All current कंप्यूटर विज्ञान courses in 2024
3147 कोर्स
Machine Learning for Cyber Threat & Anomaly Detection
Master ML techniques for cyber threat detection—build models to identify malware, detect fraud, and analyze network traffic using KNN, SVM, and neural networks on real cybersecurity datasets.
Deep Learning: Train Neural Networks and Deploy with Docker
Master the full deep learning pipeline—design and train neural networks with PyTorch and TensorFlow, track experiments, serve models via FastAPI, and deploy scalable apps using Docker.
Foundations of Spec-Driven Development with Codex
Master Spec-Driven Development with Codex—learn when specs add value, build project constitutions, create PRDs, and leverage AI for specification generation and critical review.
Deep Learn Imagery
Master deep learning for satellite imagery—fine-tune CNNs, apply transfer learning, boost performance with data augmentation, and interpret predictions using Grad-CAM for land cover classification.
Advanced Power BI Integration, AI, and Governance Strategies
Master advanced Power BI by integrating AI, Office 365, and data science workflows while implementing governance frameworks, data protection strategies, and capacity management for enterprise-grade BI solutions.
Google DeepMind: Discover The Transformer Architecture
In this Google DeepMind course you will discover the mechanisms of the transformer architecture.
Google DeepMind: Fine-Tune Your Model
Unleash the power of language models with fine-tuning. In this course, you will learn how to adjust a pre-trained model to a specific task.
Google DeepMind: Accelerate Your Model
Train more powerful models with a single GPU, learn how hardware can speed up model training and the key considerations when training models on a GPU.
Outlining Contract Management Reporting
Master SAP S/4HANA contract management reporting by adjusting list layouts, using embedded analytics, and exploring AI tools like Joule for real estate contract analysis.
Deep Learning in Electronic Health Records
Explore deep learning principles and architectures applied to Electronic Health Records, covering CNNs, RNNs, imputation techniques, and EHR encodings using MIMIC-III data for clinical prediction.
Clinical Decision Support Systems
Explore key concepts in Clinical Decision Support Systems, including ML evaluation, fairness assessment, decision curve analysis, explainability, and privacy concerns in deep learning models.
Capstone Assignment
Apply explainable AI techniques—LIME, Grad-CAM, and permutation importance—on real MIMIC-III clinical data to build transparent, trustworthy deep learning models for healthcare decision support.