network layer service models forwarding versus routing how a router works routing (path selection) dealing with

network layer service models forwarding versus routing how a router works routing (path selection) dealing with

• 2.
  Network Layer 4-2 Chapter4: Network Layer Chapter goals: • understand principles behind network layer services: • network layer service models • forwarding versus routing • how a router works • routing (path selection) • dealing with scale • advanced topics: IPv6, mobility • instantiation, implementation in the Internet
• 3.
  Supervised, semi supervised learning, clustering, classification, regression,SVM, Big data basics, its components and Hadoop installations
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  Machine learning (ML)is a subdomain of artificial intelligence (AI) that focuses on developing systems that learn or improve performance—based on the data they ingest. Artificial intelligence is a broad word that refers to systems or machines that resemble human intelligence. What is Machine Learning? • Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect.
• 5.
  Features of Machinelearning • Machine learning is data driven technology. Large amount of data generated by organizations on daily bases. So, by notable relationships in data, organizations makes better decisions. • Machine can learn itself from past data and automatically improve. • From the given dataset it detects various patterns on data. • For the big organizations branding is important and it will become more easy to target relatable customer base. • It is similar to data mining because it is also deals with the huge amount of data.
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  Properties ML • Learningfrom Data: Machine learning algorithms learn patterns and relationships from data rather than being explicitly programmed to perform a task. • Adaptability: Machine learning models can adapt and improve their performance over time as they are exposed to more data. • Automation: Once trained, machine learning models can automate the process of making predictions or decisions based on new data. • Generalization: Machine learning models aim to generalize patterns from the training data to make predictions or decisions on new, unseen data. • Scalability: Machine learning methods can often scale to handle large amounts of data and complex tasks. • Robustness: Well-designed machine learning models can be robust to noise and outliers in the data, making them suitable for real-world applications.
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  Available ML Algorithms
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  Evaluation Metrics Confusion Matrix(N X N matrix) It is extremely useful for measuring precision-recall, Specificity, Accuracy, and most importantly, AUC-ROC curves.
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  Accuracy: The proportionof correctly classified instances out of the total instances. Precision / positive predictive value : It measures the proportion of true positive predictions among all positive predictions made by the model. TP (TP + FP) Recall (Sensitivity): It measures the proportion of true positives that were correctly identified by the model out of all actual positives. TP (TP + FN) Recall is important when the cost of false negatives is high..
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  F1 Score: Theharmonic mean of precision and recall. It provides a balance between precision and recall. It is calculated as 2 * (Precision * Recall) (Precision + Recall) Specificity: It measures the proportion of true negatives that were correctly identified by the model out of all actual negatives. TN (TN + FP) ROC AUC (Receiver Operating Characteristic Area Under the Curve): It measures the area under the ROC curve sensitivity 1-specificity
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  Mean Absolute Error(MAE): The average of the absolute differences between predicted and actual values. Mean Squared Error (MSE): The average of the squared differences between predicted and actual values. Root Mean Squared Error (RMSE): The square root of the MSE. R-squared (Coefficient of Determination): It measures the proportion of the variance in the dependent variable that is predictable from the independent variables.
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  Algorithms Evaluation Metrics Regression MeanAbsolute Error, Mean Squared Error, Root Mean Squared Error, R-squared, Mean Absolute Percentage Error ,Median Absolute Error Classification Accuracy, Precision, Recall (Sensitivity), F1- score, ROC-AUC, Precision- Recall Curve, Confusion Matrix Especially in binary classification Precision, Recall, and F1- score Clustering Metrics Silhouette Score, Calinski-Harabasz Index, Davies-Bouldin Index, Adjusted Rand Index (ARI)
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  What is Supervisedlearning? • Supervised learning, as the name indicates, has the presence of a supervisor as a teacher. Supervised learning is when we teach or train the machine using data that is well-labelled. Which means some data is already tagged with the correct answer. After that, the machine is provided with a new set of examples(data) so that the supervised learning algorithm analyses the training data(set of training examples) and produces a correct outcome from labeled data. • For example, a labeled dataset of images of Elephant, Camel and Cow would have each image tagged with either “Elephant”,“Camel”or “Cow.” • Key Points: Supervised learning involves training a machine from labeled data. Labeled data consists of examples with the correct answer or classification. The machine learns the relationship between inputs (fruit images) and outputs (fruit labels).
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  Machine learning, Supervised, semisupervised learning, clustering, classification, regression, SVM, Big data basics, its components and Hadoop installations Machine learning https://www.geeksforgeeks.org/machine-learning/ Supervised : types - classification, regression (also unsupervised is here), clustering https://www.geeksforgeeks.org/supervised-unsupervised-learning/ semi supervised learning, https://www.geeksforgeeks.org/ml-semi-supervised-learning/ SVM : https://www.geeksforgeeks.org/support-vector-machine-algorithm/ Big data basics, its components https://www.geeksforgeeks.org/what-is-big-data/ https://www.geeksforgeeks.org/difference-between-traditional-data-and-big-data/ Hadoop https://www.geeksforgeeks.org/hadoop-an-introduction/ installations https://www.javatpoint.com/hadoop-installation https://medium.com/@DataEngineeer/how-to-set-up-hadoop-on-windows-a-step-by-step-guide-37d1ab4bee57
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  • Types ofUnsupervised Learning