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About This Course
- Deep Learning & Neural Networks Python – Keras Course Overview
The Deep Learning & Neural Networks Python – Keras course is designed to provide learners with practical knowledge of deep learning, artificial neural networks, and machine learning model development using Python and the Keras framework. The course introduces the core principles of deep learning, including neural network architecture, data processing, model training, and performance evaluation used in modern artificial intelligence applications. Learners will gain hands-on understanding of how intelligent systems learn from data to perform prediction, classification, and automation tasks.
This course explores essential deep learning concepts such as activation functions, backpropagation, optimisation techniques, convolutional neural networks (CNNs), and model evaluation methods. Learners will also work with Python programming and Keras libraries to build, train, and test neural network models efficiently. Emphasis is placed on practical implementation, algorithm understanding, and real-world AI applications across various industries.
By the end of the course, learners will be able to develop deep learning models, understand neural network operations, and apply machine learning techniques confidently using Python and Keras for data-driven problem-solving and AI development projects.
- What You Will Learn from Deep Learning & Neural Networks Python – Keras Course
- Understand deep learning and neural network fundamentals
- Build machine learning models using Python and Keras
- Learn neural network architecture and layer structures
- Apply activation functions and optimisation techniques
- Train, evaluate, and improve deep learning models
- Work with real-world datasets and predictions
- Understand convolutional neural network concepts clearly
- Develop practical artificial intelligence programming skills
- Why Should You Choose Deep Learning & Neural Networks Python – Keras Course?
This course provides practical and industry-focused training in artificial intelligence, machine learning, and neural network development using Python and Keras. It combines theoretical understanding with hands-on coding exercises to help learners build real deep learning models confidently. The flexible online learning format makes it suitable for aspiring AI professionals, programmers, data analysts, and technology enthusiasts seeking career opportunities in machine learning and intelligent systems development.
- Key Topics Covered
- Introduction to deep learning and AI concepts
- Neural network structures and model architecture
- Python programming for machine learning applications
- Keras framework and model development techniques
- Activation functions and optimisation algorithms
- Convolutional neural networks and image processing
- Model training, validation, and evaluation methods
- Real-world AI applications and predictive modelling
- Who Should Take This Course?
This course is suitable for aspiring data scientists, software developers, AI enthusiasts, students, and professionals interested in machine learning and artificial intelligence technologies. It is ideal for individuals seeking to develop practical deep learning skills using Python and Keras for real-world AI applications. Learners with basic programming knowledge who want to expand into neural networks, predictive analytics, and intelligent system development will benefit greatly from this course.
- Entry Requirements
There are no formal entry requirements for this course. It is open to learners aged 16 and above. Basic understanding of Python programming, mathematics, and computer concepts is recommended. An interest in artificial intelligence, machine learning, and data science will help learners fully benefit from the course content and practical exercises.
- Why Should You Learn with Imperial Academy
- Flexible online learning with practical coding projects
- Professionally designed AI and machine learning curriculum
- Learn Python and Keras through hands-on practice
- Study anytime at your own preferred pace
- Expert-developed deep learning training materials
- Gain recognised certification after course completion
- Career Prospects
- Machine Learning Engineer (£45,000 – £90,000)
- Data Scientist (£40,000 – £85,000)
- AI Developer (£45,000 – £95,000)
- Deep Learning Specialist (£50,000 – £100,000+)
- Python Developer (£35,000 – £70,000)
Course Curriculum
-
Course Introduction and Table of Contents
00:11:00
-
Deep Learning Overview – Theory Session – Part 1
00:06:00
-
Deep Learning Overview – Theory Session – Part 2
00:07:00
-
