Machine learning is basically an application of artificial intelligence that has computer algorithms. These machine learning algorithms are explicitly programmed so that the system improves on its own by learning with experience.
Machine learning is used for predictive analysis, voice/image/speech recognition, automation of vehicles, data science, etc. It is widely used in healthcare, education, automotive, banking & insurance, IT (information technology) industries.
This wide range of applications in the real world has given a boost to the development and research of better machine learning algorithms and eventually better machine learning models. Despite the availability of many tools and expert teams of data scientists, there are some common mistakes that most people make, and almost any machine learning company can help you fix them as they are trivial yet have a huge impact.
Common Machine Learning Mistakes
Mistake #1: Not emphasizing the Quality of Data
One of the most common machine learning mistakes is that enough attention is not paid to the data set used for feeding the Machine Learning algorithms. You must accept that data preparation is the prominent and time-consuming phase of implementing a machine learning system.
Using good data is very important for yielding the right results consistently. Here is the categorization of data that will definitely pose a problem. The following table will help you identify what not to feed for training and testing the complex modeling of machine learning algorithms.
| Data Type | Identifiers for the Data |
|---|---|
| Dirty Data | Contains Inconsistent/Erroneous/Missing values, undefined data. |
| Noisy Data | Data that has wrongful or misleading information |
| Inadequate data | Insufficient/incomplete which does not convey the whale information and hinders results |
| Sparse Data | Is large in size but holds little information that can be used and excess of 0s and missing values |
Fix:
The best way to overcome this problem from occurring is by carrying out data preparation through data cleansing. Use the mean/median/mode method for missing and undefined categories of information. Use methods of data segregation, feature engineering, etc. Choose an appropriate machine learning model that fits the use and is not unnecessarily complex. Apply the candidate model evaluation technique for the same.
By ensuring these fixes, no matter what amount of data is used for the machine learning algorithms, you will always fetch the correct results.
Mistake #2: Not employing the right expertise for the job.
It is undeniable that you must need and use knowledgeable and expert data scientists for avoiding common machine learning mistakes. They work day and night in the field and understand the most optimum way of implementing ML for you.
It is necessary to understand that deep knowledge of data analytics in combination with mathematics and computer science is necessary for machine learning. It is not everyone’s cup of tea. When you hire a machine learning professional, half of your job is done.
Fix:
Hire at least 1-2 experts in the field i.e. data scientists depending on the size of your project in hand. Hire, interns, or freshers who qualify for the basic knowledge and can be trained for the same. If your project is large enough and also has a long-term application, it’s wise to build an in-house team for machine learning with employees who have different areas of expertise. Another option is to hire a machine learning development company and outsource all the headaches by just providing your requirements.
Mistake #3: Lacking Hardware and Software infrastructure
With the right team, it becomes the organization’s responsibility to equip them with all the required resources for efficient functioning. Handling Big data requires massive storage and robust computational devices. Also, a few analytical tools need to be purchased.
Fix:
Primarily, you need a reliable DBMS database management system that does not crash and works spectacularly with a large amount of data. With a proper strategy in mind of the team, you can estimate the resources you require. For eg., Your system may need GPUs (Graphical Processing Units), SSDs (solid-state drives), analytical software, equipment to record speech, scanners, etc. The infrastructure must be scalable and flexible. As you progress in the organization, you will need to expand. With a constricted infrastructure, your growth will be hindered and your organization may suffer losses.
In Conclusion
While the enlisted are the most common mistakes in machine learning, others include an improper decision about strategy implemented, wrong techniques followed, hasty work accomplished without measuring the impact it would have, etc.
So, the golden rule to avoid common machine learning mistakes is to learn from the mistakes of others and calculate your success ratio with an adept team. You would certainly reap the benefits of this wonderful machine learning technology.