Machine Learning: An overview

Machine Learning: An overview
Written by Nilima PaulDecember 22, 2021
14 min read
Machine Learning
Nilima Paul

Data Team

In this article, we will know about Machine Learning an overview

What is machine learning?

The entire idea of AI is sorting out manners by which we can train a PC to play out an assignment without expecting to give unequivocal guidelines. One more method for contemplating it is that we're attempting to "program" instinct in a PC. You and I can check out an email and effectively observe whether or not it's spam, yet how would you get a PC to do such an assignment? You could build an enormous tangled rationale foundation of "if.. then, at that point.." articulations to figure out the spam messages, yet it would be an aggravation to develop and presumably wouldn't function admirably. All things considered, the AI approach is to outfit the PC with abilities to acquire all alone and feed it a lot of models. AI is detonating as a field right now as individuals are understanding a huge number of errands that we can encourage PCs to perform by taking care of it enormous datasets.


Applications of ML

Before we go into the specifics of learning by machines, let's list some applications of machine learning to whet our appetite for what's to come.

Here's a list of some popular applications of machine learning.

  • Tagging faces, objects, and places in pictures.
  • Captioning images and videos.
  • Driverless cars, drones, robotic vacuum cleaners, and intelligent game-playing bots.
  • Recognizing hand-written text.
  • Transcribing human speech to text.
  • Translating speech or text in one language into another.
  • Recommending shopping items, movies, books, songs, or new stories based on a person's interest.
  • Filtering out email spam.
  • Categorizing email, text-documents, movies, songs, new stories or books into pre-defined categories or genres.
  • Predicting the next earthquake or weather event such as rain, thunderstorm, drought, or snowfall.
  • Discovering faulty industrial components or predicting the time-to-failure.
  • Estimating housing prices, credit scores, mortgage risk, and stock market movements.
  • Gauging customer sentiment based on textual reviews or social interactions.
  • Recommending candidates suitable for a job opening.
  • Predicting social connections.
  • Personalized search, education, healthcare, advertisements, travel, and shopping experiences.
  • Smart home automation
  • And, many, more ...
  • Machine learning is gradually becoming ubiquitous and part of pretty much everything we do. Even if you do not intend to be a practitioner of machine learning, it is still an essential concept to understand and appreciate.

What is the future of machine learning?

While AI calculations have been around for a really long time, they've achieved new fame as man-made brainpower has filled in noticeable quality. Profound learning models, specifically, power the present most exceptional AI applications.

AI stages are among big business innovation's most aggressive domains, with most significant sellers, including Amazon, Google, Microsoft, IBM and others, dashing to sign clients up for stage benefits that cover the range of AI exercises, including information assortment, information readiness, information arrangement, model structure, preparing and application organization.

As AI keeps on expanding in significance to business activities and AI turns out to be more viable in big business settings, the AI stage wars will just escalate.

Proceeded with examination into profound learning and AI is progressively centered around growing more broad applications. The present AI models require broad preparing to create a calculation that is exceptionally advanced to perform one assignment. In any case, a few specialists are investigating ways of making models more adaptable and are looking for strategies that permit a machine to apply setting gained from one assignment to future, various undertakings.

Advantages and Disadvantages of Machine Learning

Advantages of Machine Learning

There is an endless number of advantages of ML. We can take a look at the ones which are really helpful. The advantages of Machine Learning tell us how using ML would benefit us.

So, let’s have a look at the advantages of Machine Learning.

1. Automation of Everything

Machine Learning is responsible for cutting the workload and time. By automating things we let the algorithm do the hard work for us. Automation is now being done almost everywhere. The reason is that it is very reliable. Also, it helps us to think more creatively.

Due to ML, we are now designing more advanced computers. These computers can handle various Machine Learning models and algorithms efficiently. Even though automation is spreading fast, we still don’t completely rely on it. ML is slowly transforming the industry with its automation.

2. Wide Range of Applications

ML has a wide variety of applications. This means that we can apply ML on any of the major fields. ML has its role everywhere from medical, business, banking to science and tech. This helps to create more opportunities. It plays a major role in customer interactions.

Machine Learning can help in the detection of diseases more quickly. It is helping to lift up businesses. That is why investing in ML technology is worth it.

3. Scope of Improvement

Machine Learning is the type of technology that keeps on evolving. There is a lot of scope in ML to become the top technology in the future. The reason is, it has a lot of research areas in it. This helps us to improve both hardware and software.

In hardware, we have various laptops and GPUs. These have various ML and Deep Learning networks in them. These help in the faster processing power of the system. When it comes to software we have various UIs and libraries in use. These help in designing more efficient algorithms.

4. Efficient Handling of Data

Machine Learning has many factors that make it reliable. One of them is data handling. ML plays the biggest role when it comes to data at this time. It can handle any type of data

Machine Learning can be multidimensional or different types of data. It can process and analyze these data that normal systems can’t. Data is the most important part of any Machine Learning model. Also, studying and handling of data is a field in itself.

5. Best for Education and Online Shopping

ML would be the best tool for education in the future. It provides very creative techniques to help students study.

Recently in China, a school has started to use ML to improve student focus. In online shopping, the ML model studies your searches. Based on your search history, it would provide advertisements. These will be about your search preferences in previous searches. In this, the search history is the data for the model. This is a great way to improve e-commerce with ML.

Now in TechVidvan’s advantages and disadvantages of Machine Learning article, we will see what are the drawbacks of Machine Learning. Let’s start:

Disadvantages of Machine Learning

Similar to the advantages of Machine Learning, we should also know the disadvantages of Machine Learning. If you don’t know the cons, you won’t know the risks of ML. So, let’s have a look at these disadvantages:

1. Possibility of High Error

In ML, we can choose the algorithms based on accurate results. For that, we have to run the results on every algorithm. The main problem occurs in the training and testing of data. The data is huge, so sometimes removing errors becomes nearly impossible. These errors can cause a headache to users. Since the data is huge, the errors take a lot of time to resolve.

2. Algorithm Selection

The selection of an algorithm in Machine Learning is still a manual job. We have to run and test our data in all the algorithms. After that only we can decide what algorithm we want. We choose them on the basis of result accuracy. The process is very much time-consuming.

3. Data Acquisition

In ML, we constantly work on data. We take a huge amount of data for training and testing. This process can sometimes cause data inconsistency. The reason is some data constantly keep on updating. So, we have to wait for the new data to arrive. If not, the old and new data might give different results. That is not a good sign for an algorithm.

4. Time and Space

Many ML algorithms might take more time than you think. Even if it’s the best algorithm it might sometimes surprise you. If your data is large and advanced, the system will take time. This may sometimes cause the consumption of more CPU power. Even with GPUs alongside, it sometimes becomes hectic. Also, the data might use more than the allotted space.

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made with by Abhishek & Priyanka Jalan