Portfolio
Excited for trying new Polywork features
Started my journey on CS224N at Stanford. So far the experience has been great. I’ve been learning a lot about fundamentals of NLP, one of the topics I find more fascinating in ML. The first part o...
I’ve happily started my journey into AI Program at Stanford. Highlights: - Lectures are dense and heavy in content but really good ones. So far it keeps you interested to continue watching and rese...
Currently reading and studying Markov Decision Processes. After seeing search problems as a deterministic way to solve problems, we know that the world is not so certain, and here it comes the stoc...
I've been crazy busy with my new course 😁 Currently I'm learning how you can define a search problem and use any search algorithm and it's like magic happens it will return the best path for the so...
Attended Hugging Face Transformers Workshop. Highlights: • It started describing what attention mechanisms are and how is the architecture • Showcased the concepts of Transformers, why and where ar...
Working on ML-Zoomcamp Week 06 - Decision Trees • Decision Trees (DTs) are used as a supervised method to do Regression and Classification. • DTs are based on the decisions taken based on features ...
Today I've worked on 2 main sides: Theory: - Took notes of Classification in general - Understanding of why in Logistic Regression there is no possibility to find easily the weights as with Normal ...
Started working on a Pet Project for Classification. Few highlights from the start: • EDA is important and cannot be dismissed as it will allow you to see what is going on with your data before bui...
Finished Chapter 04 - Classification on Statistical Learning course - ISLR Highlights: • When doing classification you can work through different algorithms/techniques to perform classification, su...
Working on ML Zoomcamp course Week 4. This week's content is about Evaluation metrics for Classification. Highlights: • Sometimes calculating the accuracy of the model is not enough as we might be ...
Just finished the last assignment of Week 4 - PCA :) This course was the hardest of the whole Mathematics for Machine Learning specialization but also the most useful of all 3 courses. I truly enjo...
Just finished last course of Mathematics for Machine Learning by Imperial College of London. Last course was related to Principal Component Analysis, with the main content as: 2. Measures of center...
Worked on the assignment for week 3 of Mathematics for ML Specialization by Imperial College of London. The assignment was fairly simple as you will apply the concepts to calculate projection matri...
Week 3 - Statistical Learning Course. Just started Linear Regression chapter, in which the basics of how linear regression works were explained. • Learned about regression parameters. • Residual su...
Weekend :-) Spent the morning in a coffee shop studying Orthogonal projections in 1D and 2D. Later in the day I moved to another coffee shop and started drafting a blog post on Bayes classifiers an...
Continue working on Statistical Learning - Week 2 Notes: • Intro to Nearest Neighbours and Linear Regression • Nearest Neighbours modeling is cool but it has few disadvantages, for example, if the ...
Week 2 - I've been working on the Machine Learning Intermediate Kaggle course as part of the 30 Days Of ML So far this week I have learned more about: • How to treat missing values and categorical ...
Started working on Statistical Learning - STATSX0001 course by Stanford ( edX ) Few notes: 2. The introduction is very friendly and starts explaining how we got from Statistics to Machine Learning ...
Day 9 - 30DaysOfML with Kaggle Completed Lesson 3 - First ML Model Few notes: • A quick explanation about building a model, from exploring the data, feature selection, fitting the data to the model...
Day 8 of 30DaysofML with Kaggle finished. • Basic definition of Decision Trees • Data exploration with Pandas. Now off to some relax and reading ☺️
Sunday's are for Algebra. Watched a lecture about Positive Definite Matrices from MIT Courseware Few notes: • Matrix has to be symmetric • All its eigenvalues are positive How to find out if a Matr...
Got myself a little something as Bday gift :) Introduction to Natural Language Processing by Jacob Eisenstein https://mitpress.mit.edu/books/introduction-natural-language-processing
Completed accumulated work from 30 days of ML at Kaggle. Day 3 to 5. Python course is pretty basic and you will fly through it if already have experience with the language.
Started working on the last course of Math for ML from Imperial College of London. Module 1 - Measures of Central Tendency • Refreshed positive definite matrices
Starting 30 days of ML with Kaggle
Joined Polywork, looking forward to share insights and learnings from Life, Books and ML :)