What You’ll Learn: Deep Learning with TensorFlow & Keras
You’ll move beyond theory and build, train, and deploy real deep learning models — the way they’re used in industry.
Neural Networks
Build dense and convolutional networks from scratch using Keras Sequential and Functional APIs.
CNNs & Transfer Learning
Use pre-trained models like MobileNet or ResNet to solve custom image tasks with minimal data.
Model Evaluation
Interpret metrics, confusion matrices, and use callbacks for early stopping and checkpointing.
Deploy & Serve
Export models in SavedModel format and serve predictions via Flask or TensorFlow Serving.
Who Is This Course For?
Designed for learners who’ve built basic ML models and now want to dive into deep learning with industry-standard tools.
- Developers with Python & beginner ML experience
- Data scientists ready to use TensorFlow in production
- Students preparing for deep learning roles or research
Hands-On Projects
Image Classifier from Scratch
Build a CNN to classify CIFAR-10 images using Keras, with custom callbacks and augmentation.
Transfer Learning on Custom Dataset
Fine-tune a pre-trained model to identify medical or satellite images with limited data.
Capstone
Deployed Prediction API
Package your best model and serve real-time predictions through a lightweight web API.
5-Week TensorFlow & Keras Syllabus
~50 hours total • Lifetime LMS access • 1:1 mentor support
Weeks 1–2: Neural Nets & CNNs
- Tensors, layers, and model building in Keras
- Training loops, optimizers, loss functions
- Building CNNs for image classification
- Data augmentation and overfitting control
Weeks 3–4: Transfer Learning & Evaluation
- Using pre-trained models (MobileNet, ResNet)
- Feature extraction vs. fine-tuning
- Evaluation metrics, confusion matrices
- Custom callbacks and model checkpointing
Week 5: Deployment & Serving
- Model serialization with SavedModel
- Building a REST API with Flask
- Intro to TensorFlow Serving (optional)
- Testing and monitoring predictions
NSTC‑Accredited Certificate
Share your verified credential on LinkedIn, resumes, and portfolios.
Frequently Asked Questions
Yes — you should be comfortable with basic Python, NumPy, Pandas, and the concept of training a model (e.g., from a beginner ML course). We build on that foundation.
Yes! You’ll export models with TensorFlow SavedModel format and serve predictions via a lightweight Flask or TensorFlow Serving API.
