🟢 Restarted C++ (Algorithms)
🟢 Made videos on how to connect VS Code with GitHub
🟢 Did debugging on previous code
🟢 Working on an Excel Project
🟢 Completed an Excel Course on Udemy
🟢 Solved theoretical mechanics problems
🟢 Learned C++ (Functions)
🟢 Learned some philosophy today. Sometimes people just don't think so much about things and are happy. But sometimes people think about thinks like- the meaning of life, who am i, etc and feel unhappy. Still choosing 1 which gives you relaxation is the best.
🟢 Took ideas of machine learning, what is it, how it works, why we need it, etc
🟢 Learned c++ functions more
🟢 Started learning AI Engineering
🟢 AI engineer roadmap https://roadmap.sh/ai-engineer
🟢 I'm using "https://roadmap.sh/ai-engineer" this to record my ai engineering progress
🟢 Just read an article "https://www.simplilearn.com/tutorials/artificial-intelligence-tutorial/how-to-become-an-ai-engineer". In this article I learned what we need to learn to become an AI Engineer.
🟢 Solved some PDE (partial differentail equation) problems.
🟢 Participated in a chess tournament (ranked 7th place) and also practiced some ideas of c++
🟢 Just read an article "https://www.techtarget.com/whatis/feature/How-to-become-an-artificial-intelligence-engineer". In this article I learned what we need to learn to become an AI Engineer.
🟢 Started using jupyter notebook and vscode together.
🟢 Delivered a presentation on Philosophy (What is philosophy, why it is needed, how this is connected with other specialities and etc were the topics I explained.)
🟢 Studied an ai engineering article
🟢 Studied an ai engineering article (a new one)
🟢 Practiced c++ functions
🟢 Started Learning Ai Engineering. Currently learning from https://app.datacamp.com/learn/?utm_source=customerio&utm_medium=email&utm_campaign=240719_1-welcome_2-mix_3-all_4-na_5-na_6-na_7-le_8-emal-ci_9-na_10-bau_11-email&utm_content=auto. (Update: it has 13 courses to complete the whole course, but only the first one is free. that's why not learning from here anymore)
Read an article on ai engineer "https://roadmap.sh/ai/guide/what-is-ai-engineering-1770816097584"
Took idea of how a ai engineer is hired from here https://resources.workable.com/ai-engineer-job-description
🟢 Learned AI in Product Development: Netflix, BMW, and PepsiCo from https://www.virtasant.com/ai-today/ai-in-product-development-netflix-bmw#:~:text=AI%20can%20help%20make%20product,and%20gain%20a%20competitive%20edge.
🟢 Learned ai engineer vs ml engineer from https://www.coursera.org/articles/what-is-machine-learning-engineer & https://www.codecademy.com/resources/blog/what-does-an-ai-engineer-do
🟢 Learned about AGI from https://aws.amazon.com/what-is/artificial-general-intelligence/. Just read an article on ai vs agi from https://www.forbes.com/sites/bernardmarr/2024/05/20/the-crucial-difference-between-ai-and-agi/ but remember agi (artificial general intellegence) is still a long way to go. it's not invented yet.
🟢 Learned about LLM from https://www.cloudflare.com/en-gb/learning/ai/what-is-large-language-model/
🟢 AI & LLM explanation from here https://www.youtube.com/watch?v=osKyvYJ3PRM
🟢 Learned about inference from https://www.cloudflare.com/learning/ai/inference-vs-training/
🟢 Started doing a task "https://www.kaggle.com/datasets/uciml/breast-cancer-wisconsin-data?resource=download"
🟢 Did data cleaning (removed unwanted columns), changed category M & B to 1 & 2 because the ML model needs numeric input
🟢 Did Feature Scaling in the model (its to ensure all variables contribute equally to the model, also because difference between the max variables is too high)
🟢 Learning Training Logistic Regression
🟢 Did logistic Regression on the project (Breast Cancer ML)
🟢 Learned and used Linear Logistic Regression in the model
🟢 Went to lab on 4th floor and showed the project to the lab professor and othersde
🟢 Saw the needed imports for ml (like- numpy, pandas, matplotlib, seaborn, models, etc)
🟢 Started working on "Parkinson's Telemonitoring" project
🟢 Imported the csv file, scalled it, and trained linar Regression.ad
🟢 Learned about F1 score
🟢 Trying to Understand Precision and (vs) Recall
🟢 Learned Cross-validation
🟢 Learned Precision and Recall through project (practical).
🟢 Today I tried to Understand why ROC-AUC is useful
🟢 Did some work on "Parkinson's Telemonitoring" project
🟢 Trained new model on "Parkinson's Telemonitoring" project
🟢 Started learning from the new bootcamp
🟢 Started Working on my mini project (Daily Fuel application)
🟢 Launched "Daily Fuel" app on playstore (currently added testers-14)
🟢 Solved partial differential equations problems. and applied in international youth event 2026(russia)
🟢 Learned ml pipelines a little more.
🟢 Learned some basics of Computer Vision.
🟢 Brainstormed new ideas of projects and tried writing the abstract for lund3d ai project
🟢 Reviewed some computer vision projects (in lab-4th floor)
🟢 Did partial differential equations for fluid dynamics (it was not interesting-haha)
🟢 Read some article on Artificial General Intelligence
🟢 Learned C# basics to work on unity (to make games)
🟢 Started working on a new personal ai voice agent app (didn't create a repo for it yet)
🟢 Ai Voice agent app (preparation done)
🟢 Did some work on ai voice agent app (didn't create the repo yet)
🟢 Did some work on ai voice agent app today too (didn't create the repo yet)
🟢 Did some work on ai voice agent app today too (trying to fix android debuging to test on phone) (didn't create the repo yet)
🟢 Started learning c++ algorithms again for exam😅
🟢 Preparing for the Object-Oriented C++ Programming course
🟢 Working on another project (from lab)
🟢 insertionSort, quickSort, mergeSort, heapSort (revised these sorting algorithms for c++ exam)
🟢 Preparing for the startup competition (with nurdaulet agai (4th floor lab)
🟢 Started Internship project (academic internship)
🟢 Worked on internship project
🟢 Worked on internship project today as well
Used for numerical computation, array operations, and mathematical foundations of machine learning.
Needed to write clean code, analyze data, and prepare real-world datasets.
Used to visualize data, trends, and model behavior.
Helps create statistical visualizations for better data understanding.
Used for prediction tasks with labeled data.
Used to discover hidden patterns in unlabeled data.
Planned work on neural network architectures and training methods before the end of 2026.