CNNs, RNNs, TensorFlow, and Keras.
What You'll Learn
Understand how artificial neural networks are built and trained
Implement forward propagation and backpropagation from scratch
Build and train CNNs for image classification
Apply transfer learning with VGG, ResNet, and MobileNet
Build RNNs and LSTMs for sequences and time-series
Prevent overfitting with dropout, batch norm, and data augmentation
Optimise training with Adam, schedulers, and early stopping
Course Curriculum
Projects You'll Build
Handwritten digit recogniser (MNIST)Custom image classifier (your dataset)Stock price prediction with LSTMMedical X-ray abnormality detector
Tools & Technologies
PythonTensorFlow 2KerasNumPyMatplotlibStreamlitGoogle Colab (GPU)
Prerequisites
- ▸Machine Learning Essentials (or equivalent)
- ▸Python proficiency
- ▸Basic linear algebra (matrices, dot products)
Who It's For
- ▸College students (CS/Data Science)
- ▸ML enthusiasts ready to go deeper
- ▸Anyone targeting AI engineering or research
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Deep Learning & Neural Networks
14 weeks
Duration
Intermediate
Level
Yes
Certificate
Online / Offline
Mode