How I Became a Machine Learning Engineer Without a CS Degree or Bootcamp; When I first became interested in machine learning (ML), I had no formal computer science (CS) degree and had never attended a bootcamp.
My background was in a completely unrelated field, yet today, I work as a machine learning engineer. This journey was filled with self-study, countless hours of trial and error, and a determination to break into the tech industry.
Discovering Machine Learning with Degree
How I Became a Machine Learning Engineer Without a CS Degree or Bootcamp; My interest in ML started when I stumbled upon an article about artificial intelligence (AI) transforming various industries.
The idea of computers making intelligent decisions fascinated me, but at the time, I had no programming experience and no knowledge of mathematics beyond basic high school algebra.
I started by watching YouTube videos explaining ML in simple terms. Some of the first channels I explored included 3Blue1Brown, Sentdex, and Two Minute Papers. These videos introduced me to concepts like neural networks, supervised and unsupervised learning, and the importance of data.
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Learning Programming from Scratch
To work in ML, I knew I needed to learn programming, particularly Python, which is widely used in the field. Without a CS background, I had to start from the very basics. Here’s how I did it:
- Learning Python Syntax
- I started with Python for Everybody by Dr. Charles Severance (available on Coursera for free).
- I practiced coding exercises on Codecademy, LeetCode, and HackerRank.
- I built small projects like a simple calculator, a to-do list app, and a weather scraper to gain confidence.
- Understanding Data Structures and Algorithms
- I read the book “Grokking Algorithms” by Aditya Bhargava.
- I solved problems on LeetCode (easy problems first, then moved to medium difficulty).
- I watched YouTube explanations from CS Dojo and Abdul Bari to understand sorting algorithms, recursion, and dynamic programming.
Diving Into Machine Learning Fundamentals
Once I had a basic understanding of Python, I started learning ML fundamentals. My approach was to learn through free online courses and practical projects:
- Mathematical Foundations
- I took Khan Academy’s Linear Algebra and Probability & Statistics courses.
- I followed 3Blue1Brown’s videos on linear algebra and calculus.
- I read “The Elements of Statistical Learning” (though challenging, it gave me a solid foundation).
- Machine Learning Concepts
- I completed Andrew Ng’s Machine Learning course on Coursera, which was an eye-opener.
- I studied Google’s Machine Learning Crash Course.
- I read “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron, applying concepts as I learned.
Building Practical Projects on Machine Learning Engineering
Rather than just learning theory, I started applying what I learned through projects. These projects served as both learning tools and portfolio pieces:
- Predicting House Prices
- Used datasets from Kaggle.
- Applied regression models using Scikit-Learn.
- Learned about data cleaning, feature engineering, and model evaluation.
- Image Classification with CNNs
- Used the MNIST dataset to build a digit recognizer.
- Implemented convolutional neural networks (CNNs) with TensorFlow and Keras.
- Learned about deep learning concepts like activation functions and backpropagation.
- Sentiment Analysis on Twitter Data
- Scraped Twitter data using Tweepy.
- Used NLTK and Transformers for sentiment analysis.
- Experimented with pre-trained models like BERT.
Contributing to Open Source and Networking
To gain more exposure and real-world experience, I started contributing to open-source projects:
- GitHub Contributions: I worked on beginner-friendly issues in ML-related repositories.
- Kaggle Competitions: Participated in Kaggle challenges to gain hands-on experience.
- Blogging & LinkedIn: I wrote about my projects and ML concepts, which helped me build credibility.
I also joined online communities like r/MachineLearning, Towards Data Science on Medium, and LinkedIn groups for networking. This allowed me to learn from others, get feedback, and discover job opportunities.
Landing My First ML Job
After months of learning and project-building, I started applying for jobs. Since I didn’t have a formal CS degree, I had to rely on my portfolio and networking:
- Tailoring My Resume
- Highlighted my projects, skills, and GitHub contributions.
- Emphasized self-learning and hands-on experience.
- Preparing for Interviews
- Studied ML interview questions from Glassdoor and InterviewBit.
- Practiced coding problems daily on LeetCode.
- Reviewed concepts from Andrew Ng’s Deep Learning Specialization.
- Networking & Cold Emailing
- Reached out to professionals on LinkedIn.
- Asked for informational interviews and career advice.
- Applied for internships and freelance ML gigs to gain experience.
Finally, I landed my first role as a Machine Learning Engineer Intern, where I worked on a recommendation system project. This internship later turned into a full-time role.
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Lessons Learned & Advice for Aspiring ML Engineers
- Self-discipline is key – Without a structured program, you must stay motivated.
- Work on real-world projects – Employers value practical experience over certificates.
- Networking matters – Engage with ML communities and share your work.
- Continuous learning – ML evolves rapidly, so keep learning new advancements.
- Don’t fear rejection – Job applications come with rejection, but persistence pays off.
Conclusion
How I Became a Machine Learning Engineer Without a CS Degree or Bootcamp; Becoming a machine learning engineer without a CS degree or bootcamp was challenging but rewarding. By leveraging free resources, building projects, contributing to open-source, and networking, I carved my own path into the field.
If I could do it, so can you.
Stay curious, keep learning, and never give up on your goals!