Machine learning
Machine learning is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence.
Definition of machine learning
Machine-learning algorithms use statistics to find patterns in massive* amounts of data. And data, here, encompasses a lot of things — numbers, words, images, clicks, what have you. If it can be digitally stored, it can be fed into a machine-learning algorithm.
Machine learning is the process that powers many of the services we use today-recommendation systems like those on Netflix, YouTube, and Spotify; search engines like Google and Baidu; social-media feeds like Facebook and Twitter; voice assistants like Siri and Alexa. The list goes on.
In all of these instances, each platform is collecting as much data about you as possible — what genres you like watching, what links you are clicking, which statuses you are reacting to — and using machine learning to make a highly educated guess about what you might want next. Or, in the case of a voice assistant, about which words match best with the funny sounds coming out of your mouth.
Machine learning (ML)
is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks. for more information: https://en.wikipedia.org/wiki/Machine_Learning_(journal)
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. watch this video for more information:
Artificial intelligence
Machine learning is one modern innovation that has helped man enhance not only many industrial and professional processes but also advances everyday living. It is a subset of artificial intelligence, which focuses on using statistical techniques to build intelligent computer systems in order to learn from databases available to it. Currently, machine learning has been used in multiple fields and industries. For example, medical diagnosis, image processing, prediction, classification, learning association, regression, etc.
The intelligent systems built on machine learning algorithms have the capability to learn from past experience or historical data. Machine learning applications provide results on the basis of past experience.
I recommend you to watch this video:
Programmers at OpenAI, an artificial intelligence research company, recently taught a gaggle of intelligent artificial agents — bots — to play hide-and-seek. Not because they cared who won: The goal was to observe how competition between hiders and seekers would drive the bots to find and use digital tools. The idea is familiar to anyone who’s ever played the game in real life; it’s a kind of scaled-down arms race. When your opponent adopts a strategy that works, you have to abandon what you were doing before and find a new, better plan. It’s the rule that governs games from chess to StarCraft II; it’s also an adaptation that seems likely to confer an evolutionary advantage.
So it went with hide-and-seek. Even though the AI agents hadn’t received explicit instructions about how to play, they soon learned to run away and chase. After hundreds of millions of games, they learned to manipulate their environment to give themselves an advantage. The hiders, for example, learned to build miniature forts and barricade themselves inside; the seekers, in response, learned how to use ramps to scale the walls and find the hiders.
These actions showed how AI agents could learn to use things around them as tools, according to the OpenAI team. That’s important not because AI needs to be better at hiding and seeking, but because it suggests a way to build AI that can solve open-ended, real-world problems.
The rules of the game were simple. Each AI team had between one and three agents, and games lasted 240 moves. The setting was a (virtual) square arena with boxes, ramps, and walls that agents could push around and that hiders could lock in place. The arena was based in a 3D physics simulator that prevented real-world impossibilities like agents walking through walls or two agents occupying the same space.
Seekers were frozen in place for the first 96 moves, giving hiders a chance to, well, hide. Each agent was programmed to maximize its point totals, and points were awarded to all the members of a team at once. Hiders gained a point if they were all concealed, and they lost a point if one of them was seen by a seeker. Seekers gained a point if they saw at least one hider, and they lost a point if they couldn’t find any hiders by the end of the round. The point system helped both sides learn better strategies.
Nevertheless, the way the AI agents used self-play and competition to develop tools does look a lot like evolution — of some variety — to some researchers in the field. Leibo notes that the history of life on Earth is rich with cases in which an innovation or change by one species prompted other species to adapt. Billions of years ago, for example, tiny algae-like creatures pumped the atmosphere full of oxygen, which allowed for the evolution of larger organisms that depend on the gas. He sees a similar pattern in human culture, which has evolved by introducing and adapting to new standards and practices, from agriculture to the 40-hour workweek to the prominence of social media.
How We Get Machines to Learn
There are different approaches to getting machines to learn, from using basic decision trees to clustering to layers of artificial neural networks (the latter of which has given way to deep learning), depending on what task you’re trying to accomplish and the type and amount of data that you have available.
While the emphasis is often placed on choosing the best learning algorithm, researchers have found that some of the most interesting questions arise out of none of the available machine learning algorithms performing to par. Most of the time this is a problem with training data, but this also occurs when working with machine learning in new domains.
The two biggest, historical (and ongoing) problems in machine learning have involved overfitting (in which the model exhibits bias towards the training data and does not generalize to new data, and/or variance i.e. learns random things when trained on new data) and dimensionality (algorithms with more features work in higher/multiple dimensions, making understanding the data more difficult). Having access to a large enough data set has in some cases also been a primary problem.
When a learning algorithm (i.e. learner) is not working, often the quickest path to success is to feed the machine more data, the availability of which is by now well-known as a primary driver of progress in the machine and deep learning algorithms in recent years; however, this can lead to issues with scalability, in which we have more data but time to learn that data remains an issue.
algorithms find natural patterns in data that generate insight and help you make better decisions and predictions. They are used every day to make critical decisions in medical diagnosis, stock trading, energy load forecasting, and more. For example, media sites rely on machine learning to sift through millions of options to give you song or movie recommendations. Retailers use it to gain insight into their customers’ purchasing behavior.
How Do You Decide Which Machine Learning Algorithm to Use?
Choosing the right algorithm can seem overwhelming — there are dozens of supervised and unsupervised machine learning algorithms, and each takes a different approach to learning.
There is no best method or one size fits all. Finding the right algorithm is partly just trial and error — even highly experienced data scientists can’t tell whether an algorithm will work without trying it out. But algorithm selection also depends on the size and type of data you’re working with, the insights you want to get from the data, and how those insights will be used.
by Google, research python is the best language for machine learning.
What is the best language for machine learning
simply after typing it in google, the result is PYTHON
Python leads the pack, with 57% of data scientists and machine learning developers using it and 33% prioritizing it for development. Little wonder, given all the evolution in the deep learning Python frameworks over the past 2 years, including the release of TensorFlow and a wide selection of other libraries.
Python is the best choice for beginners in the field. There are plenty of python libraries such as Teano, Keras, and sci-kit-learn that are available for machine learning, deep learning, artificial intelligence, NLP, etc. For example, Numpy is its library that helps to solve many computations and Pybrain is for using machine learning in python.
Another reason for its popularity is that its syntaxes are very simple and can be easily learned which makes algorithms easily implemented. It gives direct access to its users for predictive analytics. It is a preferred language for developers who are looking to frame better questions and expand their capabilities of existing machine learning systems.