https://speakerdeck.com/madhuri28 2026-09-16 04:13:51 -0400 Explores machine learning in smart factories, including predictive maintenance, automated inspection, process optimization, energy management, Industrial IoT, digital twins, and real-world Industry 4.0 case studies. Explores machine learning in smart factories, including predictive maintenance, automated inspection, process optimization, energy management, Industrial IoT, digital twins, and real-world Industry 4.0 case studies. Mon, 05 Oct 2026 00:00:00 -0400 https://speakerdeck.com/madhuri28/smart-manufacturing-and-industrial-automation-ml-digital-twins-and-industry-4-dot-0 https://speakerdeck.com/madhuri28/smart-manufacturing-and-industrial-automation-ml-digital-twins-and-industry-4-dot-0 Covers condition monitoring, predictive maintenance workflows, sensor data analysis, failure prediction, anomaly detection, remaining useful life estimation, evaluation metrics, and industrial machine case studies. Covers condition monitoring, predictive maintenance workflows, sensor data analysis, failure prediction, anomaly detection, remaining useful life estimation, evaluation metrics, and industrial machine case studies. Mon, 05 Oct 2026 00:00:00 -0400 https://speakerdeck.com/madhuri28/predictive-maintenance-and-condition-monitoring-using-machine-learning https://speakerdeck.com/madhuri28/predictive-maintenance-and-condition-monitoring-using-machine-learning Explains how machine learning enables intelligent robots and autonomous systems through object recognition, navigation, localization, robotic grasping, reinforcement learning, and practical industrial robotics case studies. Explains how machine learning enables intelligent robots and autonomous systems through object recognition, navigation, localization, robotic grasping, reinforcement learning, and practical industrial robotics case studies. Mon, 05 Oct 2026 00:00:00 -0400 https://speakerdeck.com/madhuri28/intelligent-robotics-and-autonomous-systems-machine-learning-applications https://speakerdeck.com/madhuri28/intelligent-robotics-and-autonomous-systems-machine-learning-applications Explores machine vision and machine learning for automated quality inspection, covering image preprocessing, feature extraction, defect classification, object detection, anomaly detection, and industrial case studies. Explores machine vision and machine learning for automated quality inspection, covering image preprocessing, feature extraction, defect classification, object detection, anomaly detection, and industrial case studies. Mon, 05 Oct 2026 00:00:00 -0400 https://speakerdeck.com/madhuri28/machine-vision-for-automated-inspection-ml-based-quality-control https://speakerdeck.com/madhuri28/machine-vision-for-automated-inspection-ml-based-quality-control An introductory guide to computer vision and machine learning using Python, covering image processing with OpenCV, ML workflows, regression, classification, clustering, and model evaluation for mechatronics applications. An introductory guide to computer vision and machine learning using Python, covering image processing with OpenCV, ML workflows, regression, classification, clustering, and model evaluation for mechatronics applications. Mon, 05 Oct 2026 00:00:00 -0400 https://speakerdeck.com/madhuri28/introduction-to-opencv-and-scikit-learn https://speakerdeck.com/madhuri28/introduction-to-opencv-and-scikit-learn Covers hierarchical clustering, agglomerative and divisive approaches, distance measures, linkage methods, dendrogram interpretation, cluster selection, Python implementation, model parameters, and a step-by-step numerical example. Covers hierarchical clustering, agglomerative and divisive approaches, distance measures, linkage methods, dendrogram interpretation, cluster selection, Python implementation, model parameters, and a step-by-step numerical example. Mon, 05 Oct 2026 00:00:00 -0400 https://speakerdeck.com/madhuri28/hierarchical-clustering-agglomerative-clustering-and-dendrogram-analysis https://speakerdeck.com/madhuri28/hierarchical-clustering-agglomerative-clustering-and-dendrogram-analysis Introduces unsupervised learning and clustering, covering clustering concepts, applications, similarity measures, dimensionality reduction, association rules, anomaly detection, classification vs. clustering, major clustering algorithms, and key challenges. Introduction_to_Clustering Introduces unsupervised learning and clustering, covering clustering concepts, applications, similarity measures, dimensionality reduction, association rules, anomaly detection, classification vs. clustering, major clustering algorithms, and key challenges. Introduction_to_Clustering Mon, 05 Oct 2026 00:00:00 -0400 https://speakerdeck.com/madhuri28/introduction-to-clustering-unsupervised-learning-and-clustering-algorithms https://speakerdeck.com/madhuri28/introduction-to-clustering-unsupervised-learning-and-clustering-algorithms Explains DBSCAN through density-based clustering, ε-neighborhood, core, border and noise points, density reachability, parameter selection, K-distance graph, feature scaling, Python implementation, and predictive-maintenance applications Explains DBSCAN through density-based clustering, ε-neighborhood, core, border and noise points, density reachability, parameter selection, K-distance graph, feature scaling, Python implementation, and predictive-maintenance applications Mon, 05 Oct 2026 00:00:00 -0400 https://speakerdeck.com/madhuri28/k-means-clustering-algorithm-centroid-based-unsupervised-learning-6eb4b634-5569-4a69-8161-6c0efa0c24ef https://speakerdeck.com/madhuri28/k-means-clustering-algorithm-centroid-based-unsupervised-learning-6eb4b634-5569-4a69-8161-6c0efa0c24ef Explains K-Means clustering through centroids, distance-based assignment, iterative centroid updates, WCSS, K-Means++ initialization, numerical example, Elbow Method, Silhouette Score, feature scaling, limitations, and Python implementation. Explains K-Means clustering through centroids, distance-based assignment, iterative centroid updates, WCSS, K-Means++ initialization, numerical