement Learning
Introduction to reinforcement learning, Markov decision processes, and Q-learning

Module #13
Neural Networks
Introduction to neural networks, perceptron, and multi-layer perceptron

Module #14
Deep Learning
Introduction to deep learning, convolutional neural networks (CNNs), and recurrent neural networks (RNNs)

Module #15
Natural Language Processing (NLP)
Introduction to NLP, text preprocessing, and text classification

Module #16
Machine Learning in Software Engineering
Applications of machine learning in software engineering, defect prediction, and effort estimation

Module #17
Software Quality Prediction
Predicting software quality metrics, such as bugs, faults, and failures

Module #18
Requirement Engineering with Machine Learning
Applications of machine learning in requirement engineering, requirement prioritization, and requirement classification

Module #19
Machine Learning for Testing
Applications of machine learning in software testing, test case generation, and test data generation

Module #20
Machine Learning for Maintenance
Applications of machine learning in software maintenance, bug localization, and code smell detection

Module #21
Explainability and Interpretability of Machine Learning Models
Techniques for explaining and interpreting machine learning models, LIME, and SHAP

Module #22
Machine Learning Ethics
Ethical considerations in machine learning, bias, and fairness

Module #23
Machine Learning Model Deployment
Deploying machine learning models, model serving, and model management

Module #24
Machine Learning in DevOps
Applications of machine learning in DevOps, continuous integration, and continuous deployment

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Machine Learning in Software Engineering career

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Machine Learning in Software Engineering ( 25 Modules )

Machine Learning in Software Engineering
( 25 Modules )