Mendix is a low-code application development platform that enables organizations to build, integrate, and deploy business applications with speed and ease. It allows users to visually design and develop applications using a drag-and-drop interface and pre-built components, reducing the need for manual coding and accelerating the development process. Mendix offers a wide range of features and capabilities, including model-driven development, reusable app templates, seamless integration with existing systems and data sources, collaboration tools, and built-in governance and security measures.
SNS Tech Academy offers comprehensive Mendix online training in Hyderabad, India, catering to individuals looking to master the art of low-code application development. The training program covers a wide array of Mendix concepts, including app design, data modeling, user interface development, business logic implementation, and deployment strategies. Participants engage in hands-on projects, real-world case studies, and personalized mentoring sessions, gaining practical experience in building scalable and innovative business applications. Whether aspiring developers, business analysts, or IT professionals seeking to enhance their skills, learners receive expert guidance and certification preparation to excel in today's competitive job market. SNS Tech Academy's Mendix online training equips individuals with the knowledge and expertise needed to leverage the power of low-code development and drive digital transformation in organizations across diverse industries and domains.
Mendix Online Training course content :-
Introduction to Mendix
How to install Mendix?
Home Page creation
Error Handling
Sales Management Systems Pages Creation
Basics of creating products page, location page, and Sales tracking page.
Domain Model Overview
What exactly is a domain model?
Creating entities for products, location, and Sales tracking
Defining data grid sources
Entities Association and Data Widgets
Attributes and Entities
Tool Box
Microflows
Variable creation
If statement
Microflow debugging
Object creation
Nested Microflows
Microflow as Datasource
XPath
XPath as Datasource
XPath string functions
System variables
Mendix Security
Microflow Security
Entity security
User roles
Advanced Level Domain Model
Rules for validation
Event Handlers
Asynchronous Microflows
Fundamentals of Asynchronous Microflows
Mendix (AI) is a branch of computer science that focuses on creating intelligent machines capable of performing tasks that typically require human intelligence. AI is important because it enables machines to automate complex tasks, make data-driven decisions, and solve problems in various domains, leading to increased efficiency, productivity, and innovation.
The main categories of AI are narrow or weak AI and general or strong AI. Narrow AI is designed to perform specific tasks, such as speech recognition or image classification, within a limited domain. General AI, on the other hand, refers to machines with human-like intelligence and the ability to understand, learn, and apply knowledge across different domains.
Supervised learning involves training a model on labeled data, where the desired output is provided along with the input features. The model learns to make predictions based on this labeled data. Unsupervised learning, on the other hand, involves training a model on unlabeled data, where the algorithm learns patterns and relationships within the data without explicit guidance on the output.
Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers to learn from large amounts of data. Deep learning algorithms can automatically discover and extract features from raw data, whereas traditional machine learning algorithms often require manual feature engineering.
Some popular deep learning frameworks include TensorFlow, PyTorch, Keras, and Caffe. These frameworks provide high-level APIs for building and training deep neural networks, as well as efficient implementations of common deep learning algorithms.
Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. The goal of the agent is to maximize cumulative rewards over time by learning optimal strategies or policies.
Mendix has numerous applications in real life, including virtual assistants (e.g., Siri, Alexa), recommendation systems (e.g., Netflix, Amazon), autonomous vehicles, healthcare diagnostics, fraud detection, language translation, and robotic process automation.
Natural Language Processing (NLP) is a subfield of AI that focuses on enabling computers to understand, interpret, and generate human language. NLP techniques are used in applications such as speech recognition, sentiment analysis, language translation, chatbots, and text summarization.
Ethical considerations in AI include issues related to bias and fairness in algorithms, privacy concerns with data collection and usage, job displacement due to automation, the potential for misuse of AI technologies (e.g., surveillance, autonomous weapons), and accountability and transparency in AI decision-making.
Machine learning model performance can be evaluated using various metrics, depending on the task and type of model. Common evaluation metrics include accuracy, precision, recall, F1 score, mean squared error (MSE), and area under the receiver operating characteristic curve (AUC-ROC). Cross-validation techniques such as k-fold cross-validation are also used to assess model generalization performance.
