Meta Front-End Developer Professional Certificate
Learn to create responsive websites using HTML, CSS, JavaScript, and React, utilize the Bootstrap framework, collaborate with GitHub, and prepare for coding interviews with portfolio-ready projects.
Description for Meta Front-End Developer Professional Certificate
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by Meta
Duration: 7 months; approx. 6 hours a week
Schedule: Flexible
Pricing for Meta Front-End Developer Professional Certificate
Use Cases for Meta Front-End Developer Professional Certificate
FAQs for Meta Front-End Developer Professional Certificate
Reviews for Meta Front-End Developer Professional Certificate
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Meta Front-End Developer Professional Certificate
Replicate is a cloud platform simplifying machine learning model execution, offering a wide range of models, scalable API deployment, and usage-based billing to minimize costs.
Acquire practical full stack development skills, knowledge of Cloud Native tools, proficiency in front-end development languages, and build a GitHub portfolio through hands-on tasks and a capstone project.
The course outlines a comprehensive curriculum aimed at equipping learners with technical skills in back-end development, covering various programming systems, portfolio development, and interview preparation.
Utilize TensorFlow.js for browser-based model execution, TensorFlow Lite for mobile deployment, TensorFlow Data Services for optimized data management, and TensorFlow Hub, Serving, and TensorBoard for advanced deployment scenarios.
Provides a hands-on approach to implementing machine learning with JavaScript and TensorFlow.js for a variety of applications.
Explore the topic of AI-powered personalization by acquiring the skills necessary to utilize LangChain and ChatGPT.
Featured Tools
Gain proficiency in the automation of software development processes through the utilization of generative artificial intelligence, AI-assisted programming, MLOps, and Amazon Web Services.
Learn to develop and implement custom GPTs for various industries to enhance productivity and innovation.
Gain practical skills in relational and NoSQL databases, Big Data tools, and data pipelines for comprehensive data engineering tasks.
This course teaches aspiring data scientists to train and compare classification models using supervised machine learning techniques, focusing on practical applications and best practices.
Gain practical experience in AI and Machine Learning for business, focusing on data extraction, feature engineering, outlier management, and feature scaling for aspiring data scientists with foundational math and Python skills.