You hear about machine learning. But do you know what is true and what is not? People are fascinated about machine learning and artificial intelligence, yet they are confused.
Multinational companies like Facebook, Google, and Amazon employed machine learning first. Google utilized it for ad placement, while Facebook used it to show post feeds. However, there are some misunderstandings about machine learning. Let’s start with a few.
1. Anyone Can Build A Machine Learning Platform That Can Be Used Anywhere
Many believe you can just Google machine learning and develop any platform. However, machine learning is a specialized skill set. While learning machine learning, it is critical to comprehend the productive system. Hands-on experience with machine learning patterns and algorithms is required to master machine learning.
This is a widespread misconception about machine learning. Nobody will spend Rs. 1,000 on a Rs. 200 job. Machine learning is only used with large amounts of data. Machine learning is useless for tiny data solutions that a person can achieve easily.
2. There is No Difference Between AI, Machine Learning, and Deep Learning
Most of the time, the phrases machine learning and artificial intelligence are used in the same sentence. Both, on the other hand, are not the same and are not synonymous with one another. Artificial intelligence encompasses a wide range of fields, including robotics, computer vision, and natural language processing. Machine learning is the process of discovering patterns in data via the use of statistics and data predictions.
Deep learning is now a frequently used phrase in the industry. People believe it to be the last solution to the data science and machine learning problems. Deep learning is one of the most difficult topics in machine learning to grasp. Deep learning is a branch of machine learning that uses multi-layer neural networks to compute.
In simple words, Deep learning is a part of Machine learning and that is in turn a part of Artificial intelligence.
3. Machine Learning Will Take Over Human Work & Can Work Independently Without Human Intervention
One of the primary concerns is that AI will replace humans. While machine learning will automate the system and undertake certain social functions, it will also generate new employment positions or skill sets. Machine learning will allow for the development of new skill sets and creative thinking.
People believe that a machine can learn a system without programming. Humans provide the algorithms for machine learning solutions. So human involvement in machine learning is unavoidable.
4. Machine Learning Is The Future
Machine learning will undoubtedly be heavily employed in the future, but it will not be the only future. There are more advanced technologies on the market that can take machine learning to the next level. A few years ago, self-driving cars and robots were simply a pipe dream. However, it is already a reality.
5. Machine learning can solve any problem
Machine learning models are now incapable of tackling all of the world’s problems. Until recently, all algorithms have been built to tackle a certain form of problem statement. Companies such as Facebook and Google have begun to visualize machine learning from a “Non supervised learning” perspective, in which data is not labelled and machines learn for themselves. Because of the reliance on annotation for labelling a large quantity of data set, they have given solid evidence that supervised learning will never be the future.
6. One machine learning algorithm will be sufficient
This is also one of the most often asked questions in the field of deep learning and machine learning. People believe that a single machine learning method will address all of the challenges in this sector. There is a misperception that algorithms have progressed and that their engagement in algorithms would provide them enough strength to address issues that were solved by prior algorithms. This, however, does not appear to be the case. The logistic regression approach is incapable of resolving regression issues.
Although algorithms have progressed, this advancement has been directed at tackling whole new or more complex problems. As a result, the method we choose is entirely reliant on the problem statement we’re trying to solve.
7. The machine learning model will perform better if there are more features in the data.
In deep learning, we may state that the accuracy of the model is determined by the quantity of data we give it. The capabilities of our machines, hardware, and compute power, on the other hand, will be the most significant bottleneck. We won’t be able to generate a machine learning model for all of the data at the same time.
It’s not always possible to extract useful information from the same data collection. Because the new extracted feature is closely connected to the previous features, full stop models can be overfitted or biassed towards one or more features.
8. The purpose of machine learning models is to deliver high accuracy
This is one of the most common misunderstandings even among Advanced Learners. Machine learning methods give more modularity than accuracy. In some cases, no ml method will outperform current conventional algorithms. Still, it can beat the traditional algorithm if it performs better in unforeseen situations.
Assume that there is one traditional technique to compute our goal variable for a supervised learning model. Our machine learning model will strive to obtain 100% accuracy with regard to that conventional algorithm. So why ML? A machine learning model for stock may demand more complexity and competition power than a traditional algorithm, in which case replacing traditional algorithms with machine learning models would be extremely helpful.
