Advantages and Disadvantages of Machine Learning
Till now, you’ve heard enough about how and where you can use Machine Learning to your benefit. You learned about the building blocks of Machine Learning which indicates its algorithms. You also came across the various tools that you can use to implement these algorithms. Apart from the current developments, we also discussed how Machine Learning can change and shape the future of Machine automation. However, before learning anything on a deeper level, you need to first analyze the advantages and disadvantages of machine learning.
You need to understand how you can use this to your benefit and how to avoid it from causing any havoc. This article from PythonGeeks will help you apprehend the various advantages as well as the possible disadvantages of Machine Learning. You’ll be able to comprehend this technology on your own and master the particular domain by analyzing the risk factor associated with it. Hence let us look at the numerous positives as well as the lesser-known negatives about Machine Learning.
Advantages of Machine Learning
Though there is no doubt that Machine Learning has been a great aid for human assistance. The list of the various rewards that are possible only due to the advancement in Machine Learning is hefty. However, we will discuss some of the major benefits that exist due to Machine Learning.
1. Ease of Automation
A highly influential effect of using Machine Learning in daily life is the ease of doing boring stuff which is prone to human error. Machine Learning algorithms prove to be a great tool for human assistants by automating decision-making tasks. This effort makes sure that developers have a lot of time to innovate other ideas which they could not work on due to doing mundane activities.
Some of the common instances of automation in our day-to-day life are chatbots and sentiment analysis. Imagine how much time and energy you can save since you can tarin machines in such a way that does not involve human interference. At any instance any negative comment is detected, the chatbot is trained in such a way that it takes care of the situation on a first-hand basis. No human intervention is required to do this job which saves a lot of time and energy. With this innovative technique, Machine Learning is leading the world to a more automated and easier world.
2. Scope of Improvement
Machine Learning algorithms are improving continually by learning from the previously stipulated data. By this mechanism, every time we provide a new dataset to the algorithm, it tries to enforce the previously applied solution to the dataset which makes it deliver even more accurate results. This also helps the algorithm to improve its decision-making capabilities and ensures a more precise and hassle-free result.
The big names in the field of the tech world like Amazon and Walmart process huge volumes of data daily. Incorporating Machine Learning algorithms to administer these datasets help these tech giants to achieve new standards of improvement. The accuracy of these algorithms helps them in delivering better product recommendations and associations to its users. This helps in training the datasets with continuous improvement.
3. Extensive Span of Application
Machine Learning algorithms are taking over each aspect of today’s world; from manufacturing to healthcare to defense, Machine Learning algorithms are everywhere. With advancements in techniques and low costs, Machine Learning models help the tech giants to achieve great profits in lower investments. Machine Learning algorithms are a great tool for companies to predict and analyze consumer needs and cater to them.
Applications like Spam Filtering, product recommendation, and text correction provide a huge aid to deliver convenient services to the consumers. Due to these reasons, many industries are leaning on Machine Learning to cater to their profits. From automation of manufacturing tools to sentiment analysis of tweets; Machine Learning algorithms are everywhere.
Apart from this, Machine Learning also caters to the needs for online education. It has a great scope for usage in the coming times. This even leads the algorithms to reach other branches of society.
4. Data Handling and Pattern Identification
Machine Learning algorithms are quite reliable in handling huge numbers of datasets. These datasets can vary from simple rea- life consumer feedback to multidimensional data; Machine Learning algorithms can handle any kind of data-related queries efficiently. Not only does it handle these data-related queries, but it also processes them in a much more readable and convenient form.
These processed datasets are then used to train new datasets using Supervised Algorithms to identify a pattern in consumption of the product. It helps the developers to cater to the needs of the consumers in a much inexpensive and practical way. ML is capable of working on multidimensional data even in a dynamic environment.
Disadvantages of Machine Learning
Though Machine Learning has taken over a vast arena of the industry, we can not say for sure that the technology is perfect. There are many drawbacks that we still need to tackle. But before that, we need to learn about these drawbacks in brief.
1. Procurement of Data
One of the most pain-staking parts of deploying in any Machine Learning algorithm is the attainment of datasets. Collecting huge amounts of data can prove to be quite costly as well; which in turn leads to the overall increase in the cost of the production.
Apart from this, collecting data from survey forms can be quite tedious and may contain irregularities that may be misleading to the algorithm. This leads to a decline in the accuracy rate of the algorithm. This imbalance in data is quite difficult to tackle and may have adverse effects on the overall program. All these factors make the acquisition of data a major disadvantage in incorporating these algorithms in the real world.
2. Elevated Level of Error Susceptibility
As we have seen many times, Supervised Algorithms make use of labeled data to train new data to attain better results. However, this labeled data must be highly accurate to appropriately train the data. A minor error in this trained data may lead to diversion in the learning path of the new dataset collected. This inappropriate data may cause errors in the automation of the overall program.
As an example of these errors, we can consider a situation in which a bank dataset is inappropriate. In this situation, a customer may be labeled as a defaulter due to the miscalculation of the poorly trained data. In such cases, human intervention becomes inevitable.
3. Resource Exploitation
Till now we know that Machine Learning Algorithms are capable of handling huge amounts of data. However, processing such an extensive amount of data may require more time to be processed. Processing such a large amount of data may also require additional amenities. This leads to higher consumption of storage on the device.
Though we can integrate a system in which the CPU can operate alongside GPU resources for smooth running, it still becomes quite tiresome to operate the datasets. This may also lead to corruption of data and degrade the reliability of the algorithm drastically.
4. Choosing Appropriate Algorithms
The level of reliability and accuracy of the Machine Learning models is unmatched. However, selecting the precise algorithm from the plethora of various algorithms to train these models is quite tedious. Though these algorithms are capable of achieving accurate results, we have to manually choose these algorithms.
Machines are yet not trained to a level where they are capable of decision-making for algorithm selection. We have to physically feed in the algorithm to the model to obtain accurate results. This process can be quite tiresome and the human intervention may lead to inaccuracy in the results.
Conclusion
With this, we have reached the conclusion of this article on Advantages and Disadvantages of Machine Learning. Hope that PythonGeeks was able to give you a close insight into the nitty-gritty of learning these algorithms. You have come to understand the various benefits of using Machine Learning. We also came across several drawbacks of this technology.
However, judging this advancement based on these drawbacks won’t be justified. These drawbacks could also be treated as future scope of improvement for these algorithms. Consequently, we have learned about some of the advantages and disadvantages of Machine Learning.