An Introduction to Machine Learning
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What is Machine Learning?
Machine learning is a field of computer science that aims to teach computers how to learn and act without being explicitly programmed. More specifically, machine learning is an approach to data analysis that involves building and adapting models, which allow programs to "learn" through experience. Machine learning involves the construction of algorithms that adapt their models to improve their ability to make predictions.
According to Tom Mitchell, professor of Computer Science and Machine Learning at Carnegie Mellon, a computer program is said to learn from experience E with reference to some task T and a few performance measure P, if its performance on T, as measured by P, improves with experience E. A mathematical way of claiming that a program uses machine learning if it improves at problem-solving with experience.
The first uses and discussions of machine learning dates go back to the 1950s and its adoption has increased dramatically within the last 10 years. Some of the applications of machine learning include image recognition, Image Detection, NLP, design of AI, self-driving car technology, and Google's web search algorithm.
Types of Machine Learning Tasks
Machine learning algorithms all aim to find out and improve their accuracy as they process more datasets. One way that we can classify the tasks that machine learning algorithms solve is by how much feedback they present to the system. In some scenarios, the computer is provided a significant amount of labeled training data is provided, which is called supervised learning. In other cases, no labeled data is provided and this is known as unsupervised learning. Lastly, in semi-supervised learning, some labeled training data is provided, but most of the training data is unlabelled. Let's review each type in more detail:
Supervised learning is one of the most practical and widely adopted sorts of machine learning. It involves creating a mathematical function that relates input variables to the preferred output variables. A large number of labeled training datasets are provided which provide examples of the data that the computer will be processing.
Supervised learning tasks can further be categorized as "classification" or "regression" problems. Classification problems use statistical classification methods to output a categorization, for instance, "hot dog" or "not hot dog". Regression problems, on the other hand, use statistical regression analysis to provide numerical outputs.
Semi-supervised learning is equivalent to the supervised learning except that of the training data provided, only a limited amount is labeled.
Image recognition is a good example of semi-supervised learning. In this example, we might provide the system with several labeled images containing objects we wish to identify, then process many more unlabelled images in the training process.
In unsupervised learning problems, all input is unlabelled and therefore the algorithm must create structure out of the inputs on its own. Clustering problems (or cluster analysis problems) are unsupervised learning tasks that seek to discover groupings within the input datasets. Examples of this could be patterns in stock data or consumer trends. Neural networks are also commonly used to solve unsupervised learning problems.
Applications and Examples of Machine Learning
Machine learning is an area of study and an approach to problem-solving. And there are many different applications to which machine learning methods can be applied. Below are a few of the many applications of machine learning strategies and methods:
- NLP: NATURAL LANGUAGE PROCESSING: Natural language processing (NLP) is a field of computing that's primarily concerned with the interactions between computers and natural (human) languages. Major emphases of natural language processing include speech recognition, natural language understanding, and natural language generation. Machine learning methods can be applied to each of these areas.
- Bioinformatics and Medical Diagnosis: The amount of biological data being compiled by research scientists is growing at an exponential rate. This has led to problems with efficient data storage and management as well as with the ability to pull the useful information from this data. Currently, machine learning methods are being developed to efficiently and usefully store biological data, as well as to intelligently pull meaning from the stored data. Efforts are also being made to apply machine learning and pattern recognition techniques to medical records in order to classify and better understand various diseases. These approaches are also expected to help diagnose disease by identifying segments of the population that are the most at risk for certain diseases.
- Search Engines: Web search also benefits from the use of deep learning by using it to improve search results and better understand user queries. By analyzing user behavior against the query and results served, companies like Google can improve their search results and understand what the best set of results are for a given query. Search suggestions and spelling corrections are also generated by using machine learning tactics on aggregated queries of all users.
- Financial Market Analysis: Algorithmic trading and market analysis have become mainstream uses of machine learning and artificial intelligence in the financial markets. Fund managers are now relying on deep learning algorithms to identify changes in trends and even execute trades. Funds and traders who use this automated approach make trades faster than they possibly could if they were taking a manual approach to spotting trends and making trades.
- Image Processing and Pattern Recognition: Using computers to spot patterns and identify objects within images, videos, and other media files are way less practical without machine learning techniques. Writing programs to spot objects within a picture wouldn't be very practical if a specific code needed to be written for each object you wanted to spot.Instead, image recognition algorithms, also called image classifiers, can be trained to classify images based on their content. These algorithms are trained by processing many sample images that have already been classified. Using the similarities and differences of images they've already processed, these programs improve by updating their models every time they process a new image. This form of machine learning used in image processing is usually done using an artificial neural network and is known as deep learning.