Machine Learning- How to Kickstart Your Learning Journey


When it comes to pursuing a career in machine learning, it is not as simple as learning and getting ahead in their careers. Beginners are often baffled with innumerable learning resources out there. This guide tells precisely where to kick start your expertise towards ML.

Step 1: Maths and Statistics

The foremost thing is the skill to comprehending problems through a mathematical instinct. It is highly recommended starting with fundamentals in linear algebra, then slowly moving to calculus. It may be difficult to master them at the beginning but given the time and practice with working, these areas will be acquainted and comfortable to work on. Another important subject that follows math is statistics. For any ML algorithm to be understood clearly, statistics is crucial. Hence, fundamental knowledge in stats should be learnt hand in hand.

Step 2. Programming

For beginners, programming is sometimes appalling to learn. Obviously, it might be daunting and difficult at first. But with consistent practice, this skill can be aced ultimately. Coming to programming languages in ML, Python stands out top due to its usefulness and ease in coding. Other languages such as R, Julia and Java also help in developing ML projects. Ultimately, programming should match your math and stat knowledge.

Once mastered, try working on numerous problems and create small projects around ML. See how it solves a problem principally by making use of math and statistics concepts. Sites such as Kaggle and DataCamp are really good at testing codes and cooperating with peers and developers. In addition, forums like Stack Overflow are outstanding to discuss problems and questions related to programming.  

Step 3. Application-Oriented Approach
Learning is only successful if it is applied rationally. Many people do the mistake of learning every algorithm in ML and forget where it truly helps in solving a problem. For beginners, it is recommended that they learn the popular and standard algorithms. A complex algorithm is not always the solution for complex applications. It is all about how an ML problem is solved optimally.
Here are a few blogs which brilliantly tells about applications in ML, which is suggested for every beginner to go through them for realising how ML is actually helpful.

·    Machine Learning Mastery by Jason Brownlee – An amazing blog by expert Jason Brownlee. He explores the fascinating world of ML and captures its essence in the real world.
·       Adam Geitey’s blog– interesting write-ups in ML and Python

·   Arthur Juliani’s blog on Reinforcement Learning – an absolute gem of a blog which particularly focuses on reinforcement learning in ML.

Apart from the tips discussed-above, you can also join a machine learning course through a reputed institution to kickstart your machine learning journey. Quality institutions start from teaching the fundamentals of the subject-matter before digging deeper into it.

Location: Bengaluru, Karnataka, India

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