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Machine Learning: Fundamental Algorithms for Supervised and Unsupervised Learning with Real-World Applications
Computers can't LEARN... Right?! Machine Learning is a branch of computer science that wants to stop programming computers using a list of detailed instructions and instead use a set of high-level commands which they can apply to many unknown scenarios - these are called algorithms.
In practice, they want to give computers the ability to Learn and to ADAPT. We can use these algorithms to obtain insights, recognize patterns and make predictions from data, images, sounds or videos we have never seen before (or even knew existed). Unfortunately, the true power and applications of today's Machine Learning Algorithms is misunderstood by most people.
Through this book I want fix this confusion, I want to shed light on the most relevant Machine Learning Algorithms used in the industry:
Supervised Learning Algorithms
K-Nearest Neighbour
NaIve Bayes
Regressions
Unsupervised Learning Algorithms:
Support Vector Machines
Decision Trees
In practice, they want to give computers the ability to Learn and to ADAPT. We can use these algorithms to obtain insights, recognize patterns and make predictions from data, images, sounds or videos we have never seen before (or even knew existed). Unfortunately, the true power and applications of today's Machine Learning Algorithms is misunderstood by most people.
Through this book I want fix this confusion, I want to shed light on the most relevant Machine Learning Algorithms used in the industry:
Supervised Learning Algorithms
K-Nearest Neighbour
NaIve Bayes
Regressions
Unsupervised Learning Algorithms:
Support Vector Machines
Decision Trees
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