5 Steps To Foray Into Machine Learning

Artificial intelligence (AI), machine learning (ML), deep learning are some of the buzzwords of the modern-day technology era. So many aspects of our daily lives that are touched by machine learning that one can not overlook the importance of getting acquainted with the fundamentals of the field. Machine learning, the term coined by Arthur Samuel, is a subfield of artificial intelligence. In simple words, machine learning is a field of study that provides machines the capability to learn, without being programmed explicitly for all scenarios.

What Are ML Algorithms?

How are machine learning algorithms different from usual algorithms, one may ask. We usually write algorithms which accept data as input and provide output as per the logic programmed in the code. Machine learning algorithms, on the other hand, treat data as training material. They also learn from the output produced for a set of input, and through a series of concepts like classification, correlation etc can then learn or create logic to handle extended cases.

Stepping Into Machine Learning

Let us get started around how one can step into the world of machine learning.

Learn A Language

Since machine learning involves algorithms and code to be written, the first step would be to pick a programming language to learn. Python is a popular language for data science and machine learning enthusiasts, since there is a certain ease in learning the language for complete beginners.

• The quick learning curve ensures you can start getting your hands dirty quicker. Python also has plenty of statistical and machine learning modules as libraries, so you can implement broader concepts easily, without having to implement every smaller function on your own.
• Learning Python is useful from a general software engineering standpoint as well. It is more widely used, so it can prove to have more usage for a student in the future.
• To learn Python, one can use courses available on any of the platforms like Coursera or Udemy. There are books like “Head First Python” and “Learn Python the Hard Way” which can help those who prefer books over online courses.

Mathematics And Statistics

If you eventually get into any course material around Machine Learning, all of them would mention techniques from algebra, probability, calculus, statistics etc. In order to grasp the concepts completely and have a firm hold on understanding the algorithms, it is important to understand high school mathematics extremely well. There are courses offered by Khan Academy on statistics, probability, linear algebra and calculus, which can be undertaken to gain insights and eventually an understanding of the concepts of machine learning.

For more advanced mathematics, ML enthusiasts may use the following sources.

Youtube Lectures | “Mathematics for Machine Learning - Linear Algebra”; “Multivariate Calculus by Imperial College London” by Dr. Sam Cooper & Dr. David Dye

Books | Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong; Applied Math and Machine Learning Basics by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Having in-depth knowledge of the school mathematics curriculum will work perfectly for beginner-level problems.

Get Hands-On With Libraries

After you've learnt the basics of Python, you need to understand Numpy Arrays and Pandas, as they are used for moving around and modifying the data you use, while Matplotlib is used to visualise this data through graphs and diagrams. A lot of beginner-level problems, like creating word clouds of most reflected sentiments from tweets on Twitter, leverage these libraries. There are resources to get some hands-on experience with Numpy and Pandas on the internet as well.

Machine learning is a field of study that provides machines the capability to learn, without being programmed explicitly for all scenarios.

Also Read | Career Opportunities For Engineers Of The Future

Online Courses

After all the groundwork you have laid, you can get started on acquiring knowledge of machine learning through dedicated courses. Some of the Machine learning courses you can try are:-

Andrew Ng – Coursera | This is a highly-recommended course for those wanting to learn ML. It may seem slightly challenging at times because its components include partial derivatives. However, the idea is for learners to grasp the broader concepts of machine learning.

Kirill Eremenko And Hadelin De Ponteves – Udemy | This course appropriately in-depth to teach the functionality of the algorithm, but without complex mathematics, thus making it easy for high school students to grasp the concepts.

Explore Real-Life Datasets

After all the conceptual learning, it is time to get your hands dirty and understand things practically.

• Kaggle offers problem sets based on difficulty which can be used as a learning ground to solve problems, and also learn from the huge community that comes as part of such a website.
• If you hit a roadblock, community threads and websites like Stack Overflow can serve as guiding nudges. There is hardly a question that isn’t answered on Stack Overflow.
• There are certain popular, beginner-level ML problems around digit recognition, sentiment analysis of tweets, labeling an image, chatbot, which can be done with some effort.
• In this process, you will also get familiar with tools that can help you collaborate on machine learning projects, like Jupyter notebooks. This also ensures that certain basic software engineering concepts are also covered in the process.

Explore Further

After all the initial steps are done with some level of comfort, one can also try to explore the different subfields of artificial intelligence like computer vision, natural language processing etc, so that they can figure out their area of liking and dwell more into such a field.

Read Scholarly Articles

It is also important to develop a habit of reading scholarly articles, since a lot of concepts are derived out of research. It is important to keep exploring new concepts to stay abreast with the latest developments, and enhance your knowledge of the field by discovering new areas of implementation and use-cases for machine learning. You can also try to explore real life scenarios of usage of machine learning and delve deeper into the implementation of such algorithms at a high scale.

Deboshree holds a BTech in Computer Science and Engineering from BIT Mesra. Backed with 6 years of experience working with Goldman Sachs and Walmart, she currently works with Cred as Backend Engineer.