Certificate in Practical Machine Learning for Quality
-- ViewingNowThe Certificate in Practical Machine Learning for Quality is a comprehensive course designed to equip learners with essential skills in machine learning. This program emphasizes the application of machine learning techniques to improve quality in various industries, making it highly relevant and in-demand in today's data-driven world.
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• Introduction to Machine Learning: Basic concepts, types, and applications of machine learning. Understanding supervised, unsupervised, and reinforcement learning.
• Data Preprocessing: Data cleaning, wrangling, and visualization. Feature scaling, normalization, and selection.
• Regression Analysis: Linear and logistic regression. Regularization techniques (L1, L2). Model evaluation metrics.
• Classification Techniques: Decision trees, random forests, support vector machines (SVMs), and k-nearest neighbors (KNN). Overfitting and underfitting.
• Unsupervised Learning: Clustering algorithms (k-means, hierarchical clustering), dimensionality reduction (PCA, t-SNE).
• Neural Networks: Introduction to artificial neural networks (ANNs), deep learning, and backpropagation.
• Convolutional Neural Networks (CNNs): Image classification, object detection, and semantic segmentation.
• Natural Language Processing (NLP): Text preprocessing, sentiment analysis, and topic modeling.
• Evaluation Metrics: Bias, variance, accuracy, precision, recall, F1 score, ROC curve, AUC, and cross-validation.
• Machine Learning in Quality Control: Process optimization, anomaly detection, predictive maintenance, and real-time quality control.
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- BasicUnderstandingSubject
- ProficiencyEnglish
- ComputerInternetAccess
- BasicComputerSkills
- DedicationCompleteCourse
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- ThreeFourHoursPerWeek
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