Python to Machine Learning: Upskilling from Scratch
Announcing The Stargate Project
— OpenAI (@OpenAI) January 21, 2025
The Stargate Project is a new company which intends to invest $500 billion over the next four years building new AI infrastructure for OpenAI in the United States. We will begin deploying $100 billion immediately. This infrastructure will secure…
When I read this announcement of big tech partnering up with the US government to invest $500 billion into AI over the next four years, I’m reminded that with all the current chaos of the world, the tech industry is always evolving and it’s never too late to pivot. I feel a mix of excitement and urgency as doors of opportunities multiply, determined to sign and accept an offer waiting for me behind one of them very soon.
What I enjoyed during my teaching years was solving problems and simplifying complex concepts into easy-to-understand explanations. Machine Learning offers the perfect blend of math, programming, and real-world impact I look forward to contributing to. I’m particularly drawn to the current climate and growing adoption of AI and Machine Learning within healthcare and education.
Specializing in Math also lent to my growing interest in learning Python alongside Math for Machine Learning. Python is a versatile programming language with friendly syntax that’s accessible for beginners like myself. It’s also the dominant scripting language used in Machine Learning so learning Python had to be a priority.
So with this in mind, it was time to be a student again. I created a 10 week learning plan to solidify my foundational knowledge of Python and complete at least three Machine Learning projects.
Following this 10 week plan, I will then carve out the next stages of my journey to successfully land a role as a Machine Learning Engineer or Data Scientist.
Weeks 1-2:
- Mastering core Python skills: for loops, list comprehensions & functions while exploring data structures and file handling.
Weeks 3-4:
- Data manipulation & visualization using libraries like Pandas and Matplotlib
Weeks 5-6:
- Apply Python skills to ML basics using Scikit-Learn to build & evaluate simple models.
Mental Checkpoint: Assess progress / what’s working / what’s not working
Weeks 7-8:
- Focus on intermediate projects, like building a classification model to predict outcomes from real-world datasets
Weeks 9-10:
- Explore advanced topics, including neural networks and hyperparameter tuning, while wrapping up with a portfolio-worthy end-to-end project.
This structured approach will help me build a strong foundation while gradually tackling more complex concepts, all while staying motivated and on track.
I’ll be dedicating 2-4 hours per day to this learning plan, alongside my current daily target of 50 XP on Math Academy which I’ll talk more about in a separate blog post. On weekdays, I will focus on the learning portion and practice with coding exercises, while on weekends I’ll focus more on project work and reviews.
I’m excited to share my progress, projects, frustrations, lessons, over the next 10 weeks so stay tuned for my progress reports. If you’re also learning to develop your skills in math, AI/ML, programming, or tech, let’s connect! I’d love for us to share what we’ve learned and grow together.
Here’s to 10 weeks of locking in, embracing growth, seeing through challenges, and celebrating breakthroughs. Let’s build the future, one line of code at a time!