Course Content
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Introduction to AI & Deep Learning
- AI vs Machine Learning vs Deep Learning
- Real-world AI applications
- Overview of neural networks
- Deep learning workflow
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Neural Networks Fundamentals
- Neurons, layers, and activation functions
- Forward propagation
- Loss functions
- Training process
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Building Your First Neural Network (Keras)
- Introduction to Keras
- Building a simple neural network
- Training and evaluating a model
- Making predictions
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Improving Neural Networks
- Overfitting vs underfitting
- Regularisation techniques
- Dropout
- Early stopping
- Learning rate
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Deep Learning for Classification
- Classification using neural networks
- Model evaluation (accuracy, confusion matrix)
- Working with real datasets
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Deep Learning for Regression
- Regression using neural networks
- Comparison with traditional regression
- Model performance and evaluation
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Natural Language Processing (NLP) with Deep Learning
- Introduction to NLP
- Text preprocessing
- Tokenisation and vectorisation
- Text classification using neural networks
- Practical NLP applications
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Sequence Models & Time Series (LSTM)
- Introduction to sequential data
- LSTM networks
- Time series forecasting
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Introduction to Transformers & LLMs
- Evolution from RNNs to transformers
- Attention mechanism (high-level)
- What are Large Language Models (LLMs)
- How models like ChatGPT are trained (conceptual)
- Limitations of deep learning vs LLMs
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Model Saving & Deployment
- Saving and loading models
- Using models in real applications
- Deployment concepts
- Building simple prediction pipelines
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Final Capstone Project
- End-to-end deep learning project
- Data preparation → model building → evaluation → deployment
- Real-world business problem
Course Dates, Prices & Enrolment
Scroll right for more details
| Delivery Method | Dates & Times | Hours | Price | Enrolment |
Online Training using Zoom
|
22 Jun 2026 - 26 Jun 2026
Mon, Wed & Fri
10:00 AM - 02:00 PM
BT
| 12-hour over 3-day |
| Enrol Now |
Online Training using Zoom
|
20 Jul 2026 - 31 Jul 2026
Mon, Wed & Fri (2 wks)
03:00 PM - 05:00 PM
BT
| 12-hour over 6-day |
| Enrol Now |
Online Training using Zoom
|
23 Jul 2026 - 24 Jul 2026
Thursday to Friday
10:00 AM - 04:00 PM
BT
| 12-hour over 2-day |
| Enrol Now |
Online Training using Zoom
|
24 Aug 2026 - 28 Aug 2026
Mon, Wed & Fri
10:00 AM - 02:00 PM
BT
| 12-hour over 3-day |
| Enrol Now |
Online Training using Zoom
|
24 Sep 2026 - 25 Sep 2026
Thursday to Friday
10:00 AM - 04:00 PM
BT
| 12-hour over 2-day |
| Enrol Now |
Note: Online classes are usually delivered through Zoom video conferencing.
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Frequently Asked Questions
Do I need prior Python knowledge for this course?
Yes, participants should have basic Python programming knowledge and familiarity with libraries such as NumPy, Pandas, Scikit-learn, and Matplotlib before attending this course.
Is this course suitable for beginners in AI?
This is an intermediate to advanced course designed for learners who already have some experience with Python and machine learning concepts. It is suitable for those looking to progress into deep learning and modern AI technologies.
What will I learn in this course?
You will learn how to build, train, evaluate, and optimise deep learning models using Python, Keras, and TensorFlow. The course also covers neural networks, NLP, transformers, LSTMs, and introductory concepts behind Large Language Models (LLMs).
Will I build real deep learning models during the course?
Yes, this course is highly practical and includes hands-on exercises and projects where you will build and train real deep learning models for classification, regression, NLP, and time series tasks.
Which tools and technologies will I use?
You will work with Python and industry-standard libraries and frameworks such as NumPy, Pandas, Scikit-learn, Keras, and TensorFlow.
You may use tools such as Jupyter Notebook, Google Colab, Visual Studio Code, or PyCharm during the course.
Do I need experience with machine learning before attending this course?
Yes, a basic understanding of machine learning concepts is recommended. We highly recommend completing a Data Science and Machine Learning with Python course or having equivalent knowledge before attending this course.
Does this course cover Large Language Models (LLMs)?
Yes, the course introduces modern AI concepts, including transformers, attention mechanisms, and Large Language Models (LLMs). You will gain a conceptual understanding of how systems like ChatGPT are trained and how modern AI architectures work.
Will this course cover Natural Language Processing (NLP)?
Yes, the course includes an introduction to Natural Language Processing (NLP), including text preprocessing, tokenisation, vectorisation, and text classification using neural networks.
Will I learn about time series forecasting and LSTMs?
Yes, the course covers sequence models and LSTM networks for working with sequential and time series data, including forecasting applications.
Is deep learning useful for my career?
Yes, deep learning and AI skills are in high demand across industries such as finance, healthcare, technology, marketing, and automation. These skills are valuable for careers in AI, machine learning, data science, and intelligent systems development.
What jobs can I apply for after completing this course?
After completing this course, you may pursue roles such as Junior AI Engineer, Machine Learning Engineer, Data Scientist, AI Developer, or Deep Learning Engineer, depending on your overall experience and technical background.
Will there be a final project in the course?
Yes, the course includes a final capstone project where you will work on an end-to-end deep learning solution, covering data preparation, model building, evaluation, and deployment concepts.
How is the course delivered?
The course is instructor-led and can be attended live online, in person at our London training centre, or delivered on-site at your organisation for corporate training.
Will I receive a certificate after completing the course?
Yes, you will receive a certificate of completion after successfully finishing the course.
What should I learn after completing this course?
After completing this course, you can progress to more advanced topics such as generative AI, advanced LLM applications, AI automation systems, deep learning optimisation, and intelligent AI application development.