This course provides a complete introduction to deep learning, starting with Python basics and essential libraries like NumPy, Pandas, and Matplotlib before moving on to frameworks such as TensorFlow, Theano, and Keras. You’ll learn the theory behind neural networks, including activation functions, loss functions, and optimizers, and then apply this knowledge to build multi-layer models for text data and convolutional neural networks (CNNs) for image data. With hands-on practice in CNN layers, model optimization, and techniques like image augmentation, you’ll gain the skills to confidently implement deep learning in real-world projects.
Who should take this Course?
The Deep Learning CNN with Python Online Course is designed for data scientists, AI/ML practitioners, software developers, and researchers who want to gain practical expertise in convolutional neural networks for computer vision and image processing. It is also valuable for students and professionals in fields such as robotics, healthcare, and automation who are looking to apply Python-based CNNs to real-world projects and innovations.
What you will learn
Learn the basics of Python programming
Use different Python libraries such as NumPy, Matplotlib, and Pandas
Understand the basic structure of artificial neurons and neural networks
Explore activation functions, loss functions, and optimizers
Create deep learning multi-layer neural network models for a text-based dataset
Create convolutional neural networks for an image-based dataset
Course Outline
Course Introduction
Course Introduction and Table of Contents
Introduction
Introduction to AI (Artificial Intelligence) and Machine Learning
Introduction to Deep learning
Setting Up Computer
Installing Anaconda
Python Basics
Assignment
Flow Control - Part 1
Flow Control - Part 2
List and Tuples
Dictionary and Functions - part 1
Dictionary and Functions - part 2
NumPy Basics
NumPy Basics - Part 1
NumPy Basics - Part 2
Matplotlib Basics
Matplotlib Basics - part 1
Matplotlib Basics - part 2
Pandas Basics
Pandas Basics - Part 1
Pandas Basics - Part 2
Installing Libraries
Installing Deep Learning Libraries
Artificial Neuron and Neural Network
Basic Structure
Activation Functions
Introduction
Popular Activation Functions
Popular Types of Activation Functions
Popular Types of Loss Functions
Popular Types of Loss Functions
Popular Types of Optimizers
Popular Optimizers
Popular Neural Network Types
Popular Neural Network Types
King County House Sales Regression Model
Step 1 - Fetch and Load Dataset
Step 2 and 3 - EDA (Exploratory Data Analysis) and Data Preparation - Part 1
Step 2 and 3 - EDA and Data Preparation - Part 2
Step 4 - Defining the Keras Model - Part 1
Step 4 - Defining the Keras Model - Part 2
Step 5 and 6 - Compile and Fit Model
Step 7 - Visualize Training and Metrics
Step 8 - Prediction Using the Model
Heart Disease Binary Classification Model
Heart Disease Binary Classification Model - Introduction
Step 1 - Fetch and Load Data
Step 2 and 3 - EDA and Data Preparation - Part 1
Step 2 and 3 - EDA and Data Preparation - Part 2
Step 4 - Defining the Model
Step 5 and 6 - Compile Fit and Plot the Model
Step 7 - Predicting Heart Disease Using Model
Red Wine Quality Multiclass Classification Model
Introduction
Step 1 - Fetch and Load Data
Step 2 and 3 - EDA and Data Visualization
Step 4 - Defining the Model
Step 5 and 6 - Compile Fit and Plot the Model
Step 7 - Predicting Wine Quality using Model
Serialize and Save Trained Model for Later Use
Digital Image Basics
Digital Image
Basic Image Processing Using Keras Functions - Part 1
Basic Image Processing Using Keras Functions - Part 2
Basic Image Processing Using Keras Functions - Part 3
Image Augmentation
Keras Single Image Augmentation - Part 1
Keras Single Image Augmentation - Part 2
Keras Directory Image Augmentation
Keras Data Frame Augmentation
Convolutional Neural Network
CNN (Convolutional Neural Networks) Basics
Stride Padding and Flattening Concepts of CNN
Flowers CNN Image Classification Model
Fetch Load and Prepare Data
Create Test and Train Folders
Defining the Model - Part 1
Defining the Model - Part 2
Defining the Model - Part 3
Training and Visualization
Save Model for Later Use
Load Saved Model and Predict
Improving Model - Optimization Techniques
Dropout Regularization
Padding and Filter Optimization
Augmentation Optimization
Hyper Parameter Tuning - Part 1
Hyper Parameter Tuning - Part 2
Transfer Learning Using Pretrained Models
VGG Introduction
VGG16 and VGG19 Prediction
VGG16 and VGG19 Prediction - Part 1
VGG16 and VGG19 Prediction - Part 2
ResNet50
ResNet50 Prediction
Transfer Learning Training Flowers Dataset
VGG16 - Part 1
VGG16 - Part 2
Transfer Learning Flower Prediction
VGG16 Transfer Learning Flower Prediction
VGG16 Transfer Learning Using Google Colab GPU
Preparing and Uploading Dataset
Training and Prediction
VGG19 Transfer Learning using Google Colab GPU
Training and Prediction
ResNet-50 Transfer Learning using Google Colab GPU
