COURSE OVERVIEW
This is an intensive course that covers key concepts in machine learning, computer vision, natural language processing,
and neural network design while emphasizing hands-on projects and practical applications. It is designed to equip
participants with the practical skills and knowledge required to build, train, and deploy deep learning models using
TensorFlow. By the end of the course, participants will be fully prepared to take the official TensorFlow Developer
Certification exam and demonstrate their ability to implement scalable AI solutions.

WHO SHOULD ATTEND?
This course is ideal for software developers, data scientists, AI engineers and machine learning practitioners who wish
to advance their careers in artificial intelligence. It is also suited for professionals seeking to validate their TensorFlow
expertise with an industry recognized certification and organizations aiming to upskill their technical teams in applied
machine learning.

COURSE OUTCOMES
Delegates will gain the skills and knowledge to:
• Build, train, and deploy machine learning and deep learning models using TensorFlow.
• Apply key TensorFlow concepts like tensors, computational graphs, and automatic differentiation.
• Develop and fine-tune neural networks for image, text, and time-series tasks.
• Optimize model accuracy through hyperparameter tuning and efficient data pipelines.
• Deploy trained models using TensorFlow Serving, Lite, and TensorFlow.js.
• Demonstrate practical skills to succeed in the TensorFlow Developer Certificate exam.

KEY COURSE HIGHLIGHTS
At the end of the course, you will understand;
• How to build, train, and optimize deep neural networks using TensorFlow.
• Core concepts of computer vision and how to apply convolutional neural networks (CNNs) for image
recognition.
• Techniques to prevent overfitting, including data augmentation and dropout.
• Natural language processing (NLP) fundamentals and how to build models with recurrent neural networks
(RNNs), gated recurrent units (GRUs), and long short-term memory (LSTMs).
• How to work with embeddings and text data for advanced NLP applications.
• Time series analysis concepts and forecasting using machine learning.
• Best practices for scalable AI model deployment in real-world scenarios.

Course Booking

Please use the “book now” or “inquire” buttons on this page to either book your space or make further enquiries.