What’s the difference between Artificial Intelligence (AI), Machine Learning, Deep Learning, and Data Science?
What’s the difference between Deep Learning, Machine Learning, Artificial intelligence, and Data Science? All these terms sound technical, and you might be wondering if they are related to each other. You might wonder if for all of them you need to learn coding or not. There’s some ambiguity related to all these terms, and your first impression would be to get afraid of this jargon.
However, you need not worry about it. This blog aims to break the definition of Artificial Intelligence, Machine Learning, Deep Learning, and Data Science into the simplest terms. As technological advancements increase, staying updated with the trend is the need of the hour.
However, the question is should you be worried about learning what these terms mean?
This blog will help you understand these concepts and their importance easily and intuitively. Let’s dive into their meanings and see the real-world applications of these concepts.
Deep learning is associated with neural networks. In deep learning, we train machines to do smart tasks. It is valid to say that deep learning models are trained to learn concepts similar to humans gaining knowledge about a concept.
In deep learning, there are two popular frameworks used globally by engineers: Pytorch and TensorFlow. If you have been using any of these frameworks for your program, you have been on the deep-learning route.
Deep learning can be considered an advanced version of machine learning. Deep learning will be helpful if you need to work with large datasets to train your model that includes complicated features. However, the hardware requirements for deep learning are more compared to ML. Also, training your deep learning model can be time-consuming.
Machine learning is deep learning along with statistical models such as Decision Trees, K-Means Clustering, Linear Regression Single Variable, and Support Vector Machines. Therefore, it’s not necessary to use neural networks for machine learning. You can use the above-mentioned statistical algorithms also.
Machine learning can further be divided into three categories such as:
- Supervised learning
- Unsupervised learning
Machine learning is an inseparable component of data science. Machine learning models are useful in data analysis to predict and generate actionable insights which help businesses strive. The different algorithms in ML are useful for classification and regression problems.
Next, we have artificial intelligence.
Artificial Intelligence (AI) is the broader term under which Machine Learning falls. An example will help us understand the concept better.
Let’s suppose you are designing a robot that does smart work. But for the robot, you will not use machine learning. You will use motion and light sensors, for improving the accuracy of navigation for the robot. Designing such smart robots can be called Artificial Intelligence. You are trying to make a machine as smart as humans.
AI deals with imparting knowledge to a machine, to making it smart enough to make decisions humans would fail to. Machines are fed data so that they can understand every bit of it and gain insights capable of smoothening the decision-making process. Plenty of mathematical algorithms are used for understanding data and conclusions.
Computers are smart and capable of performing technical tasks as well. However, when it comes to computer vision, image recognition, or audio, humans are way superior to computers. Artificial Intelligence steps in when we try to make those computers as smart as humans.
Data Science is a broad field. You can use artificial intelligence for data science. However, data science can also be done in an Excel sheet. Data science is the process of evaluating data and drawing actionable business insights from it. For that, you can use tools like Power BI, or even Tableau which are great visualization tools. Also, learning Python programming can be beneficial for data scientists.
But is data science only limited to using complex machine learning algorithms and visualization tools? Not.
Using pie charts or even bar charts to draw insights is considered data science. However, for advanced data analysis, you can use neural networks and even machine learning algorithms.
Nowadays, almost all companies generate huge amounts of data. But did you ever wonder how useful this data is to generate business insights? The more data, the better your scope of getting insights. This data can be useful for predicting customer behavior, sales patterns, and much more. As long as you have data with yourself, you can apply different algorithms to it and use Python coding for building models and performing analyses.
The biggest advantage of learning Python programming for data science is that Python has a relatively lower learning curve. For budding data scientists looking forward to building a career in data science, Python is the best language to learn. Not only does it have a simple syntax, but it also has different libraries like NumPy, Pandas, and Sklearn which aid data scientists and analysts churn data effectively.
Sklearn is a popular Python library consisting of different algorithms and functions helpful in machine learning problems. Moreover, it also provides other useful tools for data mining and analysis. It doesn’t make sense to do something manually if you have a library for it that saves you time. Just like Sklearn, Scipy is another Python library that helps data scientists with complex programming.
There are plenty of libraries like the ones mentioned above, which assist data scientists with enhancing the performance of the program and executing things faster. Therefore, learning Python programming can be a good decision.
These are all the details related to Artificial Intelligence, Machine Learning, Deep Learning, and Data Science. Hopefully, this blog helped you understand all the terms and their real-life importance. The tech industry is booming with ideas, and if you are planning to enter any of these industries, you can gain in-depth knowledge of these concepts through online resources and even courses.
Today, you will find plenty of online websites that focus on teaching technology intuitively. Moreover, in all these fields you will find that learning Python programming can be beneficial! You can find all the resources which follow a structured and organized approach to these fields. Enrolling in such courses can help you learn all the concepts properly and practice them with exercises. Moreover, online courses are cheaper than offline education programs and certificate courses which offer the same value at a much higher price. Online courses not only make valuable information accessible but also help learners synthesize that information. Remember, along with learning, synthesizing information and its application is equally essential. Therefore, look for premium quality online courses that make learning more than a process! For Readers!