6 Advantages and Disadvantages of Machine Learning | Limitations & Benefits of Machine Learning

Artificial Intelligence and Machines have revolutionized the world of technology. Machine learning is an
advanced technology which uses algorithms and datasets to understand and learn human behavior. 

The main purpose of machine learning is to predict outcomes without human involvement. Under normal circumstances, the outcomes predicted by machine learning is highly accurate. Moreover, machine learning can also be used to effectively organize large sets of data. 

Applications of machine learning can be seen in fields like Google translate, Social media and Self driving cars. Obviously, machine learning comes with vast number of advantages. But machine learning also faces certain limitations. We must know the pros and cons of machine learning so as to understand to which extent can we depend on this technology. 

In this article, I will be concentrating on 6 Advantages and Disadvantages of Machine Learning | Limitations & Benefits of Machine Learning. From this post, you will know the pros and cons of machine learning. 

Let's get started,

Advantages of Machine Learning

1. Efficiency

Machine learning completely lacks human intervention. The entire prediction of machine learning is carried out by a computer. Machine learning studies the algorithm and analyzes the pattern so that it can deliver best possible result. This is what makes machine learning highly reliable and efficient.

2. Flexibility

Machine learning is a component of Artificial Intelligence. Therefore, it is not just restricted to one field. Currently, ML usage can be seen in major fields like engineering, business, banking, medical and most importantly tech. Whatever the field it is used in, the outcome is highly accurate without any flaws. 

3. Data Analysis

Identifying patterns and trends of data using machine learning is a hassle free process. Machine learning can seamlessly analyze data even if the volume is enormous. Typically, this data analysis is not understandable to humans. Due to its greater accuracy levels, machine learning has been utilized in e-commerce firms to deliver best known products to their customers.

4. Continuous Advancements

Machine learning concepts are on continuous evolvement ; there is no dead end. Continuous advancements are necessary to make it more accurate and efficient. Based on the historical data and action, the system keeps on learning. Eventually, this aids in the process of decision making. 

5. Data Handling

Extent of data handling is another important functionality of machine learning. Conventional systems struggle at handling multidimensional data. Fortunately, with its advanced algorithms, it has the capability to handle any kind of data including multidimensional. 

6. Quality Education

In recent years, machine learning has stabilized education in many ways. Considering the dynamic nature of education, lots of smart learning techniques are implemented through the use of machine learning. E-learning is one such popular example.

Disadvantages of Machine Learning

1. Expensive

Machine learning is not without funding. The major part of expense comes with the software. The software used in machine learning is highly expensive ; not everybody can afford them. Currently, only giant tech firms and government agencies own it. 

Apart from that, the developers who are experts in this field charge a lot for their service. Now this is not one time investment, the remuneration is of monthly basis. 

2. Accuracy

The results interpreted through machine learning is not always accurate. The already existing algorithm set has the tendency to misinterpret results. The only way to overcome this is by developing an algorithm which at all times generate perfect results. 

3. Data Requirement

Data requirement of machine learning is massive. For achieving potential results, machine learning needs to be provided with tremendous amounts of data. Now the collection of data is not free it comes with a price tag. Apart from that, the collection data also must be of good quality. Even a minor error can lead to the malfunctioning of the entire system. 

4. Time Consumption

Not only does machine learning requires huge quantities of data, the learning process also needs a lot of time. If an algorithm is updated, machine learning, machine learning needs to be provided with enough time so as to adopt to the new environment. 

In terms of resources too the consumption rate is higher. Higher the data consumption, more the CPU power. At the end for compensating the excess CPU power, you may need to setup additional computers. 

5. Privacy Concerns

Data is the primary factor for machine learning. Whether machine learning always safeguards data remains a question. Especially, if the dataset contains sensitive information. For this reason, many people are hesitant to trust machine learning algorithms. 

6. Error Prone

Since the amount of data required by machine learning is massive, it is highly vulnerable to errors. If the source of data consists of errors, it will be continuing to the upcoming predictions as well. In this type of instance, the errors need to be identified at the initial stages. Otherwise, the rectification process can get more difficult.