Artificial intelligence is the driving force of the industrial revolution. I have a bachelor's degree in industrial engineering and a master's degree in sustainable energy engineering. After graduation, I decided to learn machine learning so that I could play a role in this technology that impacts human life.
Before I get started, I had a lot of questions and I remember feeling lost on who to ask them.
Which resources to use while learning?
Should I remember everything I am learning? How to take notes?
Should I spend months on the theory and mathematics behind machine learning?
What is Machine Learning?
To introduce, Machine Learning (ML) is a type of artificial intelligence (AI) that allows computers to learn from data to discover patterns to make decisions or predictions.
Within machine learning, there are several different techniques:
Supervised learning: the algorithm learns from labeled data to predict output values.
Unsupervised learning: there are no labels for the training data. A machine learning algorithm tries to learn from underlying patterns or distributions that govern the data.
Reinforcement learning: the algorithms figures out which actions to take in a situation to maximize a reward.
From Udacity - AWS Machine Learning FoundationsHow I am navigating into it?
Now, I am a scholar at Udacity AI&ML Nanodegree Programming, which has been providing me with a structured environment to learn and take part in challenging projects. In my approach to making the most of the learning journey, this is my approach:
1 Sponge Mode
First, I immerse myself in a sponge mode by soaking as much theory and knowledge as possible to give myself a strong foundation. To begin with, I have learned Python fundamentals. My goal here was to understand Python enough to deal with libraries and find the right resources to debug my code.
A second course I am following for Sponge Mode is a famous course taught by Andrew Ng on Machine Learning. At the same time, I am following the AWS Machine Learning Foundations course.
In this step, my metrics of success are mainly:
a) To pay attention to the big picture and always ask questions.
When I am introduced to a new concept, I as myself "why", how this is used in the real world?
b) Take notes enough to understand the big picture and try to accept that I will not remember everything.
After sponge mode, I try to put the theory into code by building projects. I practice using Kaggle projects and Kackerank challenges.
2 My Learning style
I learn best by struggling through something on my own at my own pace and rereading the same thing over and over again until I understand it. In school, I fell in love with reading and the majority of my knowledge came from textbooks. I realized that I learn best a theoretical concept when I can teach it later. To be able to teach, it is important to take good notes so that I can review the material at my own pace. The tools I use are Notion, Obsidian, and Jupyter Notebook.
Although my primary method of obtaining knowledge was through books, I admit that my learning of data science concepts today involves videos and YouTube tutorials. For example, I prefer watching short videos from different sources about the same topic to look at things from a different angle. One of the most important keys to accelerating learning is to find a suitable medium that makes sense to you, this could be reading a blog post, watching a video, or listening to a podcast.
A few podcasts I am listening to be updated with the technology are:
- Lex Fridman Podcast
- TWiWL & AI
- Gradient Dissent
- The Robot Brains Podcast
- Ken's Nearest Neighbors
3 Taking some business course
As much as I am learning on the technical side, I try to differentiate myself as a data scientist so that I can promote my work by speaking to different people. The core skills where I can differentiate myself is due to my communication. So, I try to improve to become a better writer, storyteller, and negotiation.
4 Talking about what you are working on (A LOT)
After listening to a lot of advice from people in the industry, I understood a job to put my work in front of people who need to see it. It is a tough shift for people - like me - that tooting your own horn makes you braggy. I find it valuable when I look backward and keep being motivated while moving forward. Here are some takeaways to put yourself out there:
Solving a problem/completing a project, then writing a blog post about how you did it.
Committing to writing social media posts 3x week about the progress of your current project.
Joining communities to share what you are working on and ask for feedback on your projects or portfolio.
Conclusion
As I continue to grow, I am very excited to learn as much as I can about this end-to-end technology and by building projects. Completing technical skills with business skills is valuable to differentiate me. Along the road, I will show my work and share my journey with others.
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