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Deep Learning & Neural Networks Python - Keras

5 enrolled 11h 9m of learning Updated Nov 2026
CertificateOn completion
11h 9mSelf-paced
80 lessonsOn the UISC learning portal
All LevelsNo prior training needed

Course curriculum

1 modules 80 lessons 11h 9m total
01 Deep Learning & Neural Networks Python - Keras 80 lessons
  1. Course Introduction and Table of Contents
  2. Deep Learning Overview – Theory Session – Part 1
  3. Deep Learning Overview – Theory Session – Part 2
  4. Choosing Between ML or DL for the next AI project – Quick Theory Session
  5. Preparing Your Computer – Part 1
  6. Preparing Your Computer – Part 2
  7. Python Basics – Assignment
  8. Python Basics – Flow Control
  9. Python Basics – Functions
  10. Python Basics – Data Structures
  11. Theano Library Installation and Sample Program to Test
  12. TensorFlow library Installation and Sample Program to Test
  13. Keras Installation and Switching Theano and TensorFlow Backends
  14. Explaining Multi-Layer Perceptron Concepts
  15. Explaining Neural Networks Steps and Terminology
  16. First Neural Network with Keras – Understanding Pima Indian Diabetes Dataset
  17. Explaining Training and Evaluation Concepts
  18. Pima Indian Model – Steps Explained – Part 1
  19. Pima Indian Model – Steps Explained – Part 2
  20. Coding the Pima Indian Model – Part 1
  21. Coding the Pima Indian Model – Part 2
  22. Pima Indian Model – Performance Evaluation – Automatic Verification
  23. Pima Indian Model – Performance Evaluation – Manual Verification
  24. Pima Indian Model – Performance Evaluation – k-fold Validation – Keras
  25. Pima Indian Model – Performance Evaluation – Hyper Parameters
  26. Understanding Iris Flower Multi-Class Dataset
  27. Developing the Iris Flower Multi-Class Model – Part 1
  28. Developing the Iris Flower Multi-Class Model – Part 2
  29. Developing the Iris Flower Multi-Class Model – Part 3
  30. Understanding the Sonar Returns Dataset
  31. Developing the Sonar Returns Model
  32. Sonar Performance Improvement – Data Preparation – Standardization
  33. Sonar Performance Improvement – Layer Tuning for Smaller Network
  34. Sonar Performance Improvement – Layer Tuning for Larger Network
  35. Understanding the Boston Housing Regression Dataset
  36. Developing the Boston Housing Baseline Model
  37. Boston Performance Improvement by Standardization
  38. Boston Performance Improvement by Deeper Network Tuning
  39. Boston Performance Improvement by Wider Network Tuning
  40. Save & Load the Trained Model as JSON File (Pima Indian Dataset) – Part 1
  41. Save & Load the Trained Model as JSON File (Pima Indian Dataset) – Part 2
  42. Save and Load Model as YAML File – Pima Indian Dataset
  43. Load and Predict using the Pima Indian Diabetes Model
  44. Load and Predict using the Iris Flower Multi-Class Model
  45. Load and Predict using the Sonar Returns Model
  46. Load and Predict using the Boston Housing Regression Model
  47. An Introduction to Checkpointing
  48. Checkpoint Neural Network Model Improvements
  49. Checkpoint Neural Network Best Model
  50. Loading the Saved Checkpoint
  51. Plotting Model Behavior History – Introduction
  52. Plotting Model Behavior History – Coding
  53. Dropout Regularization – Visible Layer – Part 1
  54. Dropout Regularization – Visible Layer – Part 2
  55. Dropout Regularization – Hidden Layer
  56. Learning Rate Schedule using Ionosphere Dataset
  57. Time Based Learning Rate Schedule – Part 1
  58. Time Based Learning Rate Schedule – Part 2
  59. Drop Based Learning Rate Schedule – Part 1
  60. Drop Based Learning Rate Schedule – Part 2
  61. Convolutional Neural Networks – Part 1
  62. Convolutional Neural Networks – Part 2
  63. Introduction to MNIST Handwritten Digit Recognition Dataset
  64. Downloading and Testing MNIST Handwritten Digit Recognition Dataset
  65. MNIST Multi-Layer Perceptron Model Development – Part 1
  66. MNIST Multi-Layer Perceptron Model Development – Part 2
  67. Convolutional Neural Network Model using MNIST – Part 1
  68. Convolutional Neural Network Model using MNIST – Part 2
  69. Large CNN using MNIST
  70. Load and Predict using the MNIST CNN Model
  71. Introduction to Image Augmentation using Keras
  72. Augmentation using Sample Wise Standardization
  73. Augmentation using Feature Wise Standardization & ZCA Whitening
  74. Augmentation using Rotation and Flipping
  75. Saving Augmentation
  76. CIFAR-10 Object Recognition Dataset – Understanding and Loading
  77. Simple CNN using CIFAR-10 Dataset – Part 1
  78. Simple CNN using CIFAR-10 Dataset – Part 2
  79. Simple CNN using CIFAR-10 Dataset – Part 3
  80. Train and Save CIFAR-10 Model

