Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python

Chapter 1: Installation and Environment Setup

Chapter Goal: Making System Ready for Image Processing and Analysis

No of pages 20

Sub -Topics (Top 2)

1. Installing Jupyter Notebook

2. Installing OpenCV and other Image Analysis dependencies

3. Installing Neural Network Dependencies

Chapter 2: Introduction to Python and Image Processing

Chapter Goal: Introduction to different concepts of Python and Image processing Application on it.

No of pages: 50

Sub - Topics (Top 2)

1. Essentials of Python

2. Terminologies related to Image Analysis

Chapter 3: Advanced Image Processing using OpenCV

Chapter Goal: Understanding Algorithms and their applications using Python

No of pages: 100

Sub - Topics (Top 2):

1. Operations on Images

2. Image Transformations

Chapter 4: Machine Learning Approaches in Image Processing

Chapter Goal: Basic Implementation of Machine and Deep Learning Models, which takes care of Image Processing, before applications in real-time scenario

No of pages: 100

Sub - Topics (Top 2):

1. Image Classification and Segmentation

2. Applying Supervised and Unsupervised Learning approaches on Images using Python

Chapter 5: Real Time Use Cases

Chapter Goal: Working on 5 projects using Python, applying all the concepts learned in this book

No of pages: 100

Sub - Topics (Top 5):

1. Facial Detection

2. Facial Recognition

3. Hand Gesture Movement Recognition

4. Self-Driving Cars Conceptualization: Advanced Lane Finding

5. Self-Driving Cars Conceptualization: Traffic Signs Detection

Chapter 6: Appendix A

Chapter Goal: Advanced concepts Introduction

No of pages: 50

Sub - Topics (Top 2):

1. AdaBoost and XGBoost

2. Pulse Coupled Neural Networks