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ASP.NET Core MVC [.NET 8] - The Complete Guide
This course begins with an introduction, setting you up with essential tools and a clear roadmap for your learning journey. Delve into the fundamentals of .NET Core, understanding the MVC architecture, routing intricacies, and dependency injection.
As the course progresses, you'll dive into Category and Product CRUD operations, gaining hands-on experience with database connectivity, Entity Framework Core, and dynamic UI development. We will learn the nuances of Razor Projects and the significance of N-Tier Architecture, enhancing your understanding of scalable and maintainable code structures. The Repository Pattern module deepens your grasp of efficient data handling, leading to seamless database transitions. We will also cover shopping cart implementation, order processing, and user management. You'll tackle advanced features like product image handling, social logins, and interactive UI elements, ensuring your e-commerce platform stands out.
As you approach the course's climax, the focus shifts to crucial aspects of user authentication, role management, and secure user experiences with .NET Core Identity. The final modules guide you through deploying your application, integrating email services with SendGrid, and refining your project for real-world use.
As the course progresses, you'll dive into Category and Product CRUD operations, gaining hands-on experience with database connectivity, Entity Framework Core, and dynamic UI development. We will learn the nuances of Razor Projects and the significance of N-Tier Architecture, enhancing your understanding of scalable and maintainable code structures. The Repository Pattern module deepens your grasp of efficient data handling, leading to seamless database transitions. We will also cover shopping cart implementation, order processing, and user management. You'll tackle advanced features like product image handling, social logins, and interactive UI elements, ensuring your e-commerce platform stands out.
As you approach the course's climax, the focus shifts to crucial aspects of user authentication, role management, and secure user experiences with .NET Core Identity. The final modules guide you through deploying your application, integrating email services with SendGrid, and refining your project for real-world use.
209 الخطوات
محتوى جديد
Machine Learning, Data Science and Generative AI with Python
This course begins with a Python crash course and then guides you on setting up Microsoft Windows-based PCs, Linux desktops, and Macs. After the setup, we delve into machine learning, AI, and data mining techniques, which include deep learning and neural networks with TensorFlow and Keras; generative models with variational autoencoders and generative adversarial networks; data visualization in Python with Matplotlib and Seaborn; transfer learning, sentiment analysis, image recognition, and classification; regression analysis, K-Means Clustering, Principal Component Analysis, training/testing and cross-validation, Bayesian methods, decision trees, and random forests.
Additionally, we will cover multiple regression, multilevel models, support vector machines, reinforcement learning, collaborative filtering, K-Nearest Neighbors, the bias/variance tradeoff, ensemble learning, term frequency/inverse document frequency, experimental design, and A/B testing, feature engineering, hyperparameter tuning, and much more! There's a dedicated section on machine learning with Apache Spark to scale up these techniques to "big data" analyzed on a computing cluster.
The course will cover the Transformer architecture, delve into the role of self-attention in AI, explore GPT applications, and practice fine-tuning Transformers for tasks such as movie review analysis. Furthermore, we will look at integrating the OpenAI API for ChatGPT, creating with DALL-E, understanding embeddings, and leveraging audio-to-text to enhance AI with real-world data and moderation.
Additionally, we will cover multiple regression, multilevel models, support vector machines, reinforcement learning, collaborative filtering, K-Nearest Neighbors, the bias/variance tradeoff, ensemble learning, term frequency/inverse document frequency, experimental design, and A/B testing, feature engineering, hyperparameter tuning, and much more! There's a dedicated section on machine learning with Apache Spark to scale up these techniques to "big data" analyzed on a computing cluster.
The course will cover the Transformer architecture, delve into the role of self-attention in AI, explore GPT applications, and practice fine-tuning Transformers for tasks such as movie review analysis. Furthermore, we will look at integrating the OpenAI API for ChatGPT, creating with DALL-E, understanding embeddings, and leveraging audio-to-text to enhance AI with real-world data and moderation.
132 الخطوات