Deep Learning & Neural Networks Python - Keras
Course
Online
Description
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Type
Course
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Level
Intermediate
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Methodology
Online
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Class hours
11h
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Duration
Flexible
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Start date
Different dates available
Deep Learning & Neural Networks Python – Keras is a comprehensive course designed to introduce learners to modern deep learning concepts using Python and the Keras framework. The course focuses on building, training, and optimising neural networks for real-world applications such as image recognition, natural language processing, and predictive analytics.
Learners will explore the fundamentals of artificial neural networks, including perceptrons, activation functions, loss functions, and optimisation techniques. The course provides hands-on experience with Keras, enabling learners to design deep learning models efficiently while leveraging TensorFlow as a backend. Practical examples and projects ensure learners understand how to apply theory to real datasets.
This course is ideal for aspiring data scientists, machine learning engineers, and developers who want to strengthen their AI skill set. By the end of the course, learners will confidently build, evaluate, and deploy deep learning models using Python and Keras, gaining a strong foundation for advanced AI and machine learning careers.
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About this course
Understand the fundamentals of deep learning and neural networks
Build and train neural networks using Python and Keras
Apply activation functions, loss functions, and optimisers
Work with real-world datasets for predictive modelling
Evaluate and improve model performance
Understand overfitting, regularisation, and model tuning
Prepare deep learning models for practical applications
Deep Learning & Neural Networks Python – Keras is designed for students, developers, data analysts, and professionals who want to enter or advance in the field of artificial intelligence.
The course is suitable for learners with basic Python knowledge who are interested in machine learning, data science, or AI-driven solutions. It is particularly valuable for professionals working in software development, analytics, engineering, or research who want to gain hands-on experience with deep learning tools.
Whether you are building intelligent applications, enhancing your technical profile, or preparing for advanced AI studies, this course provides the practical skills and conceptual understanding needed to work confidently with neural networks and Keras.
Learners should have basic knowledge of Python programming and a general understanding of mathematics concepts such as algebra and statistics. No prior experience with deep learning or Keras is required.
The course is suitable for learners aged 16 and above. Basic computer literacy and internet access are recommended to follow practical demonstrations and complete exercises effectively.
Upon successful completion of Deep Learning & Neural Networks Python – Keras, learners receive a UK and internationally recognised professional certification.
You may also choose to obtain:
PDF Certificate – £9
Hardcopy Certificate – £15
This course combines theoretical clarity with hands-on practical learning using Python and Keras, one of the most widely used deep learning frameworks. Learners benefit from real-world examples, step-by-step model building, and practical insights into how deep learning is applied in industry today.
The flexible, self-paced online format allows learners to study anytime, anywhere, making it ideal for professionals and students alike. The course focuses on job-relevant skills, ensuring learners can immediately apply what they learn to real projects and career development.
No. The course starts with fundamentals and gradually progresses to more advanced concepts.
It combines both, with strong emphasis on hands-on coding and real-world applications.
The course is fully online and self-paced, allowing learners to study at their own convenience from any device.
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More than 50 reviews in the last 12 months
This centre has featured on Emagister for 7 years
Subjects
- Networks
- Network Training
- Network
- Neural network
- Saving Augmentation
Teachers and trainers (1)
One Education
Course Provider
Course programme
Course Curriculum
- Course Introduction and Table of Contents
- Deep Learning Overview
- Choosing Between ML or DL for the next AI project - Quick Theory Session
- Preparing Your Computer
- Python Basics
- Theano Library Installation and Sample Program to Test
- TensorFlow library Installation and Sample Program to Test
- Keras Installation and Switching Theano and TensorFlow Backends
- Explaining Multi-Layer Perceptron Concepts
- Explaining Neural Networks Steps and Terminology
- First Neural Network with Keras - Understanding Pima Indian
- Diabetes Dataset
- Explaining Training and Evaluation Concepts
- Pima Indian Model - Steps Explained
- Coding the Pima Indian Model
- Pima Indian Model - Performance Evaluation
- Pima Indian Model - Performance Evaluation - k-fold Validation - Keras
- Pima Indian Model - Performance Evaluation - Hyper Parameters
- Understanding Iris Flower Multi-Class Dataset
- Developing the Iris Flower Multi-Class Model
- Understanding the Sonar Returns Dataset
- Developing the Sonar Returns Model
- Sonar Performance Improvement - Data Preparation -
- Standardization
- Sonar Performance Improvement - Layer Tuning for Smaller Network
- Sonar Performance Improvement - Layer Tuning for Larger Network
- Understanding the Boston Housing Regression Dataset
- Developing the Boston Housing Baseline Model
- Boston Performance Improvement by Standardization
- Boston Performance Improvement by Deeper Network Tuning
- Boston Performance Improvement by Wider Network Tuning
- Save & Load the Trained Model as JSON File (Pima Indian Dataset)
- Save and Load Model as YAML File - Pima Indian Dataset
- Load and Predict using the Pima Indian Diabetes Model
- Load and Predict using the Iris Flower Multi-Class Model
- Load and Predict using the Sonar Returns Model
- Load and Predict using the Boston Housing Regression Model
- An Introduction to Checkpointing
- Checkpoint Neural Network Model Improvements
- Checkpoint Neural Network Best Model
- Loading the Saved Checkpoint
- Plotting Model Behavior History
- Dropout Regularization - Visible Layer
- Dropout Regularization - Hidden Layer
- Learning Rate Schedule using Ionosphere Dataset - Intro
- Time Based Learning Rate Schedule
- Drop Based Learning Rate Schedule
- Convolutional Neural Networks - Introduction
- MNIST Handwritten Digit Recognition Dataset
- MNIST Multi-Layer Perceptron Model Development
- Convolutional Neural Network Model using MNIST
- Large CNN using MNIST
- Load and Predict using the MNIST CNN Model
- Introduction to Image Augmentation using Keras
- Augmentation using Sample Wise Standardization
- Augmentation using Feature Wise Standardization & ZCA Whitening
- Augmentation using Rotation and Flipping
- Saving Augmentation
- CIFAR-10 Object Recognition Dataset - Understanding and Loading
- Simple CNN using CIFAR-10 Dataset
- Train and Save CIFAR-10 Model
- Load and Predict using CIFAR-10 CNN Model
- RECOMENDED READINGS
Deep Learning & Neural Networks Python - Keras