Machine Learning

What is Machine Learning?

Machine learning is a widely used term. It is a type of artificial intelligence which allows software applications to predict more accurate outcomes without being explicitly programmed to do so. Software engineers develop algorithms which are used to feed data into artificial intelligence systems. The AI then uses the given data to come up with all the possible outcomes. We come across machine learning examples everyday. While watching a show or a movie on a streaming platform, it gives the user recommendations. The recommendations are a result of machine learning. The AI uses the input data, that is, the shows the user has watched recently or is currently watching, analyses the genre and language and uses this data to recommend a few shows which the user may like.

More popular uses of machine learning include detection of fraud, spam filtering and malware threat detection. Machine learning is gaining popularity among business owners and corporate firms. It provides essential data of customer trends and behaviour as well as business operation patterns. It also helps with the development of new products. Multinational companies such as Uber, Facebook and Google make use of machine learning artificial intelligence systems to predict customer behaviour and is a central part of their operations. Machine learning has now become a competitive differentiator for multinational and growing companies.

Machine learning is basically how an algorithm uses trial and error to become more accurate in its predictions. This is carried out by four different areas of approach.

  1. Supervised Learning – Input and output of the algorithm is defined and variables are supplied by data scientists.
  1. Unsupervised Learning – The algorithm is carried out on unlabeled data. It scans data sets from the same domains and searches for meaningful connections between the given data. The data and output predictions are predetermined.
  2. Semi-Supervised Learning – This involves a mix of the above two data types. The algorithm is free to explore the data set on its own and develop an understanding of it.
  3. Reinforcement learning – This method is often used by data scientists to teach the machine a multi-level process. Such a process has predefined rules to it.They then run the algorithm to complete a task, however the algorithm decides on its own what steps to take along the way.

Applications of Machine Learning

Machine learning is now being integrated into all our daily lives. From streaming sites to finding directions, machine learning applications can be found everywhere.

  1. Google Maps- How does the application know when and which route will have the most traffic? How long would it take to travel a certain distance between two points? This is where machine learning comes into play. The AI uses the information from people currently using the app, the previous data of the route collected and real time speed and location of its users. This data helps the AI to predict upcoming traffic and the user can then plan their route accordingly.
  2. Social Media – When tagging a friend on a post or searching for another user in the search bar of the application, the user gets many recommendations. This is the job of the AI system used on social media platforms.
  3. Production Recommendations – On shopping sites, users may search for an item but they may not end up buying it immediately. However, the next day, it so happens that the user gets an advertisement for a similar product on Youtube and another on Facebook. All these apps are owned by Google. Google tracks your search history and it recommends products based on it. This is a very commendable feature of machine learning.
  4. Google Translate- Finding it difficult to converse in a certain language? Travelling to another country but don’t know how to speak their language? In such cases, Google translate is the first thing which comes to mind. Google has had a huge monopoly over translation services and this may be due to the countless languages it translates into. The database includes thousands of languages and dictionaries, and it uses Natural Language Processing.

Advantages of Machine Learning

One of the greatest advantages to machine learning is the ability to go through large amounts of data in a small amount of time. It can even detect current and future trends and patterns which would not even be apparent to humans. Such a feat would take a human hundreds of hours, but the AI does this in mere minutes.

With machine learning, one does not have to keep checking the program ever so often. Since the word machine learning itself means giving the machine a chance to learn, the AI can make predictions and figure out patterns on its own. It can even improve the original algorithms to make them more efficient. A common example of this is anti-virus softwares. Machine learning is very helpful in detecting spam and threats.

There is continuous improvement to be made with machine learning. As the algorithms keep improving, their efficiency increases and as the amount of data increases, the algorithms make predictions faster.

Artificial intelligence data systems are equipped with the ability to solve multidimensional and multi-variety data of multi-variety. They can do this even in dynamic or uncertain environments. Machine learning has a huge scope for applications. It gives a much more personal experience to customers and has the ability to attract potential customers. All these features help to promote a healthy relation between a customer and a seller, or users and a company. Customer relationship management and human resource information systems can be enhanced using this technology.

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