learn artificial intelligence and machine learning

How to Learn Artificial Intelligence and Machine Learning

Table of Contents

The world has been jumping through hoops to reach the technologically advanced state that we are in at the time. Having said that, it is safe to say that we are moving towards, and expecting, a whole another state in alignment with the technology in the near future. 

Now, when we talk of the world and technology in the same sentence, you may have been acquainted with the hot buzzwords: Artificial Intelligence and Machine Learning. Also, often we come across people, or you yourself may be the one, who wants to learn and study AI (Artificial Intelligence) and ML (Machine Learning) in depth. 

Therefore, through this article, we do not wish to spoon-feed you as to what Artificial Intelligence and Machine Learning are, or what is the difference between Machine Learning v/s Deep Learning. Instead, here we talk about how you can learn Artificial Intelligence and Machine Learning from your end. 

Know Your What(s)

Yes, you want to learn Artificial Intelligence and Machine Learning, but these two concepts are as wide as the sky. Thus, while you plan to learn the same, make sure you have answers to:

  • What do you want to learn?

The umbrella terms Artificial Intelligence and Machine Learning cover a vast variety of topics: data science, big data, computational skills, data analysis and manipulation, Neural Networks, etc. Thus, before or during the process of learning, analyze where your inclinations lie. 

  • What are the prerequisites to learning AI and ML?

Another important element to consider while learning Artificial Intelligence and Machine Learning is to know what you should know, or have, before learning the same. To begin with, you may want to have a command in Python, be thorough with linear algebra, calculus, and statistics. 

However, if you do lack the same, then enrolling in an AI online course for beginners can help you with covering the basics. 

Know Your Why

Here is the hard truth: Artificial Intelligence and/or Machine Learning are not the concepts that can be stirred in a glass of water to be swallowed in one go. As these two fields are a few components of the ever-changing dynamics, excelling in them requires extreme time, efforts, and at times, investment. 

With that being said, none of us would like to place any of our resources into something that does not have a clear purpose, and even if we do, procrastination and lack of interest will catch you before you know. Therefore, it is crucial to know why you want to learn Artificial Intelligence and Machine Learning: it could be to open the door for better job prospects, or to create some project, or to simply apply the concepts and make the tasks easier. 

Know Your How

Now that you understand what you specifically want to learn, and why, it is important to know how to do the same. 

  • Get the resources

There are numerous tools and resources available on, and off, the internet which can surely help you in the process. As some of you may be learning from scratch, you may want to explore some of the open-source tools to learn Artificial Intelligence and Machine Learning: Python, R, Scikit, PyTorch, etc. 

  • Go with your learning style

It may not seem important at first, but half-way down the road you will understand that instead of simply jumping into something new, it is better to align the concepts with your learning style. 

Some of us can not do without flexibility, for those, an Artificial Intelligence Online Course is the way to go. Well, and some learn at their best when they feel the immediate need to win the competition; if you are amongst the latter category, you may want to participate in AI and ML-based competitions to stay engaged. 

After following through the above-mentioned points, we suggest you work on a project and implement your knowledge in the field of Artificial Intelligence and Machine Learning. Not only will it solidify your expertise, but might also turn out to be your gift to the integration of technology with human-based tasks. 

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