Practical AI for Healthcare Professionals: Machine Learning with Numpy, Scikit-learn, and TensorFlow, Paperback/Abhinav Suri

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• Brand: Apress
• Categoria: Foreign Books
• Magazin: elefant.ro
• Ultima actualizare: 21-12-2024 01:38:29

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Descriere magazin:
Chapter 1: Introduction to AI and feasibility - AI, ML, Big Data: What do the buzzwords mean? - Defining a problem - What can and cannot be solved - Common algorithmic alternatives - You think you need AI, now what? - Data considerations for Healthcare & Patient Privacy - Cautionary tales of AI Snake Oil in Healthcare Chapter 2: AI in theory - Classification problems in the field of healthcare - Decision trees - Logistic regression - Support vector, achines - Neural Networks and Deep Learning - Convolutional Neural Networks - Evaluation metrics for AI-driven diagnostic tools Chapter 3: Overview of Programming - Introduction to Python and environment set up - Control Structures & Loops - Data structures - Functions - File I/O - Classes - Packages/Libraries - Numpy & Matplotlib Chapter 4: Project #1 ML & Diabetes - Problem overview and why ML might be the best - Introduction to scikit-learn - Data Pre-processing - Try 1: Decision Trees - Try 2: k Nearest Neighbors - k-fold Cross Validation - Takeaways Chapter 5: Project #2 Neural Networks & Heart Disease - Problem overview and why neural networks might work - Introduction to keras - Data Pre-processing - Model design and implementation - Measure Efficacy - Takeaways Chapter 6: Project #3 CNNs & Brain Tumor Detection - Problem overview - Overview of segmentation problems and Mask-RCNN - Data Pre-processing & Working with MRI images - Data Augmentation - Model design and implementation - Measure Efficacy with Dice Score and AP metrics - Takeaways Chapter 7: The Future of Healthcare and AI - Review of book - Problems in Medical AI: Data Issues - Medical Problems waiting to be solved