Starting off, you'll learn about Artificial Intelligence and then move to machine learning and deep learning. You will further learn how machine learning is different from deep learning, the various kinds of algorithms that fall under these two domains of learning. Finally, you will be introduced to some real-life applications where machine learning and deep learning is being applied. Well as the name suggests, artificial intelligence commonly known as AI is a way of artificially making a computer intelligent. Can you think of a computer or maybe a robot which can do various tasks similar to humans?
Eg; For House price prediction, we first need data about houses such as; square foot, no. of rooms, the house has a garden or not, and so on features. We then need to know the prices of these houses ie; class labels. Now data coming from thousands of houses, their features, and prices, we can now train a supervised machine learning model to predict a new house's price based on past experiences of the model. This algorithm is used to predict the discrete values such as male female, true false, spam not spam, etc. Regression algorithms are used to predict continuous values such as price, salary, age, marks, etc. Another example is Opening emergency hospitals to the maximum prone to accident areas.
Before choosing a machine learning algorithm, it's important to know their characteristics to generate desired outputs and build smart systems. Data science is growing super fast. As the demand for AI-enabled solutions is increasing, delivering smarter systems for industries has become essential. And the correctness and efficiency through machine learning operations must be fulfilled to ensure the developed solutions complete all demands. Hence, applying machine learning algorithms on the given dataset to produce righteous results and train the intelligent system is one of the most essential steps from the entire process.
Machine learning is a field of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic the way people learn, with the goal of steadily improving accuracy. In order to anticipate new output values, machine learning algorithms use past data as input. In order to make predictions or decisions without being explicitly taught, machine learning algorithms construct a model based on sample data, referred to as "training data." Machine learning algorithms are utilized in a broad range of applications, including medicine, email filtering, speech recognition, and computer vision, where developing traditional algorithms to do the required tasks is difficult or impossible. Machine learning is a crucial part of the rapidly expanding area of data science.
To many of us, the term machine learning is a fancy one. Yeah, we have seen the application of it in several fields and be amazed by it. I'm writing this article to give you an introduction to the machine learning to start your journey from zero to hero. It would be like a kick-starter for your journey on machine learning. In addition to machine learning, I want to introduce the other two terms which would be very important in this journey.