Choosing Between ML or DL for the next AI project – Quick Theory Session
00:09:00
-
Preparing Your Computer – Part 1
00:07:00
-
Preparing Your Computer – Part 2
00:06:00
-
Python Basics – Assignment
00:09:00
-
Python Basics – Flow Control
00:09:00
-
Python Basics – Functions
00:04:00
-
Python Basics – Data Structures
00:12:00
-
Theano Library Installation and Sample Program to Test
00:11:00
-
TensorFlow library Installation and Sample Program to Test
00:09:00
-
Keras Installation and Switching Theano and TensorFlow Backends
00:10:00
-
Explaining Multi-Layer Perceptron Concepts
00:03:00
-
Explaining Neural Networks Steps and Terminology
00:10:00
-
First Neural Network with Keras – Understanding Pima Indian Diabetes Dataset
00:07:00
-
Explaining Training and Evaluation Concepts
00:11:00
-
Pima Indian Model – Steps Explained – Part 1
00:09:00
-
Pima Indian Model – Steps Explained – Part 2
00:07:00
-
Coding the Pima Indian Model – Part 1
00:11:00
-
Coding the Pima Indian Model – Part 2
00:09:00
-
Pima Indian Model – Performance Evaluation – Automatic Verification
00:06:00
-
Pima Indian Model – Performance Evaluation – Manual Verification
00:08:00
-
Pima Indian Model – Performance Evaluation – k-fold Validation – Keras
00:10:00
-
Pima Indian Model – Performance Evaluation – Hyper Parameters
00:12:00
-
Understanding Iris Flower Multi-Class Dataset
00:08:00
-
Developing the Iris Flower Multi-Class Model – Part 1
00:09:00
-
Developing the Iris Flower Multi-Class Model – Part 2
00:06:00
-
Developing the Iris Flower Multi-Class Model – Part 3
00:09:00
-
Understanding the Sonar Returns Dataset
00:07:00
-
Developing the Sonar Returns Model
00:10:00
-
Sonar Performance Improvement – Data Preparation – Standardization
00:15:00
-
Sonar Performance Improvement – Layer Tuning for Smaller Network
00:07:00
-
Sonar Performance Improvement – Layer Tuning for Larger Network
00:06:00
-
Understanding the Boston Housing Regression Dataset
00:07:00
-
Developing the Boston Housing Baseline Model
00:08:00
-
Boston Performance Improvement by Standardization
00:07:00
-
Boston Performance Improvement by Deeper Network Tuning
00:05:00
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Boston Performance Improvement by Wider Network Tuning
00:04:00
-
Save & Load the Trained Model as JSON File (Pima Indian Dataset) – Part 1
00:09:00
-
Save & Load the Trained Model as JSON File (Pima Indian Dataset) – Part 2
00:08:00
-
Save and Load Model as YAML File – Pima Indian Dataset
00:05:00
-
Load and Predict using the Pima Indian Diabetes Model
00:09:00
-
Load and Predict using the Iris Flower Multi-Class Model
00:08:00
-
Load and Predict using the Sonar Returns Model
00:10:00
-
Load and Predict using the Boston Housing Regression Model
00:08:00
-
An Introduction to Checkpointing
00:06:00
-
Checkpoint Neural Network Model Improvements
00:10:00
-
Checkpoint Neural Network Best Model
00:04:00
-
Loading the Saved Checkpoint
00:05:00
-
Plotting Model Behavior History – Introduction
00:06:00
-
Plotting Model Behavior History – Coding
00:08:00
-
Dropout Regularization – Visible Layer – Part 1
00:11:00
-
Dropout Regularization – Visible Layer – Part 2
00:06:00
-
Dropout Regularization – Hidden Layer
00:06:00
-
Learning Rate Schedule using Ionosphere Dataset
00:06:00
-
Time Based Learning Rate Schedule – Part 1
00:07:00
-
Time Based Learning Rate Schedule – Part 2
00:12:00
-
Drop Based Learning Rate Schedule – Part 1
00:07:00
-
Drop Based Learning Rate Schedule – Part 2
00:08:00
-
Convolutional Neural Networks – Part 1
00:11:00
-
Convolutional Neural Networks – Part 2
00:06:00
-
Introduction to MNIST Handwritten Digit Recognition Dataset
00:06:00
-
Downloading and Testing MNIST Handwritten Digit Recognition Dataset
00:10:00
-
MNIST Multi-Layer Perceptron Model Development – Part 1
00:11:00
-
MNIST Multi-Layer Perceptron Model Development – Part 2
00:06:00
-
Convolutional Neural Network Model using MNIST – Part 1
00:13:00
-
Convolutional Neural Network Model using MNIST – Part 2
00:12:00
-
Large CNN using MNIST
00:09:00
-
Load and Predict using the MNIST CNN Model
00:14:00
-
Introduction to Image Augmentation using Keras
00:11:00
-
Augmentation using Sample Wise Standardization
00:10:00
-
Augmentation using Feature Wise Standardization & ZCA Whitening
00:04:00
-
Augmentation using Rotation and Flipping
00:04:00
-
Saving Augmentation
00:05:00
-
CIFAR-10 Object Recognition Dataset – Understanding and Loading
00:12:00
-
Simple CNN using CIFAR-10 Dataset – Part 1
00:09:00
-
Simple CNN using CIFAR-10 Dataset – Part 2
00:06:00
-
Simple CNN using CIFAR-10 Dataset – Part 3
00:08:00
-
Train and Save CIFAR-10 Model
00:08:00
-
Load and Predict using CIFAR-10 CNN Model
00:16:00
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