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. Discover why Machine Learning and Big Data are interdependent.

Machine Learning Definition: 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 Machine Learning is very effective in situations where the insights to be discovered from large sets of diverse and changing data, ie: 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.

Machine Learning and Big Data: 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 .

The basic analytical methods are used by business intelligence tools and reporting for the report are, for the accounts and 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.

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 the Python computer language. The latter, quite simple to learn, 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.

Machine Learning and Big Data: Why Machine Learning Is Nothing Without Big Data


Without Big Data, Machine Learning and Artificial Intelligence would be nothing. The data is the instrument that allows the AI to understand and learn about how 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. The technology is now sufficiently agile to access huge data sets and analyze . In fact, companies from all industries are now joining Google and Amazon to implement AI solutions for their businesses.

An example of machine learning applied? MetLife, one of the world’s leading corporate insurers, uses this technology and Big Data to optimize its business . Speech recognition has allowed him to improve the tracking of accidents and better measure their consequences. Claims processing is now better supported because claim templates have been enriched with unstructured data that can be analyzed by Machine Learning.

Another example, this technique allows to learn the habits of the occupants of a home. Designers of connected objects, including thermostats, can analyze the temperature of the dwelling to understand the presence and absence of occupants to turn off the heater and turn it back on a few minutes before they return.

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, he 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, unsupervised learning in which the machine will feed on data not previously selected, 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 or 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

Machine Learning and Big Data: Predictive Analytics Make Big Data Meaningful


The predictive analysis is to use the data, statistical algorithms and Machine Learning techniques to predict the likelihood of trends and financial results of companies, based on the past. They bring together several technologies and disciplines such as statistical analysis, data mining, predictive modeling and Machine Learning to predict the future of businesses . For example, it is possible to anticipate the consequences of a decision or the reactions of consumers.

The predictive analytics can produce actionable insights from large data sets, to allow companies to decide which direction to take and subsequently deliver a better customer experience . With increased data, computing power, and the development of easier-to-use AI software and analytical tools like Salesforce Einstein, many companies can now use predictive analytics.

According to a Bluewolf survey of 1,700 Salesforce customers, 75 percent of companies that increase their investments in analytics technology are benefiting . 81% of these Salesforce product users believe that using predictive analytics is the most important initiative in their sales strategy. Predictive analytics can automate decision-making, increasing the profitability and productivity of a business.

Artificial intelligence and Machine Learning represent the top level of data analysis. The cognitive computer systems are constantly learning about the company and intelligently predict industry trends, consumer needs and much more . Few companies have already reached the level of cognitive applications, defined by four main characteristics: the understanding of unstructured data, the ability to reason and extract ideas, the ability to refine expertise at each interaction, and the ability to see, speak and hear to interact with humans in a natural way.

Machine Learning and Big Data: Machine Learning for Data Management


Faced with the massive increase in the amount of data stored by companies, companies face new challenges. Key challenges in Big Data include understanding Dark Data , data retention, data integration for better analytics, and data accessibility. The Machine Learning can be very helpful in addressing these challenges .

All companies accumulate over time large amounts of data that remain unused . It’s about dark data. Thanks to Machine Learning and the various algorithms, it is possible to sort through the different types of data stored on the servers. Then, a qualified human can review the classification scheme suggested by artificial intelligence, make the necessary changes, and put it in place.

For data retention, this practice can also be effective. Artificial intelligence can identify data that is not being used and suggest which data can be deleted . Even if the algorithms do not have the same capacity of discernment as the human beings, the Machine Learning makes it possible to make a first sorting in the data. This saves employees valuable time before permanently deleting obsolete data.

Machine Learning is also useful for data integration. In an attempt to determine the type of data that they need to aggregate for their queries, analysts typically create a directory in which they place different types of data from various sources to create a pool of analytic data . To do this, it is necessary to develop integration methods to access the different data sources from which they extract the data. This technique can facilitate the process by creating mappings between the data sources and the directory. This reduces the integration and aggregation time.

Finally, data learning makes it possible to organize the storage of data for better access. Over the last five years, vendors of data storage solutions have put their efforts into automating storage management . With the price reduction of SSD, these advances in technology enable IT organizations to use intelligent storage engines based on the Learning machine to see which data types are used most often and which are hardly ever used. Automation can be used to store data according to algorithms. Thus, optimization does not need to be done manually.