Hey! Interested too much in reading tech articles? Here is a new one only for you about Machine learning since we are living the world of technology that has moved from the slow processing vacuum tubes to the highly advanced generations of computer, Artificial intelligence including self-driving cars and humanoid robots used by large companies and even simple users for data analysis to perform the task in a fraction of seconds. But what made the advancement of processing so fast and accurate? What is the reason behind the computers making our works 1000 times easier? All thanks to machine learning and its algorithms which are discussed briefly:
What is Machine Learning?
- Machine learning (ML), which is also termed as deep learning or data mining, is a new concept and an application of Artificial intelligence that consists of algorithms that get and improve automatically through experience and finds solutions to the problems by understanding the patterns of the database. It is the subset of AI and focuses on the evolution of computer programs that can penetrate data and operate for themselves. The main focus of Machine learning is to make the computer able enough to learn automatically without explicitly writing programs or giving the same instructions repeatedly i.e. human interference. ML is also related to computational statistics where it is used to predict the statistical data, agriculture, human anatomy, adaptive websites, affective computing, banking, computer networks, data science, DNA sequence classification, and many more almost every field of technology.
Yes, machine learning makes computers learn automatically similar to humans. But how is that possible?
- Well, it is possible as Machine learning algorithms build the mathematical structure based on a dummy data given. The dummy data given is often referred to as Training data which enables a computer to predict and take decisions by itself.
But what are machine learning algorithms?
- As per your interest, machine learning algorithms are the set of step-wise processed procedures that guide a model to adapt to the training data for a given task. In simple words, the ML algorithm is the direction that directs the way of data transformation from input to output and how the model learns the appropriate mapping from input to output. A lot of ML algorithm is used for perfect execution however the 5 most popular ML algorithms are Linear Regression, Logistic Regression, Decision tree, kNN, and Naïve Bayes.
Are ML algorithms limited to them?
- No, machine learning algorithms have many categories and a lot of them are already developed and many of them are in the phase of development. However, ML algorithms type are supervised ML learning algorithm which analyses the training data and produces an inferred function to make a prediction about the output value. This algorithm is able to provide targets for any new input and compare its output with the correct one, intended output find errors, and modify the program itself according to its analyzation. Unsupervised ML learning algorithms are the ones that are used for the non-classified and non-labeled structure to explore hidden structure from that data. Semi-supervised machine learning algorithms is the middle of supervised and unsupervised ML algorithms as they use both labeled(in a small amount) and non-labeled(in large amount). This method is more considerable for improving learning accuracy. Similarly, Reinforcement machine learning algorithms are the method that interacts with its environment by producing actions and finds errors.
Advantages of ML
- It makes continuous improvement within itself without explicit programming
- The machine easily identifies trends and patterns
- It handles multi-dimensional data easily
- It makes machines automated and doesn’t need human intervention
- It is used in almost all the fields from health to education, finance, social aspects and so on