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 00:10:00
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
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 00:07:00
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 00:12:00
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 00:12:00
Recomended Readings 00:00:00

Deep Learning with Python Course Overview

The Deep Learning with Python Course introduces you to neural networks and model development using Python and Keras. Deep learning supports technologies such as image recognition, voice assistants, predictive analytics and fraud detection, making it an important area of modern artificial intelligence.

You’ll begin by exploring how neural networks process information and learn patterns from data. The course then introduces layers, activation functions and optimisers before showing you how to prepare data and train models using Keras.

Throughout this Keras Deep Learning Course, you’ll gain practical experience in designing, training and evaluating neural network models. You’ll also learn how to measure model accuracy, identify common training problems and improve model performance.

Whether you’re a developer, data enthusiast or aspiring AI professional, this Deep Learning and Neural Networks with Python training provides a structured introduction to essential concepts. By the end, you’ll understand how to build neural networks with Python and use deep learning models for classification and prediction tasks.

Learning Outcomes

  • Explain the principles of deep learning and how neural networks process information

  • Understand layers, activation functions, loss functions and optimisers

  • Prepare and clean data for training deep learning models

  • Build neural networks with Python and Keras

  • Train models for classification and prediction tasks

  • Evaluate model accuracy using suitable performance measures

  • Identify and troubleshoot common model-training problems

  • Apply techniques to improve neural network performance

  • Describe how deep learning is used in real-world AI applications

What You’ll Gain

By completing this Deep Learning with Python Course, you will:

  • Understand deep learning fundamentals: Explore how deep learning models identify patterns, process information and generate predictions.

  • Learn how neural networks operate: Understand nodes, layers, weights and activation functions through clear, accessible explanations.

  • Use Python for deep learning: Develop practical experience using Python to prepare data and create neural network models.

  • Build models with Keras: Learn how Keras simplifies the process of designing, configuring and training deep learning models.

  • Prepare training data: Explore how to clean, organise and transform data before using it within a neural network.

  • Train your own neural network: Apply your knowledge by building and training functional models with Python and Keras.

  • Evaluate model performance: Measure accuracy, recognise common training issues and identify opportunities for improvement.

  • Solve classification and prediction problems: Use neural networks with Python to approach practical, data-driven tasks.

  • Explore real-world applications: Understand how deep learning supports image recognition, natural language processing and predictive systems.

  • Prepare for advanced AI learning: Build a foundation for progressing to more complex topics in artificial intelligence, machine learning and data science.

Our Specialised Delivery Method:

  • Interactive Learning Materials: The course modules were created using an EdTech industry-recognised tool to keep you engaged at all times. With this tool, you get interactive, engaging and top-notch course content. In our courses, you can take advantage of features like—
    • Drop Down Menu
    • Drag and Drop
    • Flash Card
    • Label Graphic
    • Timeline View
  • Responsiveness: In light of contemporary mobile and point-of-need learning trends, our courses are designed to be intrinsically dynamic and provide you the ultimate eLearning solution. These courses will adapt to any gadget without any extra software.
  • Learner-Friendly Navigation: Our courses are also quite simple to navigate for any learner. These courses are designed with simplicity and a modern flow that appeals to a wide range of learning audiences, regardless of their technical background and gadgets.
  • Elegant Outline: Our courses are visually appealing, where you can —
    • Track your progress on the left-side navigation toolbar.
    • Engage in drag-and-drop sorting activities and use the multiple response question to test your understanding of course topics.

With your newly acquired skills from this course can help you

  • Increase Your Hireability.
  • Make Yourself a Valuable Asset
  • Get Your Long-awaited Promotion.
  • Boost Your Pay-scale
  • Better Your Productivity

Certification:

Once you have successfully completed the Deep Learning & Neural Networks Python – Keras course with certificate, you will receive a PDF certificate completely free of cost as a proof of your accomplishment. The hardcopy certificate is also available for the cost of £9.99. UK students are required to pay a £10 as a delivery fee, while international students have to pay £19.99 for the shipment of a hardcopy certificate to their designated address.

Who is this Deep Learning with Python course for?

This Deep Learning & Neural Networks Python – Keras course is developed for people who wish to excel in their professional and personal life. Learn from industry leaders and interact with a global network of experts by enrolling in this Deep Learning & Neural Networks Python – Keras course.

Requirements

Enrolling in our Deep Learning & Neural Networks Python – Keras course does not require any prior knowledge or experience. All that is required is an internet-connected gadget and a passion to learn.

Career Path in Deep Learning with Python

  • Junior Machine Learning Engineer

    Assists with data preparation, model training and experiments while working under the direction of experienced machine learning professionals.

    UK salary range: £30,000–£47,500 per year

  • Deep Learning Engineer

    Designs, trains and improves neural networks for applications involving images, speech, text or predictive data.

    UK salary range: £45,000–£75,000 per year

  • Machine Learning Engineer

    Creates machine learning pipelines covering data preparation, model development, testing and deployment.

    UK salary range: £47,500–£106,000 per year

  • Computer Vision or NLP Engineer

    Applies deep learning to specialist areas such as image recognition, video analysis and natural language processing.

    UK salary range: £50,000–£85,000 per year

  • Senior Data Scientist

    Uses deep learning, machine learning and statistical modelling to solve complex organisational and commercial problems.

    UK salary range: £55,000–£90,000 per year

  • Senior or Lead Machine Learning Engineer

    Leads machine learning projects, makes architecture decisions, mentors technical teams and oversees large-scale model deployment.

    UK salary range: £102,000–£170,500 per year

Certification:​

Once you have successfully completed this course, you can order a certificate as proof of your achievement.

A PDF certificate is available for £5.99. You can also request a hardcopy certificate for £9.99.

Delivery of the hardcopy certificate is free within the United Kingdom. For international learners, an additional delivery charge will apply based on the destination country.

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I have studied a few courses and have…

I have studied a few courses and have found the whole experience quick and efficient, and believe the courses will help contribute to my job in the Healthcare. Fabulous!

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A clear and concise course

This self-paced course delivers clear, high-quality content on political science fundamentals, ideal for beginners. It builds confidence in key topics like political theory, institutions, the British constitution and Parliament, US government, elections, and doctrines (liberalism, anarchism, conservatism, nationalism). The flexible format fits busy schedules. Highly recommended for aspiring international relations professionals seeking internships or jobs—it broadens knowledge and strengthens foundational skills.