This blog is about Machine Learning and its overview.
What is Machine?
In a simple sentence, we can say that it is the functional system made by humans which follows some steps defined by the person who made it. The system consists of functional properties and this model is called a system architecture model. This is used to perform a particular task that reduces human efforts.
We already know that every machine has to be followed different steps of instructions to work on a task that comes under the functionality of the machine.
What is Learning?
In basics, learning is a process of gaining knowledge, skills, behavior, understanding about some task.
Getting an understanding of the concepts.
Therefore, in most of the work or tasks, every action we take is the result of past learning. For some people, learning remains an activity undertaken in or associated with, an educational context.
Example: Human body grows and develops it becomes more functional, we learn an inordinate range of skills.
What is Machine Learning?
Till now we have read about the machine and learning. So now let’s discuss machine learning.
Machine learning is a branch of artificial intelligence (AI) in computer science that empowers the use of data and algorithms to imitate the machine as humans.
The basic of machine learning and its overview is very crucial for approaching the deep implementation of Machine Learning and its feature.
From all these, we get that the instructions, previous results, and experience are very important to find the patterns in data and make the decisions better for the future result.
Machine Learning Methods
Supervised Learning
The supervised is the first technique that used the labeled datasets to train the algorithms for classification and prediction. Here some adjustments are done for cross-validation of the model to ensure that the model is trained perfectly.
The aim of this learning is to provide the power to learn automatically without involving the human’s efforts.
This model avoids overfitting and underfitting.
As a result, the developer has an ease to tackle the unlabeled data with the help of previous learning through labeled data.
Some of the methods that use supervised machine learning techniques are Support Vector Machine (SVM), Naïve Bayes, Linear Regression, Logistic Regression, Random Forest, etc.
Unsupervised Learning
- Firstly, this learning technique works while finding hidden patterns in the datasets. For instance, this technique discovers the hidden patterns for groupings the data without the need for human involvement. Moreover, unsupervised learning has the ability to detect similarities. In addition to this, it helps to find differences in the information present in the data make it the ideal solution for exploratory data analysis, image, and pattern recognition.
- Secondly, it helps to reduce unwanted features in a model through the process of reduction and normalization in the dataset given to perform the unsupervised learning model.
- Thirdly, some methods that use unsupervised learning techniques are k-means clustering, Probabilistic Clustering, and more.
Semi-supervised Learning
It is an intermediate technique b/w supervise and unsupervised machine learning. It is very similar to them. But this uses the small chunks of data to guide the model. After that, the extraction of the features and performing classification in the large set of unlabelled data is so easy.
Reinforcement Learning
- Firstly, the RL model is similar to other popular models, However, a feature used in this model is different and it works by doing trials and finding errors.
- Secondly, this model will help them to not use the sample data for training the model.
- Thirdly, it increases the list of successful outcomes concludes that reinforcement learning is the best recommendation or strategy for a given problem.
Real-world machine learning use cases
There are many reasons to use it. Above all, implies this.
Moreover, these are some of the few examples from machine learning use cases that you might encounter in your day-to-day life:
- Text Classification.
- Face Recognition.
- Cyber Security.
- Healthcare.
- Speech Recognition.
Challenges of Machine Learning
- Technological Singularity
- Privacy
- Bias and Discrimination
- Accountability
I hope this article made you help gain a better insight into this concept.
Thank you
For more information on Machine Learning, click here.
In conclusion, Machine Learning is an important aspect. This is the end of the Blog. In the next blog, we will surely discuss some of the important topics related to machine learning. Thanks for the readings.
