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## Essential Units for Masterclass Certificate in Textile Data Mining
**• Introduction to Data Mining**
* Definition of data mining and its applications in textile industry
* Types of data used in textile data mining
* Data quality and cleaning techniques
**• Data Preprocessing**
* Data wrangling and transformation
* Feature engineering and feature selection
* Handling missing values and outliers
**• Feature Selection and Dimensionality Reduction**
* Feature selection methods (correlation-based, wrapper methods)
* Dimensionality reduction techniques (PCA, t-SNE)
**• Data Mining Techniques**
* Supervised learning algorithms (linear regression, decision trees)
* Unsupervised learning algorithms (k-means, hierarchical clustering)
* Dimensionality reduction techniques
**• Evaluation and Interpretation**
* Metrics for model evaluation (accuracy, precision, recall)
* Interpreting model results and drawing insights from data
**• Case Studies in Textile Data Mining**
* Analyzing real-world textile datasets
* Identifying patterns and trends in textile data
* Building predictive models for various textile applications
**• Ethical Considerations in Data Mining**
* Data privacy and security
* Bias and fairness in data mining
* Responsible data mining practices
**• Advanced Topics in Textile Data Mining**
* Natural language processing in textile data
* Text mining and sentiment analysis
* Recommender systems for personalized textile recommendations
**• Tools and Technologies for Textile Data Mining**
* Introduction to Python programming language
* Data wrangling libraries (pandas, NumPy)
* Feature engineering libraries (scikit-learn)
* Dimensionality reduction libraries (PCA, t-SNE)
* Machine learning libraries (scikit-learn, TensorFlow)