Skip to main content

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)

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!!






  

Comments

Popular posts from this blog

Rising of the AI in the human centric Development

Rising of the AI in the human centric Development The rising of the artificial intelligence in later 90's have make a rapid impact in field of technology and from 21st century the blooming of a mechanism makes several impact in various industries including software, education, healthcare and many more. As the world becomes increasingly reliant on technology, the role of  artificial intelligence  (AI) in human-centric development has risen to the forefront. From healthcare to transportation to education, AI is being used to improve the lives of people around the globe. Major areas where artificial intelligence AI makes an Impact One area where AI has made significant strides is in the healthcare industry. AI-powered virtual assistants can now assist doctors in diagnosing and treating patients, freeing up valuable time for medical professionals. In addition, AI-powered wearable devices can track a person's health and alert them to any potential issues. The transportation industr...

The Magic of Data Visualization using Matplotlib

      The Magic of Data Visualization Using Matplotlib Matplotlib is a multiplatform data visualization library built on Numpy arrays and designed to work with broader Scipy Stack. Matplotlib was developed by John Hunter in 2003 with version 0.1. This project is supported by Space Telescopic institute for complete development and extension for better capabilities. Matplotlib library enhances the plotting and visualization technique in python. As using the matplotlib we can create various plots, histogram, maps, chart and many more plotting. Visualization of Data     Important features of Matplotlib   It play and operates well with many operating systems and graphics back-ends.   Matplotlib have strength of running cross platform graphics engine smoothly and reliable to different types of graphics system.   There are various API’s and wrappers make this library to useful to dive into Matplotlib’s syntax to adjust the final plot output. Customizatio...

Components of Data Science Life Cycle

                                            Components of Data Science Life Cycle Data Science continues to evolve as the one of the most promising and demanding career of 21st century. The insights drawn from the data is very much useful and profitable for the businesses when processed with intelligent algorithms to find pattern and insights from it. The complete Data science follows a life cycle pattern which defines the steps of each stage of data and apply them to make it processed in more informative and easier way. The components of Data Science life cycle consist of five stages. Each stage have different tasks which perform on data during complete life-cycle span of Data science.                                                        ...