Programme Parameters
Foundation
4 Weeks (40 Hrs)
Online (e-LMS)
• No prior experience required — basic computer literacy is sufficient.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Generative AI & LLM Applications with TensorFlow teaches building modern generative systems in Google’s TensorFlow. You learn the generative toolkit — from generative models to working with large language models — implemented in TensorFlow and Keras, and how to build real applications: text generation, assistants and creative tools. The course is hands-on, ending in a deployable generative-AI application. You finish able to build generative-AI and LLM applications in the TensorFlow ecosystem. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers generative AI and LLM applications with TensorFlow — building and deploying generative and language models using the TensorFlow ecosystem.
📋 Course Objectives
1. Build generative models in TensorFlow.
2. Work with large language models.
3. Implement text-generation and assistant apps.
4. Fine-tune and adapt models.
5. Deploy a generative-AI application.
👥 Who Should Enroll?
• Developers building generative AI
• TensorFlow and ML practitioners
• AI product engineers
• Students of generative AI
🚀 Key Learning Outcomes
• The ability to build GenAI apps in TensorFlow.
• A hands-on generative-AI project.
• A TensorFlow-ecosystem skill set.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Foundations of AI Research Projects with TensorFlow
Understand how TensorFlow is used for AI research, model development, experimentation, and real-world implementation. • Learn key concepts such as datasets, features, labels, model architecture, training, validation, prediction, and evaluation. • Explore how AI research projects are planned, structured, documented, and converted into portfolio-ready outcomes.
Python, Data Preparation, and Research Dataset Handling
Prepare research datasets using Python, NumPy, Pandas, and basic preprocessing methods. • Handle missing values, scaling, encoding, train-test splitting, and data transformation for AI projects. • Convert raw datasets into clean, structured, and model-ready inputs for TensorFlow workflows.
Building AI Models with TensorFlow and Keras
Build AI models using TensorFlow and Keras for prediction, classification, regression, and pattern recognition. • Understand layers, activation functions, optimizers, loss functions, metrics, and model compilation. • Train machine learning and deep learning models using practical research-style examples.
Deep Learning Architectures for Research Projects
Learn neural networks, dense networks, convolutional neural networks, recurrent models, and transfer learning basics. • Understand how different model architectures are selected based on research problem, data type, and project goals. • Apply deep learning models to image data, structured data, sequence data, and prediction-based research problems.
Model Training, Validation, and Performance Evaluation
Train TensorFlow models using research datasets and monitor model behavior during learning. • Evaluate model performance using accuracy, precision, recall, F1-score, RMSE, MAE, ROC curves, and confusion matrix. • Improve research models through tuning, regularization, early stopping, dropout, and validation strategies.
TensorBoard, Experiment Tracking, and Research Comparison
Use TensorBoard to visualize training progress, loss curves, accuracy trends, and model metrics. • Compare different model versions, parameter settings, and experimental results. • Document experiment observations and prepare research-style model comparison summaries.
Applied AI Research Use Cases
Apply TensorFlow to research projects in healthcare, biotechnology, finance, manufacturing, environment, education, and automation. • Explore use cases such as image classification, disease prediction, text classification, anomaly detection, and forecasting. • Translate research questions into TensorFlow-based AI project workflows with measurable outcomes.
Research Documentation, Reports, and Presentation
Prepare project reports covering problem statement, dataset, methodology, model design, results, and limitations. • Present AI research outputs using charts, metrics, tables, visual summaries, and interpretation notes. • Build project documentation suitable for academic portfolios, internships, research profiles, and professional resumes.
Capstone: End-to-End AI Research Project with TensorFlow
Work on a complete AI research project from dataset selection to final model evaluation and documentation. • Build, train, tune, test, compare, and present a TensorFlow-based AI model. • Create a portfolio-ready research project that demonstrates practical TensorFlow, machine learning, and AI research skills.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | AI Research Projects |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Python |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | Scikit-Learn |
| Covered Tool / Platform | TensorBoard |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Deep Learning |
| Covered Tool / Platform | Model Evaluation |
| Covered Tool / Platform | Research Documentation |
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