Phd_ML

Welcome to my codenotes for machine learning!

This repository is dedicated to my trajectory in the area of Classical Machine Learning.

I like studyng with online couse, i think this hel a lote whend you need a diretion in you study or whend you is a begining I like studying online, I think it helps a lot when you need guidance in your studies or when you’re starting out. These are the main sources I used:

• #1 - Starting from data analysis.

  This course covers everything from the fundamentals of data analysis with Python to the creation and evaluation of data models.

  Detailed details include:

  • data collection and import
  • cleaning, preparing and formatting data
  • manipulation of data frames
  • data summary
  • creating machine learning regression models
  • model refinement
  • creating data pipelines.

  Link: IBM course

• #2 - Going through a basic and general study of ML .

  This course will begin with a brief introduction to Machine Learning and what it is, with topics such as supervised and unsupervised learning, linear and nonlinear regression, simple regression, and more.

  You will then dive into classification techniques using different classification algorithms such as K-Nearest Neighbors (KNN), decision trees, and logistic regression. You will also learn about the importance and different types of clustering such as k-means, hierarchical clustering and DBSCAN.

  With all the many concepts you will learn, great emphasis will be placed on hands-on learning. You will work with Python libraries, such as SciPy and scikit-learn, and apply your knowledge in laboratories.

  Link: IBM course

• #3 - And finally, delving deeper into neural networks through a “Lecture Series” given by Prof. Dr. Florian Marquardt.

  Link :https://pad.gwdg.de/s/Machine_Learning_For_Physicists_2021