Machine Learning is an Artificial Intelligence technology that allows computers to learn without having been explicitly programmed for that purpose. To learn and grow, however, computers need data to analyze and to train on. In fact, Big Data is the essence of Machine Learning, and Machine Learning is the technology that makes full use of the potential of Big Data.
What is Machine Learning?
If Machine Learning is not new, its precise definition remains confusing for many people. Concretely, it is a modern science for discovering patterns and making predictions from data based on statistics, data mining, pattern recognition and predictive analysis. The first algorithms were created in the late 1950s. The best known of them is none other than Perceptron.
Machine Learning is very effective in situations where insights must be discovered from large and diverse datasets, i.e., Big Data. For the analysis of such data, Machine Learning is much more efficient than traditional methods in terms of accuracy and speed. For example, based on information associated with a transaction such as amount and location, and historical and social data, Machine Learning can detect potential fraud in a millisecond. Thus, this method is much more efficient than traditional methods for analyzing transactional data, data from social networks or CRM platforms.
Why Use Machine Learning with Big Data?
Traditional analytical tools are not powerful enough to fully exploit the value of Big Data. The volume of data is too large for comprehensive analysis, and the correlations and relationships between these data are too important for analysts to test all the assumptions to derive a value from these data.
Basic analytics are used by Business Intelligence and reporting tools to report sums, to do accounts, and to perform SQL queries. Online analytical treatments are a systematic extension of these basic analytical tools that require the intervention of a human to specify what needs to be calculated.
How it works?
Machine Learning is ideal for exploiting the hidden opportunities of Big Data. This technology makes it possible to extract value from massive and varied data sources without having to rely on a human. It is data-driven, and fits the complexity of the huge data sources of Big Data. Unlike traditional analytical tools, it can also be applied to growing datasets. The more data injected into a Machine Learning system, the more the system can learn and apply the results to higher quality insights. Machine Learning thus makes it possible to discover patterns buried in data more effectively than human intelligence.
Machine Learning courses are available on the web. In particular, they make it possible to start Machine Learning from Python. It is a computer language that is not quite hard to learn, and it allows neophytes to test applications using Machine Learning with Python. Likewise, Machine Learning’s open classroom allows you to discover the operation of this data processing technique for free.
Why Machine Learning Is Nothing without Big Data?
Without Big Data, Machine Learning and Artificial Intelligence would be nothing. Data is the instrument that allows AI to understand and learn the way humans think. Big Data accelerates the learning curve and automates data analysis. The more a Machine Learning system receives data, the more it learns and the more accurate it becomes.
Artificial Intelligence is now able to learn without the help of a human. For example, the Google DeepMind algorithm recently learned to play only 49 Atari video games. In the past, development was limited by the lack of available data sets, and by its inability to analyze massive amounts of data in seconds.
Today, data is accessible in real time at any time. This allows AI and Machine Learning to move to a data-driven approach. Technology is now agile enough to access and analyze colossal datasets. In fact, companies from all industries are now joining Google and Amazon to implement AI solutions for their businesses.
Deep Learning, a sub-domain of Machine Learning
Machine learning is a subfield of Artificial Intelligence. Deep Learning is itself a subcategory of Machine Learning. The most common application example is visual recognition. For example, an algorithm will be programmed to detect certain faces from images coming from a camera. Depending on the database assigned, it can locate a wanted individual in a crowd; detect the satisfaction rate at the exit of a store by detecting smiles, etc. An algorithm set can also recognize the voice, the tone, the expression of a questioning, an affirmation and the words.
To do this, Deep Learning is mainly based on the reproduction of a neural network inspired by the brain systems present in nature. The developers decide according to the desired application what type of learning they will put in place. In this context, we speak of supervised learning, unsupervised learning in which the machine will feed on unselected data, semi-supervised, reinforcement (linked to an observation), or transfer in which the algorithms will apply a learned solution in a situation never seen.
In contrast, this technique requires a lot of data to train and get enough success rates to be used. A Data Lake is essential to perfect the learning of Deep Learning algorithms. Deep learning also requires higher computing power to perform one’s duties. You have to equip yourself.