Machine Learning for Beginner's, PDF

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Machine Learning for Beginners
A Complete and Phased Beginner’s Guide to Learning and Understanding Machine Learning and Artificial Intelligence
By: Ethem Mining



Table of Contents
Introduction
Chapter 1: What is Machine Learning?
Definition of Machine Learning
History of Machine Learning
The Future of Machine Learning
Application of Machine Learning
Technology Industry
Agricultural Industry
Medical Industry
Financial Industry
Marketing Industry
Human Behavior Industry
Benefits of Machine Learning
Practical Examples of Everyday Use of Machine Learning
Chapter 2: Machine Learning Methods
Supervised Learning Method
Unsupervised Learning Method
Semi-Supervised Learning Method
Reinforcement Learning Method
Other Learning Methods
Chapter 3: Big Data Analysis
What is Big Data?
Why is Big Data Important?
How is Big Data Used?
Applications of Big Data in Today’s World
Big Data Analysis Tools
Zoho Analytics
Cloudera
Microsoft Power BI
Oracle Analytics Cloud
Pentaho Big Data Integration and Analytics
SAS Institute
Sisense
Splunk
Tableau
Big Data and Machine Learning
Chapter 4: Machine Learning Algorithms
What is An Algorithm?
What Are Machine Learning Algorithms?
What is the Use of Machine Learning Algorithms?
Chapter 5: K Means Clustering Algorithm
What Is the K Means Clustering Algorithm?
How Does This Algorithm Work?
When Should This Algorithm be Used?
Behavioral Segmentation
Inventory Categorization
Sorting Sensor Measurements
Detecting Bots or Anomalies
Tracking and Monitoring Classification Change
Chapter 6: Artificial Neural Networks
What Are Artificial Neural Networks?
How Does They Work?
When Should They be Used?
Identification and Process Control
General Game Playing
Various Forms of Recognition
3D Reconstruction
Diagnosis
Finances
Filtering
Chapter 7: Decision Trees
What Are Decision Trees?
How Do Decision Trees Work?
The Root Node
Splitting
The Decision Node
The Leaf Node
Pruning
Branches
Parent and Child Nodes
How the Tree Works
How the Tree is Read
When Should Decision Trees be Used?
Business Decisions
Government Decisions
Educational Decisions
Programming Decisions
Chapter 8: Naïve Bayes Classifier Algorithm
What Is the Naïve Bayes Classifier Algorithm?
How Does This Algorithm Work?
Multinomial Naïve Bayes
Bernoulli Naïve Bayes
Gaussian Naïve Bayes
When Should This Algorithm be Used?
Filing Documents
Spam and Priority Filters
Chapter 9: Random Forests
What Are Random Forests?
How Do Random Forests Work?
When Should Random Forests be Used?
Chapter 10: Apriori Algorithm
What Is the Apriori Algorithm?
How Does This Algorithm Work?
When Should This Algorithm be Used?
Marketing
Commerce
Statistical Analysis Companies
Mechanics
Service Engineers
Chapter 11: Linear and Logistic Regression
What is Linear Regression?
Multiple Linear Regression
Ordinal Regression
Multinominal Regression
Discriminant Analysis
How Does Linear Regression Work?
When Should Linear Regression be Used?
Budgeting
Agriculture
Retail – Ordering
What is Logistical Regression?
How Does Logistic Regression Work?
When Should Logistic Regression be Used?
Conclusion




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