What is machine learning?
A learning machine, broadly defined, is any device whose actions are influenced by past experiences.
— Nils J. Nilsson
Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "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."[Mitchell, T. (1997). Machine Learning. McGraw Hill. p. 2.]
Machine Learning is using generic algorithms to tell you something interesting about your data without writing any code specific to the problem you are solving.
The below is a similar explanation. You need not write the program to do the work required.
Machine learning, as a type of artificial intelligence (AI), enables computers to learn without being explicitly programmed, and to improve their functions when exposed to new data. By analyzing patterns in this data, the machine learning algorithms are self-adjusting based on a set of design rules.
http://www.softvision.com/blog/what-is-machine-learning/
Machine learning was initially part of AI. But AI abandoned it. Machine learning (ML), reorganized and recognized as its own field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics, fuzzy logic, and probability theory.
Machine learning and Data mining
Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).
In machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge.
https://en.wikipedia.org/wiki/Machine_learning
Statistics
Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalizable predictive patterns. According to Michael I. Jordan, the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics. He also suggested the term data science as a placeholder to call the overall field.
https://web.archive.org/web/20171018192328/https://www.reddit.com/r/MachineLearning/comments/2fxi6v/ama_michael_i_jordan/ckelmtt/?context=3
Articles in Medium by Adam Geitgey
1https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.w55suff6b
Machine Learning is using generic algorithms to tell you something interesting about your data without writing any code specific to the problem you are solving.
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Deep Learning and Convolutional Neural Networks
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A method invented in 2005 called Histogram of Oriented Gradients — or just HOG for short.
Algorithm - face landmark estimation.
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Sequence-to-sequence learning.
Statistical machine translation systems perform much better than rule-based systems if you give them enough training data. Franz Josef Och improved on these ideas and used them to build Google Translate in the early 2000s. Machine Translation was finally available to the world.
A recurrent neural network (or RNN for short) is a slightly tweaked version of a neural network where the previous state of the neural network is one of the inputs to the next calculation. This means that previous calculations change the results of future calculations!
The idea of turning a face into a list of measurements is an example of an encoding. We are taking raw data (a picture of a face) and turning it into a list of measurements that represent it (the encoding).
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The algorithm (roughly) described here to deal with variable-length audio is called Connectionist Temporal Classification or CTC.
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The new system is called Deep Convolutional Generative Adversarial Networks (or DCGANs for short).
How DCGANs work
To build a DCGAN, we create two deep neural networks. Then we make them fight against each other, endlessly attempting to out-do one another. In the process, they both become stronger.
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Machine Learning Explained
https://blog.dataiku.com/machine-learning-explained-algorithms-are-your-friend
https://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/
How To Become A Machine Learning Engineer: Learning Path
Aug 19, 2017
https://hackernoon.com/learning-path-for-machine-learning-engineer-a7d5dc9de4a4
Machine learning - Notes
http://www.holehouse.org/mlclass/
Updated 18.11.2023, 21 July 2021, 15 July 2018, 24 June 2018, 6 October 2017, 23 August 2017, 30 July 2016
Machine Learning Explained
https://blog.dataiku.com/machine-learning-explained-algorithms-are-your-friend
Machine Learning MIT Course - Course Materials
http://www.ai.mit.edu/courses/6.867-f04/lectures.htmlMachine Learning Cheatsheet - SAS
https://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/
How To Become A Machine Learning Engineer: Learning Path
Aug 19, 2017
https://hackernoon.com/learning-path-for-machine-learning-engineer-a7d5dc9de4a4
Machine learning - Notes
http://www.holehouse.org/mlclass/
Updated 18.11.2023, 21 July 2021, 15 July 2018, 24 June 2018, 6 October 2017, 23 August 2017, 30 July 2016
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