You may often wonder about machine learning. It is a process of learning providing the ability to the systems to learn and improve automatically. It would be done with experience without the need to program it specifically. It emphasizes on the development of computer programs accessing the data and using it for independent learning.
Machine learning has several benefits and drawbacks. Let us delve on the pros and cons of taking up machine learning as your career.
Benefits of machine learning
Find below a few essential benefits of machine learning you should be aware of before taking it as a career.
- Helps in identifying patterns and trends easily
It would entail reviewing large volumes of data for discovering specific patterns and trends not easily perceived by people. For instance, machine learning would relatively useful for an e-commerce website for understanding buying histories and browsing behaviors of the users. It would help you cater to the best deals, products, and reminders relevant to them.
- No need for human intervention
Machine learning would not require human intervention. It would provide the machines with the ability to learn on their own. It would help the machines make relevant predictions along with improving the algorithms independently or without human help. For instance, the anti-virus software would be a common example, as they would automatically filter the new threats as and when it is recognized.
- Provides continuous improvement
Machine learning algorithms would help improve the accuracy and efficiency with time and experience. It would help take better and informed decisions.
Drawbacks of machine learning
If you were contemplating taking up machine learning as a career, you should be aware of a few drawbacks associated with machine learning. It would help you make an informed decision.
- Requires massive time and resources
Machine learning would require a lot of time and massive resources to function properly. It could demand more computing power. You may need more time to let the algorithms learn and develop the purpose they have been created for with accuracy. It would fulfill your purpose in the best possible way.
- Massive datasets to train
Machine learning would require huge datasets for training purposes. It should be of good quality and unbiased. In a few situations, they might require waiting for the generation of the latest data.
- Accurate interpretation is challenging
Accurate interpretation of the results would be generated by the algorithms. However, it would be a daunting task nonetheless. You would be required to be prudent while choosing algorithms for their particular purpose.
- What are the Two Popular Reasons for Choosing Machine Learning
You may often wonder about the need to compare machine learning. What do you wish to compare to machine learning? If you were contemplating comparing machine learning to human learning, rest assured that machines could do it quickly and on a large scale.
Machine learning would require data, relatively more than humans do. The major reason would be the machines usually optimizes over an artificial space considered highly ridiculous to start. Humans would develop a decent idea about the data, even before looking at it. However, the computer would rely heavily on the data.
For instance, when a child is given a few pictures of animals he does not know of, he may not be able to recognize the animals. However, when given a picture of a popular animal, he may recognize it based on his knowledge. It would be relatively better and transfer it to the new problem. The computers might not do that well.
A good solution would be to let humans guide the machines. However, the machines should be enabled to making the ultimate decision derived by the gathered data. Future engineering would assist with human guiding.
You may come across a plethora of reasons why machine-learning algorithms have been used largely. Find below the two major reasons for the increasing popularity of machine learning.
The interesting benefits of machine learning would be the system randomly trained and initialized on a few datasets. It would eventually learn good feature representations for a given job. A classical approach would involve handcrafting features by the competent human. It may consume time for fine-tuning the various parameters to get it right. Presently, machine learning would be used for discovering relevant features in different datasets. These aspects would be useful in face recognition, face detection, image classification, or speech recognition. Deep learning would emphasize on building a higher-level feature representation of different layers of the data. It would be largely helpful in image and speech recognition.
It would be relatively similar to feature learning. It entails a group of parameters easy to tune and visualized as a feature. It would employ a specific method of optimizing a wide range of parameters. It would be pertinent to mention here that such parameters would be huge in numbers. Such a parameter, when set right, could help the system run smoothly and efficiently. It would be possible for a human to find the best possible setting for various parameters by hand. Therefore, large-scale machine learning algorithms would be used for finding the best possible setting.