Artificial Intelligence vs Machine Learning vs Data Science

          Modern technologies such as artificial intelligence, machine learning, data science, deep learning and big data have become buzzwords that everyone is talking about, but nobody fully understands them. some even say they are all the same, like matches. they are wrong.

So we thought, ''Why not explain these concepts as simply as possible?'' In this blog, I am going to explain these technologies in simple terms so that you can easily understand the difference between them and their uses in business.

We'll also give you a ton of examples too, so get ready to check out some cool technology. The terms data science, artificial intelligence (AI) and machine learning practically fall into the same domain and are related to them. Miscellaneous. There may be overlap in these areas, but essentially each of these three terms has a unique usage.

For example shades of pink, There are fuchsia pink, candy pink, rouge pink, hot pink and 28 other shades, all of which fall under the colour pink. Networks, natural language processing, and many other similar terms fall under the term artificial intelligence.

          Artificial intelligence is a very ... very ... broad term with applications from robotics to text analysis. In the simplest and simplest sense, AI is the ability to give machines, or especially computers, the ability to replicate human intelligence.

Artificial Intelligence Systems Have Three Fundamental Qualities

1. Intentionality

2. Intelligence

3. Adaptability  

These fundamental properties give these systems the ability to make decisions that traditionally require human experience and expertise, such as autonomous vehicles and facial recognition systems.

In today's world, like Tesla, cars know another car and another truck. Similarly, iPhones can recognize your face with a meter-long beard too.

          On the other hand, machine learning is a subset of artificial intelligence that focuses on a small or narrow range of activities; In fact, it's the only real artificial intelligence with real-world applications like face recognition and self-driving cars.

The main focus of machine learning is the development of software and programs that can learn to make predictions and decisions without being directly intervened or programmed to do so. This technology can be used for all types of purposes, such as energy search engines diagnose diseases.

Our favourite example of this incredible technology is Google photos. Fabric automatically stack photos and incredible blogs. Further examples are recipients of emails, classification contributions in social networks, suggestions about Netflix and Amazon, suggestions on Instagram, LinkedIn, Facebook and the list goes on and in ...

          On the other hand, machine learning is a subset of artificial intelligence that focuses on a small or narrow range of activities; In fact, it's the only real artificial intelligence with real-world applications like face recognition and self-driving cars.

The main focus of machine learning is the development of software and programs that can learn to make predictions and decisions without being directly intervened or programmed to do so. This technology can be used for all types of purposes, such as energy search engines diagnose diseases.

Our favourite example of this incredible technology is Google photos. Fabric automatically stack photos and incredible blogs. Further examples are recipients of emails, classification contributions in social networks, suggestions about Netflix and Amazon, suggestions on Instagram, LinkedIn, Facebook and the list goes on and in ...

          Data science is not exactly a subset of machine learning but uses learning the machine to analyze the data and predictions about the future. It is not the future where the world will end. A smaller level, like a weather forecast.

Data science together with other disciplines such as large data analysis and cloud computing learning machines. Some common examples of deep learning are speech recognition and sentiment analysis.

          Now that you've read all this jargon, are you thinking about how people do it? Here is the answer: you need to learn computer languages ​​and coding. Python is one of the most widely used programming languages ​​in AI.

So if you know Python, learning AI, ML & DS would be easy, peezy, lemon squeezy. 

We hope you learned a lot from this blog. If you have any questions, leave them in the comment section below, and we will make sure to reply ASAP. 

Keep Learning. Keep Growing.