This month has been highly rewarding as I took some conscious steps to redirect my self-taught journey. I'm excited about the various changes and challenges on the horizon and look forward to sharing these experiences with you. Additionally, I have an update on the Machine Learning (ML) project that I'm developing, as promised. So, let's briefly delve into the insights I have garnered and the skills I have acquired during this period.
Accomplishments:
Machine Learning Specialization
Regression & Classification Models
Vectorization, Metrics, Regularization & Optimization Techniques
Random Forest, Clustering, Recommender Systems & Reinforcement Learning
Deep Learning (Neural Network)
Data Analytics
Problem-solving Oriented & Technical Mindset
Communication Between Stakeholders
Introduction to SQL
Career path:
Until now, my learning approach lacked a definitive structure. In my "If there is one thing I have learned" blog post, I expressed my regret over the unstructured learning approach I employed during my initial five months of study, during which I casually brushed over the fundamentals of :
Python
Data Structures & Algorithms
Github
Linux system & Bash scripting
Networking
Subsequently, I started this blog and in around two months, I completed the Machine Learning Specialization on Coursera. Despite this, my practical skills haven't evolved much from where I was seven months ago, when I had the naïve notion that knowing these skills would magically land me a job in the industry. Reflecting upon my "skillset", I found it hard to take pride in my time spent thus far. Envisioning myself five to seven years down the line and after some extensive research, I was able to devise a realistic career roadmap.
My long-term goal for the next 20 years is to become a professional game developer, one that is skilled in ML and can use ML to revolutionalize the entire industry. However, first of all, it's 20 years in the future, I can easily see someone taking that initiative before me. Secondly, I recognized that game development may not be the most lucrative path, so I decided to channel my energy towards mastering the ML component initially. My 7-year goal is to become a Machine Learning Engineer (MLE), a lofty ambition no doubt. Nonetheless, I discovered a viable pathway to achieve this aspiration:
At present, I aim to master the basics and obtain the necessary certifications to transition into a Data Analyst role. So, as of yesterday, I've dived into learning SQL and basic database theory. Later, I'll get my hands dirty with Power BI, which will serve as my initial go-to tool for visualization. Alongside this learning journey, I intend to secure Microsoft certifications for both SQL and Power BI. So, in a nutshell, my roadmap for the next 4 months is to accomplish these tasks.
Upon achieving this, I foresee myself gaining practical, hands-on experience as a hired Data Analyst. After that, I'll step onto the next rung of my learning ladder, aiming to pick up more skills and certifications to become a Data Engineer.
ML project:
And now, it's time to finally unveil the roadmap for my first fun and potentially impactful Machine Learning project. Each block in the graphic below corresponds to a development stage. I'm anticipating a minimum of two weeks for each stage, but some might need a bit more time. For instance, the web app stage involves UI/UX – an area where I'm starting from scratch. So, I'd possibly double or even triple the time to learn and then deploy.
Although I'm not sharing the exact final goal of this flow chart just yet, I'd like to talk about the initial three blocks, which I think will see us through to this July. Basically, I've been going over the documentation of LangChains, a Python module for crafting projects with ML models like GPT4. So, step one is to know this tool, then I'm going to build 2 mini projects:
A personal assistant, and
A text-to-speech and speech-to-text conversion tool.
The grand plan is to build my actual project atop these two mini-ventures. Can't say for sure if it's all going to fall into place, but one thing's for certain: it's going to be a wild ride.
Now, as I'm diverting a large chunk of my energy towards becoming a data analyst, I can't give my all to AI and ML projects. So, they'll be more like a hobby, with about 10 hours a week tops. But, you know what? I believe that continually sharpening my skills with AI tools and churning out diverse projects could catch the eye of future employers. And on a side note, I'm a bit bummed that I never got around to trying out the GPT4 code interpreter. I've seen some really cool quick prompting projects, and it makes me wonder how I could use it for my current project.
And that wraps up my second monthly update. The road ahead is packed with tasks, and I'll be hustling like never before. But I'm convinced that a well-crafted and detailed career plan will steer me towards my first job in data. Thanks again for riding along with me on this journey.