What do you do if you want to break into Machine Learning but have limited coding experience?

starting with introductory coding courses such as Python basics is essential. Python is widely used in the field due to its simplicity and extensive libraries for Machine Learning. Also, focusing on understanding fundamental concepts like linear algebra and statistics can provide a strong foundation. Leveraging beginner-friendly Machine Learning libraries like scikit-learn can help ease the learning curve. Actively participating in online communities and seeking mentorship can provide valuable support and guidance along the journey.

Understanding the mathematical underpinnings of machine learning is crucial for developing intuition about what algorithms are doing beneath the hood. This foundation enables practitioners to make informed decisions about which techniques to apply and how to interpret results. Python's popularity in the ML community is due to its rich ecosystem of libraries and frameworks, such as scikit-learn and TensorFlow, which abstract much of the complexity involved in implementing algorithms.

If you want to get into Machine Learning but your coding skills are limited, the best approach is to just start and get your hands dirty! Perfection isn't really necessary at the beginning. Youtube videos would be one the the easiest ways to get started!

The busy person's guide to breaking into ML: Microlearning. We're all busy these days. Because of this, I find that just a little bit of learning each day (e.g., Microlearning) is a winning strategy for growing our skillsets in a comfortable and productive manner. For those just starting their ML journey, I would recommend mobile apps such as Brilliant (Math, stats, etc.), DataCamp (Python, R, etc.), Leetcode (Data structures, algorithms, etc.), and Pluralsight's Skill IQ (Ongoing assessments of your aptitude in a technology/skill). Each of these platforms accommodate an on-the-go style of learning that I personally enjoy and use on a daily basis while commuting, waiting for my next call, or just relaxing on the couch.

Start learning the fundamental concepts of machine learning, such as supervised learning, unsupervised learning, and reinforcement learning. Understand what algorithms are used for different types of problems and when to use them.

Start working on small machine learning projects to apply what you've learned. Choose projects that interest you and align with your learning goals. Building a portfolio of projects will not only demonstrate your skills but also help you learn practical aspects of machine learning.

Engaging in hands-on projects is a highly effective method for learning machine learning, as it solidifies theoretical knowledge through practical application. Starting with a manageable project allows for a deeper understanding of fundamental ML concepts and provides a tangible way to track progress. Moreover, working on projects can lead to a portfolio that demonstrates your skills to potential employers or collaborators. Utilizing publicly available datasets and participating in competitions, such as those on Kaggle, can also offer invaluable experience and community feedback.

I believe in watching and reading first and then doing, because when you think about how we learned when we were kids it was by observing several times before we did anything. Choose a project when you feel confident that you will understand. Of course, you don't want to get stuck in analysis paralysis. It's almost like a regurgitated rule that we should immediately do because that's the "best way to learn". But says who? I believe learning is about failing, trying something new, and reflecting on what worked and what didn't. Experiment with different ways to learn.

I'd add that keep it simple in the beginning! Stick with python, pandas, numpy and scikit-learn for building your basic models (e.g. Logistic Regression / Linear Regression). There are a lot of packages out there and it might be overwhelming to get started.

Engaging with the machine learning community is crucial for growth in the field. Online platforms offer a wealth of collective knowledge that can accelerate learning. By participating in discussions and consuming content from various sources, you can gain insights into practical applications and theoretical foundations. Moreover, these interactions can lead to mentorship opportunities and collaborations that are invaluable for career development and staying abreast of cutting-edge ML advancements.

DataCamp for the win! If you're a student Github offers a free 3-month trial for DataCamp. I have a featured post with more info.

Coding is something that you learn by doing, not by reading books. And, while personal projects can be fun, most the important learning comes from coding as part of a team, where you depend on others and others depend on you. So, by all means, go through tutorials and practice on standalone personal projects. But you will find that your learning accelerates tremendously when you start coding in earnest. Be ready to work a bit harder than everyone else who has more coding experience. It's hard, but it's worth it; since you'll be the one learning the fastest.