5 modules
Explore the fundamentals of Artificial Intelligence & Machine Learning
4-4.5 Level NCrF
National Credit Framework
120 Hours
Flexible Learning
Intermediate Level
Certificate Diploma Course
04-05 Credits
Course Overview
The Deep Learning Developer (Intermediate) program combines DeepLearning.AI’s curriculum with hands-on training in computer vision and natural language processing. Students learn CNNs, RNNs, LSTMs, and Transformers using TensorFlow, Keras, and Hugging Face, applying skills to tourism and security sector challenges. Practical workshops, GPU-enabled labs, and real-world projects build expertise in model development, transfer learning, and fine-tuning, culminating in a capstone project for industry-ready roles.
Key Learning Highlights
Build and train CNNs, RNNs, LSTMs, and Transformers using TensorFlow, Keras, and Hugging Face.
Apply deep learning to real-world challenges in tourism and security sectors.
Gain hands-on experience with GPU-enabled labs and real datasets.
Implement transfer learning, model fine-tuning, and optimization techniques.
Complete a capstone project showcasing industry-ready AI solutions.
Tools & Platform Used
Learning Outcome
By the end of this course, students will be able to:
Build, train, and optimize CNN and RNN models using TensorFlow and Keras.
Apply deep learning techniques to real-world problems in tourism and security sectors.
Use pretrained models and apply transfer learning for improved performance.
Execute end-to-end deep learning projects, from data preparation to deployment.
Demonstrate skills for roles such as Deep Learning Specialist, Computer Vision Developer, and NLP Developer.
Master the course with just 5 Modules
This course takes learners from the fundamentals of Generative AI and prompt construction to advanced tuning techniques. Beginning with the principles behind AI-generated text and images, participants progress to hands-on experimentation with real-world use cases. The journey ends with a deep dive into emerging trends, ethical implications, and future opportunities in the rapidly evolving field of GenAI.
Deep Learning Foundations
Understand neural network architecture, forward/backward propagation, and activation functions
Learn optimization techniques and loss functions for training deep models
Get started with TensorFlow and Keras basics for deep learning projects
Computer Vision with CNNs
Explore convolution operations and popular CNN architectures like LeNet, VGG, and ResNet
Build and train image classification models using TensorFlow
Apply computer vision techniques to real-world datasets and scenarios
NLP with RNNs and Transformers
Learn text preprocessing techniques and sequence modeling with RNN, LSTM, and GRU
Get introduced to Transformers and Hugging Face for NLP tasks
Implement models for text classification, sentiment analysis, and language understanding
Transfer Learning & Model Tuning
Use pretrained models for faster and more efficient training
Apply fine-tuning, regularization, and dropout to improve model performance
Optimize learning rates and training schedules for better results
Capstone Project
Design and implement a deep learning solution in the tourism or security domain.
Integrate CNN or NLP models into a complete application.
Present and evaluate the project with academic and industry feedback.
Roles
- Deep Learning Specialist
- Computer Vision Engineer
- NLP Developer
- DL Model Trainer
- AI Research Assistant
