Overview What You Will Learn Curriculum Instructor

Course Overview

Deep learning is at the heart of modern artificial intelligence, powering everything from voice assistants to autonomous vehicles. This course is built around Keras, a user-friendly Python deep learning API that operates on top of powerful libraries like TensorFlow and Theano. For those new to AI or programming, understanding neural networks and their real-world applications is essential to entering the field. With step-by-step guidance, this course helps beginners bridge the gap between theory and practical implementation of deep learning systems, using Python as the foundation language.

This course begins with an introduction to deep learning concepts, helping learners distinguish between machine learning and deep learning use cases. It then walks through setting up the environment, Python basics, and key libraries like Theano, TensorFlow, and Keras. You’ll build foundational knowledge with projects such as the Pima Indian Diabetes, Iris Flower, and Sonar datasets. The course also explores model optimization, evaluation methods like k-fold validation, and advanced topics like convolutional neural networks (CNNs), image augmentation, dropout regularization, and learning rate scheduling. Hands-on projects with MNIST and CIFAR-10 datasets demonstrate real-world model training and prediction.

By the end, learners will confidently design, train, evaluate, and deploy deep learning models using Keras and Python.

What You Will Learn

  • Deep Learning and Convolutional Neural Networks using Python for Beginners
  • Learn to code Deep learning models using Python and Keras Library
  • Learn about Pima Indian Model
  • Learn how to develop Sonar Returns Model
  • Learn how to develop Boston Housing Baseline Model

Program Curriculum

  • Deep Learning Overview - Theory Session - Part 1
  • Deep Learning Overview - Theory Session - Part 2
  • $7 Million Cybersecurity Scholarship by EC-Council
  • Chapter 1 Quiz

  • Choosing Between ML or DL for the Next AI Project - Quick Theory Session
  • Chapter 2 Quiz

  • Preparing Your Computer - Part 1
  • Preparing Your Computer - Part 2
  • Chapter 3 Quiz

  • Python Basics - Assignment
  • Python Basics - Flow Control
  • Python Basics - Functions
  • Python Basics - Data Structures
  • Chapter 4 Quiz

  • Theano Library Installation and Sample Program to Test
  • Chapter 5 Quiz

  • TensorFlow Library Installation and Sample Program to Test
  • Chapter 6 Quiz

  • Keras Installation and Switching Theano and TensorFlow Backends
  • Chapter 7 Quiz

  • Explaining Multi-Layer Perceptron Concepts
  • Chapter 8 Quiz

  • Explaining Neural Networks Steps and Terminology
  • Chapter 9 Quiz

  • First Neural Network with Keras - Understanding Pima Indian Diabetes Dataset
  • Chapter 10 Quiz

  • Explaining Training and Evaluation Concepts
  • Chapter 11 Quiz

  • Pima Indian Model - Steps Explained - Part 1
  • Pima Indian Model - Steps Explained - Part 2
  • Capter 12 Quiz

  • Coding the Pima Indian Model - Part 1
  • Coding the Pima Indian Model - Part 2
  • Chapter 13 Quiz

  • Pima Indian Model - Performance Evaluation - Automatic Verification
  • Pima Indian Model - Performance Evaluation - Manual Verification
  • Chapter 14 Quiz

  • Pima Indian Model - Performance Evaluation - k-fold Validation - Keras
  • Chapter 15 Quiz

  • Pima Indian Model - Performance Evaluation - Hyper Parameters
  • Chapter 16 Quiz

Understanding Iris Flower Multi-Class Dataset

  • Developing the Iris Flower Multi-Class Model - Part 1
  • Developing the Iris Flower Multi-Class Model - Part 2
  • Developing the Iris Flower Multi-Class Model - Part 3
  • Chapter 18

  • Understanding the Sonar Returns Dataset
  • Chapter 19 Quiz

  • Developing the Sonar Returns Model
  • Chapter 20 Quiz

  • Sonar Performance Improvement - Data Preparation - Standardization
  • Chapter 21 Quiz

  • Sonar Performance Improvement - Layer Tuning for Smaller Network
  • Chapter 22 Quiz

  • Sonar Performance Improvement - Layer Tuning for Larger Network
  • Chapter 23 Quiz

