Introduction to Machine Learning for Software Engineers
summary
Overview of core machine learning concepts and workflows tailored for software engineers, including data preparation, model selection, training, evaluation, and deployment. Covers practical tools and technologies such as Python libraries (e.g., scikit-learn, TensorFlow or PyTorch), model validation techniques, and integrating models into production systems.
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chapters (10)
- 0:00Introduction to ML
- 1:25Why machine learning
- 13:40Exploratory data analysis
- 21:40Generalization vs overfitting
- 26:18Training ML models
- 39:05Model performance evaluation
- 50:37Hands-on coding demo
- 58:46Bias and fairness in ML
- 1:05:27Mitigating model bias
- 1:13:20Model deployment & monitoring
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