Prepare to Technical Skills Here are the essential skills that a Machine Learning Engineer needs, as mentioned Read me files. Within each group are topics that you should be familiar with. Study Tip: Copy and paste this list into a document and save to your computer for easy referral. Computer Science Fundamentals and Programming Topics Data structures: Lists, stacks, queues, strings, hash maps, vectors, matrices, classes & objects, trees, graphs, etc. Algorithms: Recursion, searching, sorting, optimization, dynamic programming, etc. Computability and complexity: P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc. Computer architecture: Memory, cache, bandwidth, threads & processes, deadlocks, etc. Probability and Statistics Topics Basic probability: Conditional probability, Bayes rule, likelihood, independence, etc. Probabilistic models: Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc. Statistical measures: Mean, median, mode, variance, population parameters vs. sample statistics etc. Proximity and error metrics: Cosine similarity, mean-squared error, Manhattan and Euclidean distance, log-loss, etc. Distributions and random sampling: Uniform, normal, binomial, Poisson, etc. Analysis methods: ANOVA, hypothesis testing, factor analysis, etc. Data Modeling and Evaluation Topics Data preprocessing: Munging/wrangling, transforming, aggregating, etc. Pattern recognition: Correlations, clusters, trends, outliers & anomalies, etc. Dimensionality reduction: Eigenvectors, Principal Component Analysis, etc. Prediction: Classification, regression, sequence prediction, etc.; suitable error/accuracy metrics. Evaluation: Training-testing split, sequential vs. randomized cross-validation, etc. Applying Machine Learning Algorithms and Libraries Topics Models: Parametric vs. nonparametric, decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc. Learning procedure: Linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods; regularization, hyperparameter tuning, etc. Tradeoffs and gotchas: Relative advantages and disadvantages, bias and variance, overfitting and underfitting, vanishing/exploding gradients, missing data, data leakage, etc. Software Engineering and System Design Topics Software interface: Library calls, REST APIs, data collection endpoints, database queries, etc. User interface: Capturing user inputs & application events, displaying results & visualization, etc. Scalability: Map-reduce, distributed processing, etc. Deployment: Cloud hosting, containers & instances, microservices, etc. Move on to the final lesson of this course to find lots of sample practice questions for each topic!

Anomaly detection algorithm for social networks using Graph Neural Networks by leveraging graph parameteres, between centrality, degree, closeness, on Enron and Twitter datasets

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Design and prototype two advanced anomaly detection approaches for fraud detection — a Graph Neural Network (GNN) model and a Variational Autoencoder (VAE) model — and benchmark them against a traditional gradient-boosted baseline.

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A comprehensive, production-ready implementation of anomaly detection in graphs using various Graph Neural Network architectures. This project provides multiple models, evaluation metrics, and an interactive demo for exploring anomaly detection results.

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To develop a graph neural network-based anomaly detection system capable of identifying subtle, coordinated cyberattacks by analyzing inter-dependency patterns within network traffic data.

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Graph-based Neural Networks (GNNs) are powerful tools for anomaly detection on structured data represented as graphs. Tried to implement a simple Graph Convolutional Network (GCN) for anomaly detection on a synthetic dataset using Pytorch Geometric.

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use NF-BOT-IOT dataset

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To develop a real-time credit card fraud detection system leveraging Graph Neural Networks to identify anomalous transaction patterns based on user spending behavior and transaction network characteristics.

to detect the pishing or anomaly node in the GNN

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To develop a real-time credit card fraud detection system using graph neural networks to identify anomalous transaction patterns and relationships.

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A Relation-aware Fusion Risk Perception Network (RFRP-Net), which integrates graph neural networks with anomaly-aware attention for fine-grained risk modeling and detection