Hacking A.I: big and complete guide to hacking, security, AI and big data

Introduction
Chapter One: An Introduction to Computer Systems and Networking
Chapter Two: Inside the Computer Systems and Networking Concept
Chapter Three: Computer Systems Network and Security
Chapter Four: Computer System Network: Setting Up Your Own
Chapter Five: IP And Subnetting Explained
Chapter Six: Applying the Concepts of Computer Systems Network
Chapter Seven: Outside the Network Numbers: What Still Matters
Conclusion
Resources:
Introduction
Chapter 1: Black Hat Hacking
Chapter 2: White Hat Hacking
Chapter 3: Grey Hat Hacking
Chapter 4: Networks
Chapter 5: Scripting and Other Tools
Chapter 6: The Different Types of Hacking and How they Work
Chapter 7: How to Protect yourself from Hacking
Chapter 8: Cybersecurity and How it Saves you from being Hacked
Conclusion
Artificial Intelligence
And Life
Introduction
Chapter One: Foundations of AI
Philosophy and Logic
From Life Itself
Psychology and Cognitive Science
Statistics and Probability
From Computer Engineering
Chapter Two: What is AI?
The Turing Test Approach: Acting Humanly
The Cognitive Modeling approach: Thinking humanly
The Laws of Thought Approach: Thinking Rationally
The Rational Agent Approach: Acting Rationally
Chapter Three: Basic Concepts in AI
Data
Information
Knowledge
Intelligence
Artificial Intelligence
Classifications
Association
Decision Trees
Deep Learning
Chapter Four: How Machines Learn
Forms of Learning
Components to be Improved
Representation and Prior Knowledge
Feedback to Learn From
	Unsupervised Learning
	Reinforcement Learning
	Supervised Learning
	Semi-supervised Learning
Chapter Five: Machine Learning
Memory-Based Learning
Case-Based Reasoning
Decision Trees
Data Mining and Decision Trees
EPAM
CLS
ID3
C4.5, CART and Successors
Inductive Logic Programming
Neural Networks
	Input layer
	Output layer
	Hidden layer
Unsupervised Learning
Reinforcement Learning
Chapter Six: Big data
Essentials of Big Data
Sources of Big Data
	Public sources
	Private sources
	Building new data from existing data
	Using existing data sources
Statistics and Machine Learning
Role of Algorithms
Strategies for Algorithms
	Symbolic Reasoning
	Brain Modeling
	Evolutionary Modeling
	Bayesian Inference
Training Data Sets
	Representation
	Evaluation
	Optimization
Chapter Seven: Modern AI
Games
	Chess
	Checkers
AI at Home
Advanced Driver Assistance Systems
Route Finding Maps
Recommendation Systems
In Medicine
For Scheduling
For Automated Trading
In Business Practices
In Translating Languages
For Facial Recognition
Conclusion
Sources
Introduction
Chapter One: Big Data and Data Science
History of Data Science
Definition of Data Science
Who is a Data Scientist?
How Data Scientists Increase the Worth of a Business
The Technique of Data Science
Impacts of Data Science
Importance of Data Science
Programming Languages Every Data Scientist Should Know
	Python
	R Language
	Java
	Scala
	SQL
	Julia
	Matlab
The Data Science Process
The Future of Data Science as a Carrier Choice
Chapter Two: Cyber Security
Introduction
Most Common Cybersecurity Threats
	● Trojan horses
	● Man in the Middle attacks
Impacts of Cyber Attacks on Business
	The financial cost of cyber-attack
	Reputational Impact of a Cyber-Attack
	Legitimate Impact of a Cyber-Attack
	Psychological Impact of Cyber-Attack
	Physical Impact of a Cyber-Attack
	Social Impact of a Cyber-Attack
How can Cyber-Attacks be Reduced?
	Creating responsiveness of cyber-security within an organization
	Investing in cyber safety and cyber-security backup
	Keeping up to date with all of the safety arrangements and testing the security measures regularly
Chapter Three: Cyber Technology
Features that Must be Present in a Cyber Technology Platform
Best Cybersecurity Practices for Businesses
The Future of Cyber Security
How AI (Artificial Intelligence) will shape the future of Cyber Security Methodologies
Chapter Four: Analytics and Metrics for Big Data
Analytics and Metrics of Big Data and Data Science
Cybersecurity Analytics and Metrics
Conclusion
Bibliography