About this course

Overview: Deep Learning & Neural Networks Python - Keras

Welcome to the "Deep Learning & Neural Networks Python – Keras" course! This comprehensive program is designed to provide participants with a solid foundation in deep learning and neural networks using the Python programming language and the Keras library. Deep learning has emerged as a powerful tool for solving complex problems in various domains, including image recognition, natural language processing, and predictive analytics. Through this course, participants will explore the principles, algorithms, and applications of deep learning, with a focus on building and training neural networks using Keras.

  • Interactive video lectures by industry experts
  • Instant e-certificate
  • Fully online, interactive course with Professional voice-over
  • Developed by qualified first aid professionals
  • Self paced learning and laptop, tablet, smartphone friendly
  • 24/7 Learning Assistance
  • Discounts on bulk purchases

Main Course Features:

  • Introduction to deep learning concepts, including neural networks, activation functions, and gradient descent optimization
  • Hands-on tutorials and coding exercises using Python and the Keras deep learning framework
  • Exploration of various neural network architectures, including feedforward networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs)
  • Practical projects and case studies in image classification, text generation, and time series prediction
  • Guidance on model evaluation, hyperparameter tuning, and regularization techniques to improve model performance
  • Access to a library of resources, including video lectures, code examples, and supplementary materials
  • Expert insights and best practices from industry professionals and researchers in the field of deep learning
  • Opportunities for networking and collaboration with peers through online forums, discussion groups, and project work

Who Should Take This Course:

  • Data scientists and machine learning engineers interested in deepening their understanding of neural networks and Keras
  • Python developers looking to expand their skill set into the field of deep learning and artificial intelligence
  • Students and researchers seeking to explore advanced topics in deep learning and apply them to real-world problems
  • Professionals working in industries such as healthcare, finance, and technology, where deep learning has significant applications
  • Anyone interested in mastering the principles and techniques of deep learning using the Python programming language and Keras framework

Learning Outcomes:

  • Gain a solid understanding of deep learning principles, architectures, and algorithms
  • Develop proficiency in building and training neural networks using the Keras library
  • Learn how to apply deep learning techniques to solve a variety of real-world problems
  • Explore advanced topics in deep learning, including CNNs, RNNs, and autoencoders
  • Acquire practical skills in evaluating, tuning, and deploying deep learning models
  • Build a portfolio of deep learning projects showcasing various applications and domains
  • Stay updated on the latest advancements and trends in deep learning and neural networks
  • Demonstrate proficiency in implementing deep learning solutions using Python and Keras through hands-on projects and assessments.

Certification

Once you’ve successfully completed your course, you will immediately be sent a digital certificate. All of our courses are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.

Assessment

At the end of the Course, there will be an online assessment, which you will need to pass to complete the course. Answers are marked instantly and automatically, allowing you to know straight away whether you have passed. If you haven’t, there’s no limit on the number of times you can take the final exam. All this is included in the one-time fee you paid for the course itself.

Your certificate

CPD certificate — download it when you finish

Issued the moment you complete the final module. Every certificate lists the course aims and objectives, anticipated learning outcomes, the quality-assurance process — the elements required for verifiable CPD. Non-registrants can use it as CQC training evidence and for appraisals.

Aims & objectives Learning outcomes Unique certificate number

Common questions

Where do I take the course?

On the UISC learning portal. "Enrol now" takes you to this course there, where you pay securely, get your login and study at your own pace. Your certificate is issued there when you finish.

When can I start?

Immediately. Access opens the moment your order is confirmed and you can learn from any device, in any order, as many times as you like.

How is the course assessed?

A short multiple-choice or scenario-based assessment at the end of the course with instant results. You can retake it as many times as you need at no extra cost.

Can I enrol my whole team?

Yes. Organisations can buy multiple places on one invoice and receive a training record for every learner. Contact us for team pricing.

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