Machine learning has a different approach than traditional practice. In traditional programming, we write code to solve a problem and In Machine learning, we feed data to algorithms and algorithms learn from data and create an equation(algorithm) that can be used to predict unseen data.
In this post, we will discuss machine learning, types of machine learning, the use of machine learning, and the limitations of machine learning in simple words.
Types of Machine Learning Algorithms
There are three types of machine learning algorithms based on the type of training types.
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Supervised Learning
In supervised learning, we have input and output data, and we train our model to extract features from input data and output data, It’s similar to how we learn in school, we have questions and answers, and we learn on the basis of questions and answers and we can predict answers in the exams based on our learning.
Supervised learning is widely used in classification and regression problems.
Predicting something in categories is called classification and predicting something in numbers is called regression. we will discuss classification and regression in detail in upcoming posts. keep reading.## Use of Machine Learning
Nowadays machine learning is used in almost every field, from defence to agriculture. The aim is to make things easy, faster, accurate and efficient.
Machine Learning in Agriculture
Machine learning is used in agriculture to predict weather forecasting, soil analysis, crop yield prediction, disease detection, etc.
Machine Learning in Defense
In defence, machine learning is used in many ways such as face recognition, object detection, speech recognition, threat detection, etc.
Machine Learning in Healthcare
In healthcare machine learning is used in many ways such as disease detection, drug discovery, medical imaging, etc.
Machine Learning in Finance
In finance, machine learning is used in many ways such as fraud detection, stock market prediction, loan approval, etc.
Limitations of Machine Learning
Like every other technology machine learning also has some limitations. Machine learning depends on data, There are many factors which can affect the performance of machine learning models such as data quality, data availability, bias in data, overfitting, underfitting, etc.
There is a saying in machine learning “Garbage in Garbage out”. If we feed garbage data to the machine learning model, it will give garbage output.
Conclusion
It’s the 21st century and machine learning is everywhere and it’s evolving day by day. It’s very important to understand machine learning and the logic behind it. we will discuss the logic behind machine learning in upcoming posts.
Thanks for reading this post. I hope you like this post. If you have any questions, then feel free to comment below.
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