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Machine Leaning – What It Is and Why It Matters?

Wed, 2017-11-01 20:04
Machine Leaning

From self-driving cars to disaster predictions programs and from cancer detection system to other current artificial intelligence technologies, machine learning has opened the new standards for the newer subset and deep learning. According to one survey of industry experts at an AI conference, intelligent machines will be able to perform any intellectual task a human can perform by the year 2050. As such, the demands of AI professionals are growing among companies that know the ins and outs of Machine Learning (ML). Let’s know what Machine Learning is in details.

What is Machine Learning?

A data analytics technique that teaches the computers to do or these are the conceptual programs that are refined through experience with an aim to improve the performance. Basically, Machine Learning is considered as an extension to Artificial Intelligence, which improves the performance through limited programming intervention and adapts the newer subsets through continuous practice. However, ML is not a new innovation, in fact, has been around for years but the ability to apply complex algorithms to big data, in a loop and more rapidly, is a recent development.

Apart from some basic functioning, the most complex use of ML would be early fraud detection that can be useful for many sectors especially, finance sectors such as banking.  A lot of businesses and customers are able to perform emotional and sentimental analysis and that is possible through data mining techniques, again a direct product of Machine Learning.

Why it Matters?

It is important to know why Machine Learning matters to get an idea about the intrinsic value of the field along with methods and open questions that may arise in the mind of anyone. As we already discussed, machine learning provides tools to generate the solution of complex problems faster, more accurate and more scalable then we could program a solution manually. Let’s point out some situations to examine that why Machine learning is important:

  • To control the complex inputs of the program
  • To specify how to achieve the goal for any program
  • To map out what decision a program will make and what conditions it makes them
  • To test program and measure end results as all inputs an outs are known for all conditions to achieve the goal.
  • For data preparation capabilities
  • Basic to Advanced level algorithm solution
  • Automation and Iterative processes
  • Scalability
  • Ensemble Modeling

These points clearly state the vast scope of Machine Learning. Now let’s dig dipper for a while:

Machine Learning in today’s World:

Everywhere and in every organization, algorithm-based solutions are preferred to build a model that uncovers connections and to make better decision ability without any human interventions.  Now let’s know about the technology impacting the real-time organizations that are shaping the world that we live in.

  1. Financial Services: Banks and other businesses in the financial industry use machine learning for two key purposes that are to identify important insights on data and to prevent fraud.
  2. Government: Public safety and utilities in government sector use machine learning to increase efficiency, save money, detect fraud and minimize identity theft.
  3. Health Care: In health-care industry to assess patient’s health in real time and to improve diagnoses and treatments.
  4. Marketing and Sales: E-commerce websites use machine learning to check previous purchase of a buyer along with buyer history and promote other items that you’d be interested in.
  5. Oil and Gas: Analyzing minerals in the ground, Predicting refinery sensor failure, Streamlining oil distribution to make it more efficient and cost-effective.
  6. Transportation: To identify patterns and trends is the key to the transportation industry.

Popular Machine Learning Methods:

Here, we have brought you an overview of the most popular machine learning methods:

  1. Supervised Learning: Useful where credit card transactions are likely to be fraudulent or which insurance customer is likely to file a claim.
  2. Unsupervised Learning: useful where self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition are needed.
  3. Semi Supervised Learning: Semi supervised learning is useful when the cost associated with labeling is too high to allow for a fully labeled training process.
  4. Reinforcement Learning: It is often used for robotics, gaming and navigation.

Hopefully, the above information gave a brief representation to the concept what is machine learning and why it matters. You may also have an idea how much it is trending. People, who have command in the concept, are sought in the big industries and this scope is not outdated for coming years as well. According to a popular research by pay scale, A Machine Learning Engineer earns an average salary of $100,956 per year. If you are looking for a career in machine learning or to become an AI professional, please call us at +91 90691 39140 or write us your query at info@hub4tech.com. Our career guiding consultants will get back to you and tell you the complete roadmap how you can start your career in this direction.

If you are already preparing for the technology, you can also check your level by participating in our online quiz. To read our articles on other technologies, please click here.

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Meenakshi Goyal
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