Machine Learning For Absolute Begginers A Step By Step Guide: Algorithms For Supervised And Unsupervised Learning With Real World Applications
By Raymond Kazyua and William Sullivan
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About this ebook
Machine learning occurs primarily through the use of " algorithms" and other elaborate procedures.
Whether you're a novice, intermediate or expert this book will teach you all the ins, outs and everything you need to know about machine learning.
Instead of spending hundreds or even thousands of dollars on courses/materials why not read this book instead?
Its a worthwhile read and the most valuable investment you can make for yourself.
What You'll Learn
- Supervised Learning
-Unsupervised Learning
-Reinforced Learning
- Algorithms
-Decision Tree
-Random Forest
-Neural Networks
-Python
-Deep Learning
-And much, much more!
☆★☆ Grab your copy now! ☆★☆
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Machine Learning For Absolute Begginers A Step By Step Guide - Raymond Kazyua
Conclusion
Introduction
Chapter One: What is Machine Learning?
Subjects involved in machine learning
Chapter Two: Uses of Machine Learning
Density Estimation
Latent Variables
Reduction of Dimensionality
Visualization
Varieties of Machine Learning
Chapter Three: Common Machine Learning Terms
Classification
Regression
Decision Trees
Clustering
Support Vector Machines
Deep Learning
Neural Networks
Generative Model
Chapter Four: Supervised Machine Learning
Overview
Issues to consider in Supervised Learning
Other factors to consider
Chapter Five: Unsupervised Machine Learning
Chapter Six: Reinforcement Learning
Applications of Reinforcement Learning
Chapter Seven: Machine Learning Algorithms
K Nearest Neighbors
Naïve Bayes Estimation and Bayesian Networks
Regression Modeling
Support Vector Machine
Decision Trees
Genetic Algorithms
Conclusion
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Introduction
I want to thank you for choosing this book, ‘Machine Learning For Absolute Beginners A Step by Step guide’
Machines have come a long way since the Industrial Revolution. Every house, office and factory has some machines, but it is only in recent times that we have tried to estimate the capabilities of machines and how they can be used as substitutes for manual tasks. The scope of work for machines has extended to tasks that involve simulating cognition. These tasks could be performed only by human beings until recently. Judging competitions, beating professional chess players and driving cars are some of the few tasks that machines can now perform.
Some of the capabilities of machines have begun to instill fear in some observers. Most of the fear nestles on the insecurities that human beings have about survival, and it is this fear that gives rise to the what if questions.
● What if machines decided they were superior to human beings and decided to fight us?
● What if they developed the functionality to procreate and produce offspring with outstanding capabilities?
● What if the myth surrounding singularity is true?
The other fear that most human beings have is the threat that machines pose to job security. According to BBC’s interactive online survey, Will a robot take my job?
job titles like waiters/waitresses, chartered accountants, taxi drivers, bar workers and receptionists will become automated by the year 2040.
Research studies on job automation should always be read with some skepticism since the future of artificial intelligence and machine learning is unknown.