Beginners Guide for Machine Learning

                                 Beginners guide for Machine Learning

Machine learning is nowadays a highly trending domain in computer science and many students, researchers, professors and experts using this technology to solve real-world problems human interaction.

Machine learning is a field of computer science in which the computer learns the finding pattern and insights from the data that are feed into the learning algorithms. So machine learning makes a big impact in the field of science by solving high complex and data-based tasks.

So the rapid growth of this technology makes the interest of a huge number of people from a different domain to learn this technology and solve real-world problems and help the communities using machine learning.

In this article, we are going to discuss how a beginner should start in the field of machine learning. Machine learning is a combination of elementary mathematics including concepts of (linear algebra, multivariate calculus, probability, etc.) and learning algorithms(linear regression, logistic regression, decision tree, etc.)

For a beginner, they should start machine learning from the most popular and highly rated course by Andrew Ng one of the most prominent professor of this domain Intro to machine learning. After enrolling in this course start learning the basic mathematics (parallel) that are required for this course. Professor Ng has already explained basics mathematics in this course that is sufficient for machine learning but to understanding concepts much deeper, you should have deep dive into these fundamentals.

1. Linear Algebra (Matrix, vector)

2. Multi-variate Calculus

3. Probability Theory And Statistics

All these topics are highly required for doing machine learning and understanding the basic concepts of this field. Generally, there are two way of learning mostly prefer by student:-

1) Project-Based Learning:- This is the almost most preferable way of learning. In this learning methodology, students should apply their learning by solving problems (project-based) and get better by applying new methods, applying alternate approaches, improving the existing solution.

2) Theoretical Learning:- In this type of learning one should have first learned all the theoretical concepts of domain and learn almost various concepts and then apply it into a problem. It is also a better approach for research-oriented individuals.

After learning the concepts applying it into the problem and take part in the online challenges of data science and machine learning and learn more.

I hope this article helps all the beginners of machine learning to start the journey in this field!!