Seven Steps to Success: A Seven-step Guide to Learn and Master Machine Learning (ML)

Author(s): Anushkad

Originally published on Towards AI.

In case you dream of becoming a data scientist or an ML engineer, you’re at the right place to begin your journey with. This guide is completely based on my personal experience on how I developed my machine learning skills and dream of making a career out of it! *Fingers crossed*. Let’s get started with these steps, and success is guaranteed!

1. Practice Python or R: ML models are designed using Python or R. You need a good grip on either of these languages to design ML models effortlessly. Practice Python either by taking local tuitions, doing an online course or YouTube tutorials. To polish your Python, you can be a part of the Hacckerrank community and earn badges and learn new concepts!
2. Understand basics: Now, this might seem negligible, but basics play a major role in laying the foundation of your understanding of more complex concepts. To begin with basics, you can read what exactly ML is on Wikipedia, study its benefits, and real-life applications. You don’t need to read it all; all you need is an overview of what you’re heading towards. Blind learning often leads to nowhere. It’s better to have a path to follow. Don’t have one? You’re just at the right place to map it for yourself! Keep reading.
3. Social media: This might make you wonder, but according to a survey, young adults spend most of their precious time on cell phones, especially on Instagram. *scrolling* Instead, you can follow pages related to ML and learn a lot through their everyday posts. Usually, these posts are like flashcards and help you remember the concepts you already know or teach you new ones. It’s a great way to spend some of your social media time being productive.
4. Create a network: Having a LinkedIn account is a must! It’s educative, keeps you updated about the trends, and you’re all up-to-date. LinkedIn also has groups or rather communities, especially for ML practitioners. Join these groups and read what these experts are up to.
5. Kaggle: Kaggle is a great platform to apply your understanding and design small ML models. Micro courses offered by Kaggle are great, too, plus they offer free certification! *cherry on top*. Participate in competitions, solve problems, and maintain your streak.
6. Coursera: There are many courses out there on Coursera that you can audit for free, and, to begin with, I’d suggest the course offered by Stanford University by Professor Andrew Ng. It’s a great course. *It’s kind of a tradition that every data scientist or machine learning practitioner has been a part of this course* So go ahead and enroll yourself!
7. Udemy: Udemy is a lot cheaper than Coursera, and some courses available on Udemy teach every little detail of that subject. Machine learning: A-Z is one ML course on Udemy that you can spend a little amount on because the outcomes are worth it.

What comes along with these steps are the efforts you put in understanding these concepts and the consistency and dedication you put in to master them. Learning to code and designing algorithms is immensely rewarding and satisfying, but can also be difficult and frustrating. It takes time to acquire and master any skill. It cannot be done in a day or even a week. With that said, I believe anyone can learn any skill if they are willing to show that amount of patience. Enthusiasm wears off once it becomes a daily task to practice. Take the utmost care that it doesn’t affect you and won’t put you in the habit of procrastination. There are going to be times when you’ll be stuck at some concepts and may question your ability to progress ahead. But with persistence and grit, success knows nothing that can limit it!

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Published via Towards AI