CONVERSATION INTO MY MACHINE LEARNING JOURNEY (PART 1)
A casual tea sharing with a friend jolted into gisting about my transition to tech. I always find comfort reminiscing on the back story of my Machine Learning journey.
Transitioning was cloudy! The bright side of the action I was about to take was kidnapped, or perhaps missing with no existence. The tunnel’s light was severely hazy. I felt so at ease in my own space. However, my long throat and appetite for challenging task and career refined my crude comfort.
My world view about life makes me understand that:
“Peace without challenge will result in chaos of mind”, and “mind without vision will ply road with so many portholes that will hinder it to get to the destination”, though the road to destination is not always smooth.
All these jealously guarded my story. Story aided with enormous questioning. One of such, questioned, how I could make a smooth transition, given that I had previously collaborated closely with those who I would soon be competing with in building with keypads resulting to deep-thinking and solution-driven products. Then comes the unruly prophet of doom and gloom, questioning the number of years of the people in the game. However, my fate in blooming years ahead where I would be part of team(s) that will develop products that would serve as industry standards for solving specific problems and spur industry growth dissuaded that thought.
Mind was made, plans were drawn, and actions were taken. Found myself in the pool of programming and data. The thrilling part is how data solve problems and bring about growth (which is my core strength). The intelligence from data, being the pathfinder for growth, will captain the innovation process to the execution stage garnishing the road to victory, accompany by clear vision.
Into our conversation, now my guy asked:
“How did I make it to this point, learning the guiding principles of Machine Learning?”
To me, answering this question will take me on a journey. I decided to make it succinct by painting an illustration picture. I began the periphery of the story with:
(Note: this is sequel to the cumulative build-up story )
To every house owner, each one at one point had the picture design of the house they wanted to build in their minds depending on the resources available to them.
This was my case of learning Machine Learning. The picture design of my house was Machine Learning.
To every high-rise buildings and skyscrapers, foundation is the bedrock. The quality of the soil, the area, structural planning etc, determine how high a building can go.
In my own case, I don’t know how high my building would go (thus, it’s lifelong learning journey). So, I decided to have a rock solid foundation.
Got a land (conceptualized the idea to learn Machine Learning). Proceeded to check the structural planning (formulated my machine learning road map). Did a forensic analysis on the area that host the land which will invariably host the building (mapped out the tools needed to be learned before I can start building Machine Learning solutions). Tested the quality of the soil (deciding the germane tools in building Machine Learning), and some other back-end processes that needed to be followed.
Now, relax and chill. Drink some water, and wait for the CONTINUATION PART OF THIS JOURNEY
