Artificial Intelligence – Everything You Should Know
Artificial intelligence is a very broad term. AI tackles complex problems in a human-like manner unlike conventional coding. In a traditional algorithm, a developer will set a specific chain of rules that define an output for each type of input that the software receives. In contrast, AI algorithms are designed to build out their own system of rules, rather than have those rules defined for them by a developer.
Machine Learning is a subset of Artificial Intelligence. Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned. A subset of artificial intelligence involved with the creation of algorithms which can modify itself without human intervention to produce desired output- by feeding itself through structured data.
Deep Learning is a subset of Machine Learning. Deep learning structures algorithms in layers to create an “artificial neural network” that can learn and make intelligent decisions on its own. A subset of machine learning where algorithms are created and function like those in machine learning, but there are numerous layers of these algorithms- each providing a different interpretation to the data it feeds on. Such a network of algorithms is called artificial neural networks, being named so as their functioning is an inspiration, or you may say; an attempt at imitating the function of the human neural networks present in the brain.
How is machine learning different from traditional programming?
Traditional programming involves feeding input data into a machine and then writing and testing the program to generate output. Machine learning works by feeding input and output data into the machine during the learning phase, and it develops a program for itself. Here is an illustration to help you understand:
Why is Machine Learning important today?
Today, machine learning has all the attention it needs. It can automate many tasks, especially those that humans are uniquely able to do because of their innate intelligence. Machine learning is the only way to replicate this intelligence in machines.
Seven Steps of Machine Learning
How does Machine Learning work?
There are three main components of a system: the model, the parameters, and the learner.
- A model is a system that make predictions
- In order to make predictions, the model considers the parameters
- The learner adjusts the parameters and the model to match the actual results with the predictions
Different types of Machine Learning
Algorithms for Machine Learning are implemented in a variety of programming languages and techniques. These algorithms are trained using various methods, out of which the following three are the most common types of Machine Learning:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Supervised learning is the most basic type of machine learning, which uses labeled data to train algorithms. Datasets are provided to the ML model for understanding and solving problems. A smaller dataset conveys the basic idea of the problem to the machine learning algorithm through this dataset.
In unsupervised learning, the data is not made machine-readable through human intervention, and the algorithm is trained without human input. Additionally, unsupervised learning uses unlabeled data in contrast to supervised learning.
As the algorithm does not involve any human intervention and uses unlabeled data, it is capable of handling larger data sets. As opposed to supervised learning, unsupervised learning does not require labels to establish relationships between data points.
Reinforcement Learning is a type of Machine Learning where an algorithm learns from new situations by trial-and-error. Each iteration is based on the output result that has already been fed into the system.
Which Language is best for Machine Learning?
Python is known for its readability and relative ease of use when compared to other programming languages. It takes a lot of effort and time to implement ML applications as they involve complex concepts like calculus and linear algebra. Python helps to reduce this burden with quick implementation for the ML engineer to validate an idea. Python also offers pre-built libraries as an advantage.
Difference between Machine Learning and Deep Learning
Excellent performances on a small/medium dataset
Excellent performance on a big dataset
Work on a low-end machine.
Requires powerful machine, preferably with GPU: DL performs a significant amount of matrix multiplication
Need to understand the features that represent the data
No need to understand the best feature that represents the data
From few minutes to hours
Up to weeks. Neural Network needs to compute a significant number of weights
Some algorithms are easy to interpret (logistic, decision tree), some are almost impossible (SVM, XG Boost)
Difficult to impossible
Future of Machine Learning
It will continue to evolve in the future; become increasingly sophisticated. In addition to healthcare, finance, and manufacturing, it will also be used in other aspects of life and business. In the near future, machine learning will become more accessible and more affordable to everyone.