Overview

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.

Syllabus

Course 1: Deep Learning with TensorFlow: Build Neural Networks
Course 2: Image Captioning with TensorFlow & Streamlit
Course 3: TensorFlow: Build & Deploy Face Mask Detection

Courses

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5 hours 19 minutes
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By the end of this course, learners will be able to explain the fundamentals of neural networks, apply TensorFlow to build and train models, implement convolutional neural networks for image processing, and adapt transfer learning strategies for real-world applications. This course is designed to help learners bridge the gap between theory and practice in deep learning. Starting with perceptrons and core neural network principles, participants will gain hands-on experience in building models, initializing parameters effectively, and processing image data through CNNs. Moving forward, they will learn to classify real-world datasets like dogs vs. cats and master advanced transfer learning techniques to optimize pre-trained models for specialized tasks. Unlike other tutorials, this course uniquely combines step-by-step TensorFlow implementation with conceptual clarity, ensuring learners not only follow code but also understand the reasoning behind each decision. Whether aiming to enhance AI career prospects or apply deep learning in projects, learners will leave equipped with the skills to design, train, and deploy robust neural network models confidently.
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4 hours 46 minutes
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By completing this course, learners will be able to preprocess image and text datasets, build and evaluate a deep learning model, and deploy a fully functional image captioning application. They will gain hands-on experience in applying tokenization, feature extraction, CNN-RNN architectures, and BLEU score evaluation for accurate caption generation. This course uniquely bridges computer vision and natural language processing, enabling learners to generate meaningful captions for social media images. Unlike traditional AI tutorials, it not only covers dataset preparation and neural network modeling but also demonstrates how to create an interactive Streamlit app and deploy it on AWS EC2 for real-world accessibility. Learners benefit by acquiring both technical depth and practical deployment skills, preparing them for roles in AI development, machine learning engineering, and applied data science. By the end, they will confidently design, test, and launch their own automatic image captioning systems that integrate seamlessly into modern applications.
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4 hours 11 minutes
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Learners completing this course will be able to install and configure TensorFlow, create and execute variables, implement linear models, and apply deep learning frameworks to real-world projects. They will also gain practical skills in training, saving, and deploying models for computer vision tasks such as face mask detection. This course begins by building strong foundations in TensorFlow, guiding learners through installation, setup, data types, variables, and model execution. Once comfortable with the basics, learners progress to a hands-on project—implementing a face mask detection application using TensorFlow and Keras. The project-based approach ensures that learners not only understand theoretical concepts but also gain experience applying them in a real-world scenario. By the end of the course, learners will have mastered the essential workflow of building and training models, leveraging pretrained networks, and making predictions with confidence. The combination of foundational concepts and applied project work makes this course unique, offering both academic rigor and industry relevance. Whether preparing for a career in AI or enhancing existing skills, this course provides the knowledge and practice required to thrive in machine learning projects.