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

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