62 lines
4.5 KiB
Markdown
62 lines
4.5 KiB
Markdown
---
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title: Machine Learning
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---
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## Machine Learning
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Arthur Samuel, a pioneer in artificial intelligence, defined Machine Learning in 1959 as "the field of study that gives computers the ability to learn without being explicitly programmed."
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A more formal definition of Machine Learning is provided by Prof Tom Mitchell of CMU:
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> "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E."
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Consider the example of a Machine Learning algorithm that plays chess. In this example, `E` refers to the experience of playing chess, `T` is the task of playing chess, and `P` denotes the probability that the program will win the next game of chess.
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Machine learning is exactly like how a human being learns. For example if a human wants to learn how to play poker, it will firstly learn the rules. Then it will try to get experience by playing the game. This experience is nothing but a huge data set for a machine by using which it can make intelligent decisions reagrding the proposed problem.
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In general, machine learning problems can be classified into supervised learning, and unsupervised learning. In supervised learning, you have the input and the labeled output, and you suspect that a relationship exists between the input and the labeled output. When you know neither what the labeled output is nor if a relationship exists, unsupervised learning will help you find structure in your data if there is one.
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We've covered two main categories of machine learning, but there are four broad categories of machine learning:
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1. Supervised learning
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2. Unsupervised learning
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3. Semi-supervised Learning
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4. Reinforcement Learning
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### Supervised learning
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Supervised learning is the machine learning task of inferring a function from supervised training data. The training
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data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object
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(typically a vector) and a desired output value (also called the supervisory signal). Further, the supervised learning can be taken as 2 paradigm, classification and regression.
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#### Basic flowchart/steps for supervised learning
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1. Collect training set.
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2. Divide training set into input object (features) and output object (classes or value)
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3. Decide what you will be applying, regression or classifier
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4. Decide which algorithm will you be applying, SVM, deep net, etc
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5. Run the algorithm on training set and use the model for predictions
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#### Courses:
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1. <a href='https://www.udacity.com/course/intro-to-machine-learning--ud120?autoenroll=true' target='_blank' rel='nofollow'>Intro to Machine Learning</a>
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2. <a href='https://www.coursera.org/learn/machine-learning' target='_blank' rel='nofollow'>Machine Learning - Taught by: Andrew Ng</a>
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3. <a href='https://www.udemy.com/data-science-and-machine-learning-with-python-hands-on/' target='_blank' rel='nofollow'>Data Science and Machine Learning with Python - Hands On!</a>
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4. <a href='http://ciml.info/' target='_blank' rel='nofollow'>Machine Learning</a>
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5. <a href='https://www.edx.org/course/the-analytics-edge' target='_blank' rel='nofollow'>The Analytics Edge - Taught by: MIT</a>
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#### Video Resources:
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1. <a href="https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A" target="_blank">Siraj Raval's Youtube channel</a>
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2. <a href="https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ" target="_blank">Sentdex's Youtube channel</a>
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#### More Information:
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1. <a href='https://en.wikipedia.org/wiki/Machine_learning' target='_blank' rel='nofollow'>Machine Learning on Wikipedia</a>
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2. <a href='https://www.youtube.com/watch?v=83uAOzhzs-U' target='_blank' rel='nofollow'>Machine Learning Demystified:Youtube</a>
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3. If you want a brief introduction of machine learning, and you prefer videos, try this <a href='https://youtu.be/cKxRvEZd3Mw' target='_blank' rel='nofollow'>machine learning introduction video</a>
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4. If you want to know how to proceed with learning machine learning, take a look at this <a href='https://youtu.be/nKW8Ndu7Mjw' target='_blank' rel='nofollow'> video</a>
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5.<a href='https://www.datacamp.com/courses/supervised-learning-with-scikit-learn' target='_blank' rel='nofollow'>Supervised Learning on Data camp</a>
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## Lab
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<a href="https://github.com/Microsoft/computerscience/blob/master/Labs/AI%20and%20Machine%20Learning/Azure%20Machine%20Learning/Azure%20Machine%20Learning%20(Node).md">Building Smart Apps with Azure Machine Learning Studio</a>
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