example, Elbow Method, Silhouette Score, feature scaling, limitations, and Python implementation. Mon, 05 Oct 2026 00:00:00 -0400 https://speakerdeck.com/madhuri28/k-means-clustering-algorithm-centroid-based-unsupervised-learning https://speakerdeck.com/madhuri28/k-means-clustering-algorithm-centroid-based-unsupervised-learning Introduces ensemble learning, base learners, prediction combination, bagging, bootstrap sampling, and boosting, explaining how multiple models improve prediction stability, reduce variance, and build stronger learners. Introduces ensemble learning, base learners, prediction combination, bagging, bootstrap sampling, and boosting, explaining how multiple models improve prediction stability, reduce variance, and build stronger learners. Wed, 30 Sep 2026 00:00:00 -0400 https://speakerdeck.com/madhuri28/ensemble-learning-bagging-boosting-and-model-combination https://speakerdeck.com/madhuri28/ensemble-learning-bagging-boosting-and-model-combination Explains the transition from AdaBoost to Gradient Boosting and XGBoost, covering residual-based learning, additive models, learning rate, regularization, tree construction, hyperparameters, and Python implementation. Explains the transition from AdaBoost to Gradient Boosting and XGBoost, covering residual-based learning, additive models, learning rate, regularization, tree construction, hyperparameters, and Python implementation. Wed, 30 Sep 2026 00:00:00 -0400 https://speakerdeck.com/madhuri28/xgboost-extreme-gradient-boosting-for-efficient-machine-learning https://speakerdeck.com/madhuri28/xgboost-extreme-gradient-boosting-for-efficient-machine-learning Covers Random Forest using bootstrap sampling and random feature selection, including its working, advantages, limitations, hyperparameters, Out-of-Bag evaluation, feature importance, and Python implementation. Covers Random Forest using bootstrap sampling and random feature selection, including its working, advantages, limitations, hyperparameters, Out-of-Bag evaluation, feature importance, and Python implementation. Wed, 30 Sep 2026 00:00:00 -0400 https://speakerdeck.com/madhuri28/random-forest-ensemble-decision-trees-with-random-sampling https://speakerdeck.com/madhuri28/random-forest-ensemble-decision-trees-with-random-sampling Explains AdaBoost through weak learners, sample weighting, learner error, learner weights, adaptive learning, weighted voting, mathematical formulation, Python implementation, hyperparameters, and common misconceptions. Explains AdaBoost through weak learners, sample weighting, learner error, learner weights, adaptive learning, weighted voting, mathematical formulation, Python implementation, hyperparameters, and common misconceptions. Wed, 30 Sep 2026 00:00:00 -0400 https://speakerdeck.com/madhuri28/adaboost-adaptive-boosting-and-weighted-learning https://speakerdeck.com/madhuri28/adaboost-adaptive-boosting-and-weighted-learning An introductory guide to building Python GUI applications using Tkinter, covering widgets, window properties, event handling, layout management, and practical mechatronics-oriented GUI examples. An introductory guide to building Python GUI applications using Tkinter, covering widgets, window properties, event handling, layout management, and practical mechatronics-oriented GUI examples. Tue, 29 Sep 2026 00:00:00 -0400 https://speakerdeck.com/madhuri28/gui-development-with-tkinter https://speakerdeck.com/madhuri28/gui-development-with-tkinter An introductory guide to Matplotlib covering basic plotting, graph customization, multiple data series, scatter plots, histograms, and real-time sensor data visualization for mechatronics applications. An introductory guide to Matplotlib covering basic plotting, graph customization, multiple data series, scatter plots, histograms, and real-time sensor data visualization for mechatronics applications. Tue, 29 Sep 2026 00:00:00 -0400 https://speakerdeck.com/madhuri28/data-visualization-with-matplotlib https://speakerdeck.com/madhuri28/data-visualization-with-matplotlib A practical introduction to serial communication between Python and microcontrollers using pySerial, covering connection setup, data transmission, sensor data acquisition, and troubleshooting. A practical introduction to serial communication between Python and microcontrollers using pySerial, covering connection setup, data transmission, sensor data acquisition, and troubleshooting. Tue, 29 Sep 2026 00:00:00 -0400 https://speakerdeck.com/madhuri28/python-mcu-interfacing-using-pyserial https://speakerdeck.com/madhuri28/python-mcu-interfacing-using-pyserial A practical introduction to HTTP APIs, covering client-server communication, HTTP methods, Python requests, JSON responses, status codes, API errors, and exception handling. A practical introduction to HTTP APIs, covering client-server communication, HTTP methods, Python requests, JSON responses, status codes, API errors, and exception handling. Tue, 22 Sep 2026 00:00:00 -0400 https://speakerdeck.com/madhuri28/http-apis-in-python https://speakerdeck.com/madhuri28/http-apis-in-python This lecture introduces Random Forest, an ensemble learning algorithm that combines multiple Decision Trees for reliable predictions. It explains bootstrap sampling, random feature selection, and the working of Random Forest. The presentation also covers hyperparameters, Out-of-Bag (OOB) evaluation, and feature importance. A Python implementation is included to demonstrate practical use of the algorithm. This lecture introduces Random Forest, an ensemble learning algorithm that combines multiple Decision Trees for reliable predictions. It explains bootstrap sampling, random feature selection, and the working of Random Forest. The presentation also covers hyperparameters, Out-of-Bag (OOB) evaluation, and feature importance. A Python implementation is included to demonstrate practical use of the algorithm. Wed, 16 Sep 2026 00:00:00 -0400 https://speakerdeck.com/madhuri28/random-forest-from-decision-trees-to-ensemble-learning https://speakerdeck.com/madhuri28/random-forest-from-decision-trees-to-ensemble-learning