This Specialization equips learners with the practical skills to design, train, and deploy deep learning applications using TensorFlow. Across three project-based courses, learners will explore neural networks, image captioning systems, and real-time face mask detection. They will gain expertise in convolutional and recurrent models, transfer learning, and app deployment with Streamlit and AWS. By the end, learners will be able to create production-ready AI solutions that integrate seamlessly into modern applications, preparing them for careers in machine learning engineering and applied AI.
AI Deep Learning Projects with TensorFlow Specialization
AI Deep Learning Projects with TensorFlow Specialization
Master Deep Learning Projects with TensorFlow. Build, train, and deploy real-world AI solutions using TensorFlow and Streamlit.
Instructor: EDUCBA
Included with 
Get in-depth knowledge of a subject
Beginner level
Recommended experience
1 month at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
Get in-depth knowledge of a subject
Beginner level
Recommended experience
1 month at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
What you'll learn
Apply TensorFlow to build, train, and optimize deep learning models for real-world datasets.
Integrate computer vision and NLP by developing image captioning applications with deployment.
Implement and deploy AI-powered detection systems, preparing for machine learning engineering roles.
Overview
What’s included
Shareable certificate
Add to your LinkedIn profile
Taught in English
Recently updated!
October 2025
20 practice exercises
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from EDUCBA
Specialization - 3 course series
What you'll learn
Build and train neural networks in TensorFlow with parameter initialization.
Implement CNNs for image processing and real-world dataset classification.
Apply transfer learning to adapt pre-trained models for specialized tasks.
Skills you'll gain
Deep Learning, Tensorflow, Keras (Neural Network Library), Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Data Processing, Computer Vision, Image Analysis, and Artificial Neural Networks
What you'll learn
Preprocess image/text datasets with tokenization and feature extraction.
Build CNN-RNN models and evaluate performance with BLEU scores.
Deploy a Streamlit image captioning app on AWS EC2 for real-world use.
Skills you'll gain
Application Deployment, Applied Machine Learning, Application Development, Image Analysis, Computer Vision, Deep Learning, Tensorflow, Feature Engineering, Data Processing, and Amazon Elastic Compute Cloud
What you'll learn
Install and configure TensorFlow for model building and execution.
Train, save, and deploy linear and deep learning models.
Apply TensorFlow and Keras to a real-world face mask detection project.
Skills you'll gain
Tensorflow, Keras (Neural Network Library), System Requirements, Image Analysis, Computer Vision, Artificial Intelligence and Machine Learning (AI/ML), Software Installation, Applied Machine Learning, Supervised Learning, and Deep Learning
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
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Frequently asked questions
The Specialization can typically be completed within 6 to 7 weeks, with an estimated commitment of 3–4 hours per week. This flexible pacing allows learners to balance their studies alongside professional or academic responsibilities. By following the structured sequence of hands-on projects and guided lessons, learners steadily build both theoretical understanding and practical expertise in TensorFlow-based deep learning. The timeline ensures consistent progress while providing ample opportunity to practice, implement, and refine newly acquired skills for real-world applications.
Learners are expected to have a basic understanding of Python programming and foundational concepts in machine learning. Prior exposure to linear algebra, probability, or neural networks is helpful but not strictly required, as core principles are reinforced throughout the courses.
Yes, the courses are designed to be taken in sequence for a progressive learning experience. Each course builds on the concepts and skills from the previous one, starting with fundamental TensorFlow models, advancing into image captioning, and culminating in real-world deployment projects.
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