Practical Machine Learning and Image Processing

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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 Image Processing Chapter Goal: Introduction to different concepts of Image Processing No of pages: 50 Sub - Topics (Top 2) 1. Terminologies related to Image Analysis 2. Flowchart of Image Processing Chapter 3: Python Operations Chapter Goal: Introduction to Python as a Programming Language No of pages: 50 Sub - Topics (Top 2): 1. Essentials of Python 2. Advanced Python Chapter 4: Image Processing and Analysis Algorithms Chapter Goal: Understanding Algorithms and their applications in Python No of pages: 100 Sub - Topics (Top 2): 1. Operations on Images 2. Image Segmentation Chapter 5: OpenCV - An Introduction Chapter Goal: Exploring the famous computer vision Library OpenCV, and understanding its essential elements No of pages: 100 Sub - Topics (Top 2): 1. Exploring OpenCV Algorithms 2. Applications on Images using Python Chapter 6: Machine Learning & Image Processing Chapter Goal: Applying Machine Learning Models on Processed Images. No of pages: 100 Sub - Topics (Top 2): 1. Applying Supervised and Unsupervised Learning approaches on Images using Python 2. Applying Advanced Artificial Neural Networks on Images Chapter 7: 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 2): Facial Recognition and Detection Self-Driving Cars Conceptualization Hand Gesture Movement Recognition Attendance Management Retail Stacks Management