  • Understanding the Boston Housing Regression Dataset
  • Chapter 24 Quiz

  • Developing the Boston Housing Baseline Model
  • Chapter 25 Quiz

  • Boston Performance Improvement by Standardization
  • Chapter 26 Quiz

  • Boston Performance Improvement by Deeper Network Tuning
  • Chapter 27 Quiz

  • Boston Performance Improvement by Wider Network Tuning
  • Chapter 28 Quiz

  • Save & Load the Trained Model as JSON File (Pima Indian Dataset) - Part 1
  • Save & Load the Trained Model as JSON File (Pima Indian Dataset) - Part 2
  • Chapter 29 Quiz

  • Save and Load Model as YAML File - Pima Indian Dataset
  • Chapter 30 Quiz

  • Load and Predict Using the Pima Indian Diabetes Model
  • Chapter 31 Quiz

  • Load and Predict Using the Iris Flower Multi-Class Model
  • Chapter 32 Quiz

  • Load and Predict Using the Sonar Returns Model
  • Chapter 33 Quiz

  • Load and Predict Using the Boston Housing Regression Model
  • Chapter 34 Quiz

  • An Introduction to Checkpointing
  • Chapter 35 Quiz

  • Checkpoint Neural Network Model Improvements
  • Chapter 36 Quiz

  • Checkpoint Neural Network Best Model
  • Chapter 37 Quiz

  • Loading the Saved Checkpoint
  • Chapter 38 Quiz

  • Plotting Model Behavior History - Introduction
  • Plotting Model Behavior History - Coding
  • Chapter 39 Quiz

  • Dropout Regularization - Visible Layer - Part 1
  • Dropout Regularization - Visible Layer - Part 2
  • Chapter 40 Quiz

  • Dropout Regularization - Hidden Layer
  • Chapter 41 Quiz

  • Learning Rate Schedule Using Ionosphere Dataset
  • Chapter 42 Quiz

  • Time Based Learning Rate Schedule - Part 1
  • Time Based Learning Rate Schedule - Part 2
  • Chapter 43 Quiz

  • Drop Based Learning Rate Schedule - Part 1
  • Drop Based Learning Rate Schedule - Part 2
  • Chapter 44 Quiz
  • Chapter 45 Quiz

  • Convolutional Neural Networks - Part 1
  • Convolutional Neural Networks - Part 2

  • Introduction to MNIST Handwritten Digit Recognition Dataset
  • Downloading and Testing MNIST Handwritten Digit Recognition Dataset
  • Chapter 46 Quiz

  • MNIST Multi-Layer Perceptron Model Development - Part 1
  • MNIST Multi-Layer Perceptron Model Development - Part 2
  • Chapter 47 Quiz

  • Convolutional Neural Network Model Using MNIST - Part 1
  • Convolutional Neural Network Model Using MNIST - Part 2
  • Chapter 48 Quiz

  • Large CNN Using MNIST
  • Chapter 49 Quiz

  • Load and Predict using the MNIST CNN Model
  • Chapter 50 Quiz

  • Introduction to Image Augmentation Using Keras
  • Chapter 51 Quiz

  • Augmentation Using Sample Wise Standardization
  • Chapter 52 Quiz

  • Augmentation Using Feature Wise Standardization & ZCA Whitening
  • Chapter 53 Quiz

  • Augmentation Using Rotation and Flipping
  • Chapter 54 Quiz

  • Saving Augmentation
  • Chapter 55 Quiz

  • CIFAR-10 Object Recognition Dataset - Understanding and Loading
  • Chapter 56 Quiz

  • Simple CNN using CIFAR-10 Dataset - Part 1
  • Simple CNN using CIFAR-10 Dataset - Part 2
  • Simple CNN using CIFAR-10 Dataset - Part 3
  • Chapter 57 Quiz

  • Train and Save CIFAR-10 Model
  • Chapter 58 Quiz

  • Load and Predict Using CIFAR-10 CNN Model
  • Chapter 59 Quiz
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Instructor

Abhilash Nelson

Abhilash Nelson is a pioneering, talented and security-oriented Android/iOS Mobile and PHP/Python Web Developer Application Developer offering more than eight years’ overall IT experience which involves designing, implementing, integrating, testing and supporting impact-full web and mobile applications. He is a Postgraduate Master's Degree holder in Computer Science and Engineering and is currently serving full time as a Senior Solution Architect managing my client's projects from start to finish to ensure high quality, innovative and functional design.

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