My name is Ahmad Hamad, my educational background is originally in mathematics, I earned my BA from the Lebanese University, then my master from University of Geneva. Most of my courses were theoretical; However, I was more interested in applied mathematics. Living in a world where Artificial intelligence and technologies are everywhere, I was urged to start my learning journey in this field in order to acquire new knowledge and skills, combine them with my scientific background, and integrate them in the professional world.
At that time, I had thousands of questions in my head without answers such as; How do I start? Where and when should I start? Which platform, and field, should I choose? Which course and skill are the most important to start with? etc. I felt lost until I found the Beirut AI community. After attending many workshops hosted by industrial experts, and knowing the differences between different subfields of AI, I was ready to start my learning journey. These workshops helped me to choose my master’s thesis project topic which is a combination of mathematics and statistical machine learning. Nevertheless, the problem was that I know what I need yet still need to know where to start and how much it will cost me to learn. Again, the Community Aid Program of Beirut AI, which I am very grateful for, was the solution. With no past knowledge, my motivation was the only thing that pushed me to apply for the program.
After getting accepted, I had free access to DataCamp resource: an online learning platform with more than 400 courses covering different fields and tools. Maybe someone will wonder which courses should be selected among 400 courses; however, the structure is very helpful: you have a career track like the data science track, data analyst track etc., where each track has specifically related courses. You can also choose the tool you want to use in these tracks; I have chosen Python rather than R as a programming language since I had some little experience with Python in my university. I started with the data analyst track. By the end of the track, I was able to manipulate and visualize different types of data using the most powerful Python libraries like Pandas, NumPy and Seaborn. I really enjoyed the learning strategies on this platform because it was entertaining and fruitful at the same time. After every new concept I learned, I applied it in an extensive exercise instantly. That led me to use all my knowledge acquired in order to solve a real world problem in project form. I decided to continue my learning by taking the data science track to learn new machine learning algorithms and to know which algorithm I should apply in each problem.