Programme Parameters
Advanced Postgrad
4 Weeks (40 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Machine Learning with TensorFlow is a hands-on path through Google’s leading ML framework. You learn to build and train models in TensorFlow and Keras — from neural-network basics to convolutional and sequence models — and the full workflow of data pipelines, training, evaluation and deployment. Applied throughout with real tasks, the course turns TensorFlow from intimidating to productive. You finish able to build, train and deploy machine-learning models in TensorFlow. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course teaches machine learning with TensorFlow — building, training and deploying ML and deep-learning models across real tasks using TensorFlow and Keras.
📋 Course Objectives
1. Build and train models in TensorFlow/Keras.
2. Construct efficient data pipelines.
3. Build CNN and sequence models.
4. Evaluate and tune performance.
5. Deploy trained models.
👥 Who Should Enroll?
• Developers and ML practitioners
• Data scientists adopting TensorFlow
• Engineers building models
• Students of machine learning
🚀 Key Learning Outcomes
• Hands-on TensorFlow ML fluency.
• A deep-learning project.
• A production-aware workflow.
• 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 Machine Learning with TensorFlow
Understand the role of TensorFlow in machine learning, deep learning, and artificial intelligence development. • Learn key concepts such as datasets, features, labels, model training, prediction, loss functions, and optimization. • Explore how TensorFlow supports scalable AI model development for real-world applications.
Python, NumPy, and Data Preparation for TensorFlow
Prepare datasets using Python, NumPy, Pandas, and basic data preprocessing techniques. • Handle missing values, scaling, encoding, train-test splitting, and feature preparation. • Convert clean datasets into formats suitable for TensorFlow model training.
Building Machine Learning Models with TensorFlow and Keras
Build basic machine learning models using TensorFlow and Keras APIs. • Understand layers, activation functions, optimizers, loss functions, and model compilation. • Train models for classification, regression, and prediction-based tasks.
Neural Networks and Deep Learning Fundamentals
Learn how neural networks work through neurons, weights, biases, activation functions, and backpropagation. • Design feedforward neural networks for structured data problems. • Understand model training behavior, overfitting, underfitting, and regularization methods.
Model Training, Evaluation, and Performance Improvement
Train TensorFlow models using real-world datasets and monitor learning progress. • Evaluate model performance using accuracy, precision, recall, F1-score, RMSE, MAE, and confusion matrix. • Improve models using hyperparameter tuning, dropout, batch normalization, and early stopping.
Computer Vision with TensorFlow
Learn the basics of image data processing and computer vision model building. • Build convolutional neural networks for image classification and visual pattern recognition. • Apply TensorFlow to practical use cases such as object recognition, defect detection, and image-based prediction.
TensorBoard, Experiment Tracking, and Model Debugging
Use TensorBoard to monitor training metrics, loss curves, accuracy, and model behavior. • Compare experiments and understand how model changes affect performance. • Debug common TensorFlow training issues and improve model reliability.
Deployment and Real-World TensorFlow Applications
Understand how trained TensorFlow models are saved, reused, and deployed for practical applications. • Explore use cases in healthcare, finance, manufacturing, retail, automation, and smart systems. • Learn how TensorFlow models support prediction, classification, recommendation, and intelligent decision-making.
Capstone: End-to-End Machine Learning with TensorFlow Project
Work on a complete TensorFlow-based machine learning project from dataset preparation to final model evaluation. • Build, train, tune, test, and present a practical AI model using TensorFlow and Keras. • Create a project portfolio that demonstrates real-world TensorFlow and machine learning skills.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Machine Learning |
| 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 | Neural Networks |
| Covered Tool / Platform | Deep Learning |
| Covered Tool / Platform | Computer Vision |
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