What is Machine Learning?
Machine learning is a branch of computer science that is distinct from standard computational methods. Algorithms are sets of clearly designed instructions used by computers to calculate or solve problems in conventional computing.
Machine learning techniques, on the other hand, allow computers to train on data inputs and then utilize statistical analysis to produce results that are within a certain range.
As a result, machine learning makes it easier for computers to create models from sample data and automate decision-making processes based on data inputs.
The formal definition of Machine Learning, given by Tom M. Mitchell is “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E’’.
Let’s break this down to understand it thoroughly. Whenever a computer is given a task T let’s say the task here is playing a checkers game, the computer improves its performance as measured by its ability to win or lose the game and uses this(be it a win or lose) experience to improve by playing games against itself.
Successful Applications of Machine Learning
- Recognition of Spoken Words: Speech Recognition has enabled us to use speech as a primary way of interacting with the computer. Speech recognition is invading our daily lives. It’s embedded in our phones, gaming consoles, and smartwatches. It’s even taking over our houses with devices like Amazon Echo, Alexa, etc.
- Product Recommendations: It has not just helped the companies advertise their products to the target audience, but has made it easy for us to find what we might like and saved the time of users doing the filtering.
- Self Driving Cars: One of the most fascinating applications of Machine Learning is self-driving cars. With a combination of various supervised, unsupervised and
Types of Machine Learning
- Supervised Learning: One of the most fundamental forms of machine learning is supervised learning. The machine learning algorithm is trained on labeled data. Although, when compared to the final data set, the training data set must have similar properties for the algorithm to work accurately.
The Machine Learning algorithm is given a short training dataset to work within supervised learning. This training dataset is a subset of the larger dataset, and it helps to provide the algorithm with a basic understanding of the issue, solution, and data points to be handled.
In terms of features, the training dataset is quite similar to the final dataset, and it gives the algorithm with the labeled parameters it needs to solve the issue.
For example, an algorithm may be fed data with photos of goldfish categorized as fish and images of aquariums labeled as water using supervised learning. The supervised learning system should be able to recognize unlabeled goldfish photos as fish and unlabeled aquarium images as water after being trained on this data.
- Unsupervised Learning: By its literal definition Unsupervised Learning does not need any sort of human supervision to make the dataset machine-readable.
Unsupervised learning might have a simple objective of detecting hidden patterns in a dataset, or it can have a goal of feature learning, which allows the computational machine to autonomously discover the representations required to categorize raw data.
Unsupervised learning algorithms are advantageous due to the formation of these hidden structures. Unsupervised learning algorithms can adapt to the data by constantly modifying hidden structures instead of a predetermined and fixed problem statement. When compared to supervised learning techniques, this allows for more post-deployment development.
- Reinforcement Learning: The basic concept of Reinforcement Learning is to determine what to do based on rewards and punishment.
In Reinforcement learning the algorithm is placed in a work environment with an interpreter and a reward system, based on the psychology idea of conditioning.
The output result of each iteration of the algorithm is delivered to the interpreter, who assesses whether the outcome is beneficial or not.
Another type of Machine Learning is
- Semi-Supervised Learning: This type of learning counters the disadvantages of supervised and unsupervised learning to its advantage. The training dataset contains a combination of labeled and unlabeled data.
The amount of unlabeled data is considerably large when compared to labeled data. This theory has a specific situation known as Transduction, in which the whole collection of problem cases is known at learning time except for a portion of the targets.
Some of the prerequisites you might want to revisit are Calculus, Linear Algebra, Statistics, and Probability.
Reference