Machine learning is an outcome of data science. ML is all about the machine’s ability to function as a human, which learns from its experiences. Machine learning basically does the job of data science. It collects data, draws conclusions from it and prescribes future actions without any human interaction or instruction. It is a self-evaluating and self-learning tool. In simple words, machine learning can be explained as an application of data science which allows the computer to learn on its own without any human assistance.
The modern definition of data has been improving and expanding each day, every day. Exceptional innovations have been a thing in the sector of data science. Back in the day, storage of data was a huge problem, whereas now, the focus has shifted to processing of gigabytes of data. Deducing the data for conclusions has been another major difficult thing to accomplish but data science has been proven as the solution for it from time to time.
Artificial intelligence (AI) is an area of computer science that lays prominence in the creation of intelligent machines that work and reacts like humans. Artificial intelligence has been considered the key to solving many problems. Artificial intelligence helps humans to break down complex data to deduce the data to find draw appropriate conclusions.
DATA SCIENCE VS MACHINE LEARNING VS ARTIFICIAL INTELLIGENCE
Artificial intelligence is a very broad term with applications ranging from robotics to text analysis. it is still a technology that is under evolution and there are arguments of whether we should be aiming for high-level AI or we should stop aiming further. Artificial Intelligence is a method of incorporating natural (human) intelligence in machines with the help of humongous data sets. In simple words, Data Science creates and modifies AI software in order to gain information from huge data clusters. This is where machine learning comes to aid. ML is a subset of artificial intelligence that focuses on a narrow range of activities. In fact, it is the only real artificial intelligence with some applications in real-world problems.
Machine learning is a crucial link, which connects data science and artificial intelligence. To explain it more clearly, machine learning is all about empowering machines with the ability to learn and decipher the data in order to delude conclusions. Don’t be confused and understand this, the main intention of machine learning is to ensure that machines are able to learn on their own with the help of data provided and make informed & accurate predictions.
Data science isn’t exactly a subset of ML but it uses machine learning to analyze data and make predictions about the future. It combines machine learning with other supreme disciplines like big data analytics and cloud computing. Data science is a practical application of machine learning with a complete focus on solving modern problems. Data Science is the collection and curating of mass data for analysis whereas Artificial Intelligence is implementing this data in Machine for understanding this data. Machine Learning is a discipline within artificial intelligence that is focused on using data to build intelligent systems.
Example of Data Science:
Let’s say you are crazy about Cricket, which I am sure you are, and there is an ongoing series between India and New Zealand. India loses the first two matches, and you are eager to know what will happen in the next game. You go online to check the results of past encounters between the two nations and notice a trend there. Every time India has lost two games in a row against New Zealand, they have come back strongly in the third match. You are convinced that India will win the next game, and predict the outcome. To everyone’s surprise, your prediction turns out right. Congratulations, you’re a data scientist! That’s how data science works. You drew conclusions from the past data of the matches between the two nations and predicted the result.
Example of Machine Learning:
You formulate a bunch of questions along with their answers and segregate them. Using these, you train your model to create a question-answer software. This software responds with an appropriate answer every time you ask it a question. Cortana, Alexa other digital assistants, which are essentially speech automated systems in smartphones or laptops, work this way. They train themselves to work with your inputs and then deliver amazing results according to their training.
Example of Artificial Intelligence:
Artificial intelligence is similar to machine learning but it’s not the same. Artificial intelligence is based on the theory that what if computers could work as the human brain did. Machine learning requires a set of answers for the questions asked. If the question asked is something that the machine was not trained to answer, it neglects it or doesn’t respond. It’s not the same case with artificial intelligence. The aim of artificial intelligence is to answer and work the same way human brain does. If our brain doesn’t know the answer for a said question, it searches for it in our subconscious hoping the answer might be buried there. Artificial intelligence works exactly like the human brain. It searches for answers to the questions that it was never trained for.
I hope you’re beginning to realize that artificial intelligence and machine learning are not quite the same. Artificial intelligence is the science that intents to help machines mimic human cognition and behavior, while machine learning refers to those algorithms that make machines think for themselves. In simple words, machine learning enables artificial intelligence. That is to say, a clever program that can replicate human behavior is AI. But if the machine does not automatically learn this behavior, it is not machine learning.
Artificial intelligence is a computer program that is capable of being smart. It can mimic human thinking or behavior. Machine learning falls under artificial intelligence. That being said, all machine learning is artificial intelligence, but not all artificial intelligence is machine learning. For example, a simple scheduling system may be artificial intelligence, but not necessarily a machine that can learn. Lastly, data science is a very general term for processes that extract knowledge or insights from data in its various forms. There are more similarities among the three than differences. Data science, machine learning, and artificial intelligence are branches of one single tree that have uncountable connections and are inter-related at various levels.