The field of study that deals with the capability of computers to learn and read without explicit programming is called machine learning. This is one of the most upcoming technologies on which a lot of other fields are dependent too. This study of computers and their programming is a lot similar to a person’s ability to learn. This field is one of the most active fields which are used by programmers and data scientists these days and is being used in a number of places too. In this article, we will talk about machine learning, the need to learn machine learning, its history, how machine learning works, its advantages, and its needs.
The term machine learning was actually coined by a famous computer pioneer, Arthur Samuel. He initiated the idea that computers can also work and learn just like humans. The term machine learning can also be explained as improving the system of learning by computers that could depend on their older experiences without the actual programs or human guidance. This process initiates with training some good quality data with the help of computers and then building models on them that work on the basis of machine learning algorithms. The algorithm can be chosen depending on the data type or the type of task an individual wishes to perform using the specific model.
Machine learning is a very important skill that could be possessed by a data scientist or an analyst. It could also be developed by an individual who wishes to transform a huge amount of raw data into valuable predictions, graphs, or trends.
Let us understand this more with the help of an example below:
Let us assume that there are a few students in a class who actually want to understand a concept thoroughly while preparing for the exam rather than cramming the subject. For this to happen, the students feed their machines (aka brains) with all the data related to the subject. This can be called the process of training the machine where the students are training their brains with all the approaches and logic that could help them during the examination. Every time they take up a quiz or an assignment, they get a score or an accuracy measure that determines their performance depending on the score. With immense practice, the performance keeps on improving and the score gets better.
The same way our computers work when the data is fed to them, they perform according to it. With continuous practice and improvements made to the model, the accuracy ratio increases every time. This is the main task to perform with every model where the data scientists work on the betterment of the model using various algorithms.
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The term machine learning was actually coined by a famous computer pioneer, Arthur Samuel. He initiated the idea that computers can also work and learn just like humans. The term machine learning can also be explained as improving the system of learning by computers that could be depending on its older experiences without the actual programs or we can say human guidance. This process initiates with training some good quality data with the help of computers and then building models on them that work on the basis of machine learning algorithms.
Then in the year 1949, a book was published named ‘The Organization of Behavior’ by the author Donald Hebb. This book contains theories that are based on behaviour related to the activity of the brain and neural networks. Thi became one of the most important pillars for strengthening this topic of machine learning. would go on to become one of the monumental pillars of machine learning development.
Later, in the year 1950, researcher Alan Turing designed a Turing Test for determining if a computer possesses real intelligence or not. A computer was supposed to fool a human in order to pass the test. The idea to create this whole design was to get an answer to the question - can machines actually think?
Below are the steps for understanding the working of machine learning:
Below are the features of machine learning:
Supervised learning: These algorithms are defined as that class of machine learning methodologies where the user can train with the help of continuous and well-labelled data. For instance, the data can be historical data where the user wishes to predict whether a customer will take a loan or not. Supervised algorithms tend to train over the well-structured data after the preprocessing and feature characterization of this labelled data. It is further tested on a completely new data point for the prediction of a loan defaulter. The most popular supervised learning algorithms are the k-nearest neighbour algorithm, linear regression algorithm, logistic regression, decision tree, etc.
This is further divided into 2 categories:
Unsupervised learning: These algorithms are defined as that class of machine learning methodologies where the tasks are performed using the unlabelled data. Clustering is the most popular use case for unsupervised algorithms. It is defined as the process of grouping similar data points together without manual intervention. The most popular unsupervised learning algorithms are k-means, k-medoids, etc.
This is further divided into 2 categories:
Reinforcement learning: This type of learning is based on feedback-type machine learning methods and this type does not require any kind of labelled data. An agent can perform in a certain way and then watches the reactions to his actions in this type of learning to judge the outcomes. Positive feedback is given for all the good actions however for all the bad actions, negative feedback is given. The agents are made to learn and understand using the experiences based on actions only, as there is no involvement of training data.
Semi-Supervised learning: This type of learning is a combination of both unsupervised learning and supervised learning. This type aims at overcoming the cons or drawbacks of both types. This is done by training the machine with both unlabelled as well as labelled data along with the examples.
frequently asked Machine Learning Interview questions and Answers !!
As we have discussed in the section above, machine learning is divided into 4 main categories which are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. These categories have a lot of algorithms under them which help in structuring the data in order to get better accuracy.
A few important algorithms in machine learning are linear regression, decision trees, k-means algorithm, logistic regression, SVM algorithm, KNN algorithm, random forest algorithm, Naive Bayes algorithm, etc. The data can be collected from different sources which can be further used for analysis and are presented in a number of formats. The backbone of data science is machine learning. Data Scientists who want to work on these algorithms in data science need to have a good knowledge of machine learning along with a basic knowledge of statistics.
In this article, we have talked about the basic introduction of machine learning concepts and the algorithms affiliated with it. The field of study that deals with the capability of computers to learn and read without explicit programming is called machine learning. We have also discussed the types of machine learning, the benefits of using machine learning, its applications, architecture, and its features. Machine learning is making our lives easy and efficient in an unknowing manner.
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