Machine Learning for students – Where & How to begin?

Today’s world is caught up in a technological revolution. With unique inventions and marvelous advancements in daily life equipment, science is amusing us every day. In an age where even social interactions are happening through online platforms, technology is no longer a luxury, it is a necessity. 

We have so many questions about the new tech inventions like, 

How do they do it? 

How are they recommending so accurately?? 

How do they autocorrect our mistakes and cater to our interests? 

Artificial Intelligence is rising to its peak and it is high time to learn what can be achieved with these applications. Machine learning is not only an integral part of the AI industry but it is also one of the best things we will ever learn in this era, especially if our skills and concepts are top-notch.

There are a lot of opportunities available for students in the field of Machine Learning. Major companies like Amazon, Google, and Microsoft are offering internships for students in the field of ML and software engineering. These are golden opportunities for a career and are great learning platforms for ML.  

For anyone new to the world of machine learning, the first question popping in your mind is, “Where should I begin?” 

If you are learning on the right path, the result will be perfect. So, it is important to know how and where to study. Let us see some initial preparatory steps we need to take before entering the vast ocean of machine learning. 

1. Set the goal 

The very first thing to do before studying any course is to build the mindset and willpower to finish it. As easy as it sounds, many courses might have gone unfinished for many of us due to different reasons. Remember, “Little learning is a dangerous thing”. In coding, if you stop halfway through the training, you can do nothing with the knowledge you have. So, always challenge yourself to finish whatever you have begun. 

Setting the goal also means setting a timeline for your target. Many online courses will give you a flexible deadline for completing a course which you can change according to your will. Try not to do that. Stick to the deadline target and if the course doesn’t provide you with a target, set one yourself.  That way you can use your time and effort effectively. Also, remember not to overwork yourself just to finish fast. This may affect your willpower to learn. Make an even distribution of quality time daily. 

2. Choose your language

There are a lot of programming languages available and many of them are popular. You can do machine learning with any language. It is important to choose a language and build your foundation in it. 

Here are the top 5 languages suggested for machine learning:

• Python 

Python is the revolution today and many machine learning enthusiasts learn python exclusively for machine learning, even though they are familiar with other languages. It is easy to learn and apply with a wide range of areas to modify. Its flexibility is the key to its popularity.  You can learn python basics free through many websites and courses.

• C/C++

C is one of the oldest languages developed and has been improvised for decades. It is a bit complicated and has a lot to learn for even simple problems. But once you learn it, it can act as a base for coding and any other programming language will be easy to learn in comparison. Also, many companies specify expertise in “C” for job opportunities. So, it is an important language to learn. 

• R 

R is one of the top languages for Data Science. This open-source language has its roots in statistics, data analysis, and data visualization. In recent years, it’s become the choice of new generation analysts who have appreciated its value for the open-source community. 

• Java

Java is a necessity in many machine learning arithmetics. It is an incredibly useful, speedy, and reliable programming language that helps development teams build a multitude of projects. From data mining and data analysis to the building of Machine Learning applications, Java is more than applicable to the field of data science.

• Scala 

Scala is another language that has been popular with data scientists and machine learning specialists. It is an implementation language of technologies that enable streaming data, such as Spark and Kafka. Scala combines functional and object-oriented programming and works with both Java and Javascript.

Even though there is a multitude of opportunities, it is always better to start learning basic languages like Python or C++. If you already know the basic coding in any language, you can directly study programming for machine learning. For example, you can study Python for machine learning directly if you are well versed in Python basics. 

Some major course websites through which you can learn any of these languages are:

• Udemy
• Udacity
• Coursera

Some tutorial sites where you can find full tutorials for programming languages with explained notes:

• Tutorialspoint
• W3Schools

3. Familiarise yourself with your IDE

IDE or Integrated Development Environment is the platform where you will be doing all your studies and work. After choosing the language, you have to find out the IDE to work with, considering your comfort and flexibility in using the platform. Most of the online tutorials or courses may already teach you with one platform, but make sure you find one perfectly suitable for your environment.

Some of the important IDEs available are:

• Visual Studio Code

https://code.visualstudio.com/

• Atom

https://atom.io/

• Sublime Text 3

https://www.sublimetext.com/

• Spyder

https://www.spyder-ide.org/

• PyCharm

https://www.jetbrains.com/pycharm/

• Visual Studio

https://visualstudio.microsoft.com/

• Jupyter  Notebook

https://jupyter.org/

All these platforms have their merits and demerits. It is always best to find out which one is most suitable for your language. For people choosing Python, VS Code, Jupyter Notebook and pycharm are some great options to try out. 

4. Introduction to Machine Learning

This is the final preparatory step of your entire learning process. Understand what machine learning is and study the specifications of the language you have chosen concerning ML. There are a few tips to keep in mind when you start learning:

• Always follow a single study pattern –  This means that you never jumble between courses or lectures. If you prefer the study method of taking short tests after every topic, don’t stop it and move to a course where tests are conducted at the end of the entire course or module. 

• Be active with coding –  Even if you know the basics of coding languages, you should constantly practice by doing more and more problems daily. “Hackerrank” is an amazing platform to practice coding. 

• Apply your knowledge – Once you learn some basic ML, apply it to various models, and try out the results. You can even build your models after the basic training. Never hesitate to apply what you learned! 

After having a clear understanding of the technology, you can participate in many hackathons and project competitions to complete application-level learning. These experiences will lead you to the perfect internships and projects by famous companies and also enable the right kick-start for your future.

Read our article to know how participating in hackathons can benefit us in many ways. 

Find the real thought of ‘stepping out of your comfort zone’ through hackathons by reading the story of Medha Aiyah, the founder of the first gender-based hackathon. 

Some major career options for students in the field of MI are discussed below: 

Internships

• Institutions like IITs provide public internships for people with experience in ML for overseeing projects and providing technical guidelines

• Microsoft is providing research facilities as interns for students with exceptional skills in Machine Learning. Also, there are many pieces of training and workshops available for students by Microsoft. Taking those workshops and courses will give you a better chance of securing an internship at Microsoft. 

• Companies like Google and Amazon also provide research and project internships for students. 

Fellowship programs

Fellowship programs are great opportunities to get hands-on experience and secure an amazing career. Many companies like Uber, Facebook, and Vodafone hire students for fellowships and are great opportunities to improve your skills. 

Two major company websites providing fellowship opportunities in the field of AI are:

• https://fellowship.ai/
• https://insightfellows.com/ai

Machine learning is one of the few branches of tech that allows us to expand our learning experience. Through the right steps and scheduled learning, anyone can acquire the technical skills required for the wonderful opportunities available in this field. So, believe in yourself and invest your time in your field of interest!