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By this PDF notes you can create Machine learning models for better performance in your organization. In this practical guide you can learn that how to solve real-time problems. It provides techniques that how to apply machine learning in your organizations and for AI applications. This PDF notes is for researchers, students, developer and those who interested in AI and Machine Learning.
You Cover These Topics:
What is Artificial Intelligence?
What is Machine Learning?
What is Data Science?
A Multifaceted Discipline
Examining Artificial Intelligence
Reactive Machines
Software
Data
What is Data?
Types of Data
Big Data
Volume
Variety
Small Data
Meta Data
Healthcare Data: Little and Big Use cases
Evolution of Data and its Analytics
What is Machine Learning?
Basics
Agents
Autonomy
Interface
Performance
Goals
Knowledge
Environment
Supervised Learning
Optimization
Machine Learning Algorithms
Defining Your Machine Learning Project
Performance
Experience
Decision Trees
Ensembles
Linear Regression
Logistic Regression
Deep Learning
Unsupervised Learning
Clustering
Neural Language Processing
Evaluating Learning for Intelligence
Model Developing and Workflow
Parameters and Hyperparameter
Tuning Hyperparameter
Multivariate Testing
Ethics of Intelligence
What is Ethics?
Data Ethics
Informed Consent
Freedom of Choice
Public Understanding
Who Own the Data
What Can the Data be Used For?
Security
Prediction Ethics
Future of Healthcare
Shifting form Volume to Value
Evidence Based Medicine
Vision of the Future
Connected Medicine
Smart Implantable
Smart Places
Reductionism
Case Studies
Case Study selection
Conclusion
Challenges
Project Aims
Project Description
Platform Services
Community Forum
Real-World Evidence
