Introduction to Machine Learning for Guide -Examturf

Introduction To Machine Learning

The following article provides an outline for Introduction to Machine Learning. The name suggests a machine is learning as humans learn. The traditional problem solving with software involves hard coding the logic which is not possible in many real-life cases. In machine learning, the idea is to show the machine the problem statement and some possible solutions so that it can learn from it and provide answers for similar ideas without giving any explicit instructions or logic. Machine learning is the breakthrough technology of this era where machines are getting intelligence that can ease our lives, and the best thing is they are expanding their knowledge every day or to say every second.

What Is Machine Learning?

To learn anything the crucial thing is information or you can say it data. A machine is fed with this information and learns out of it based on the problem statement. Once the machine has gone through enough information and learned the key aspects of the problem, we can check its knowledge or intelligence or learning by performing tests on it.
The test data (also known as validation set) is fed to the machine to check the machines’ understanding and its performance for the given problem statement.
To check machine’s intelligence their different evaluation metrics, this further depends on the problem statement. Once the results are satisfactory you can put this machine learning model into production. This is just a high-level overview of ML but underneath there’s much more.

Advantages & Disadvantages Of Machine Learning

Given below are the advantages and disadvantages mentioned:

Advantages:

No hardcoding logic is needed here to solve any problem.
The machine itself can identify the pattern and come up with logic.
Better experience for users with aid of machine learning for example take recommendation system of youtube, every user gets recommendation according to his test, geography, and age.
Machine learning is continuously evolving, so in near future, we will see more accurate predictions.
It has wide applications from healthcare, education, retailer business, other days to day activities and many more fields are making use of ML.
More sophisticated tools and technologies are coming up with ML for example take Tesla’s Autonomous car.

Disadvantages:

Like Grass on the other side of the road looks greener, same is with ML too. From a high level, it seems cool but it comes with some challenges as well as disadvantages which shall not be overlooked.

Machine learning is not easy to interpret always. For some algorithms, the results are interpretable but not for all algorithms.
High chances of errors, ML is susceptible to errors, it’s not consistent all the time.
ML is dependent on data that you are training it on. if the quality of data is not good the results out of ML will not be good as well.
ML needs more resources as it needs a huge amount of data for machines to learn.

Components Of Machine Learning

Below is the high-level view on various components of machine learning:

Data acquisition.
Domain knowledge and feature extraction.
Feature selection, feature engineering, and train test split of data.
Choosing the right algorithm.
Training the model and fine-tune it.
Define performance criteria & evaluate the model performance.
Deploy the model to leverage ML capabilities.

Conclusion

Machine learning is surely the technology of the new age and is changing the way we solve real-life problems. In this article, we have seen the overview of ML, the advantages & disadvantages of ML, and various components of ML.