This Specialization provides an end-to-end, hands-on learning experience in building and deploying deep learning models using Keras and TensorFlow. Learners will work on real-world projects in chatbot development, sentiment analysis, image classification, and face recognition. Each course guides participants from data preprocessing to advanced neural network architectures, emphasizing model optimization, evaluation, and deployment. By completing the program, learners will gain job-ready AI skills applicable across NLP, computer vision, and applied machine learning domains.
Keras Deep Learning Projects with TensorFlow Specialization
Keras Deep Learning Projects with TensorFlow Specialization
Build AI Models with Keras and TensorFlow. Develop, train, and optimize deep learning models for NLP and computer vision projects.
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
Design and implement deep learning models using Keras and TensorFlow for NLP and vision tasks.
Apply preprocessing, embedding, and evaluation techniques to optimize neural network performance.
Build, train, and deploy AI applications including chatbots, sentiment analyzers, and recognition systems.
Overview
What’s included
Shareable certificate
Add to your LinkedIn profile
Taught in English
Recently updated!
October 2025
28 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 - 4 course series
What you'll learn
Apply preprocessing and vectorization in NLP.
Build ML and neural chatbot models with Keras.
Evaluate and optimize conversational AI systems.
Skills you'll gain
Data Processing, Deep Learning, Generative AI, Performance Testing, Tensorflow, Generative Model Architectures, Feature Engineering, Data Transformation, Artificial Neural Networks, Data Cleansing, Keras (Neural Network Library), Natural Language Processing, Text Mining, and Machine Learning Methods
What you'll learn
Preprocess and tokenize text for sentiment analysis.
Build and train LSTM models using Keras.
Evaluate and visualize model performance in Colab.
Skills you'll gain
Text Mining, Applied Machine Learning, Keras (Neural Network Library), Data Processing, Natural Language Processing, Deep Learning, Jupyter, Artificial Neural Networks, and Python Programming
What you'll learn
Build and train CNN models with Keras in Colab.
Apply transfer learning and image augmentation.
Visualize layers and retrain models for accuracy.
Skills you'll gain
Data Processing, Google Cloud Platform, Computer Vision, Applied Machine Learning, Image Analysis, Visualization (Computer Graphics), Deep Learning, Machine Learning Methods, Keras (Neural Network Library), and Tensorflow
What you'll learn
Detect and preprocess facial images using MTCNN.
Generate embeddings and train models with FaceNet.
Build and evaluate real-world face recognition systems.
Skills you'll gain
Systems Development, Tensorflow, Artificial Neural Networks, Computer Vision, Python Programming, Supervised Learning, Keras (Neural Network Library), Deep Learning, Image Analysis, Application Development, and Applied Machine 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 Keras Deep Learning Projects with TensorFlow Specialization can typically be completed in approximately 7 to 8 weeks, with a recommended commitment of 3–4 hours per week. This flexible, self-paced structure allows learners to progress through each project systematically—building, training, and optimizing neural network models while developing both conceptual understanding and hands-on expertise. The pacing is designed to balance in-depth learning with practical implementation for real-world readiness.
Learners should have a foundational understanding of Python programming, basic knowledge of machine learning concepts, and familiarity with data handling using libraries such as NumPy or pandas. Prior exposure to deep learning or neural networks will be beneficial but is not mandatory.
Yes. The courses are designed in a progressive sequence that builds upon previously learned skills. Starting with foundational projects in NLP and sentiment analysis, learners then advance to more complex applications in computer vision and face recognition. Completing them in order ensures a smooth and cohesive learning experience.
More questions
Financial aid available,