MLSpecialization

Coursera's Machine Learning Specialization by Stanford University. Consists of:

• Supervised Machine Learning
• Advanced Learning Algorithms
• Unsupervised Learning

About

I took this specialization in the summer of 2022. Andrew Ng and a few other instructors teaches all kinds of different subjects in machine learning from supervised to unsupervised to reinforcement learning. I started off knowing little to nothing about machine learning and how implement ML consents. This course has really given me an understanding of ML at its core, along with practical ML applications. I would 100% recommend this specialization to anyone interested in getting started with ML.

Organization

• Folder: The course in the specialization
  • Folder: Week's folder named "Weekweek#"
    • Multiple Files: Notes for each group of videos
    • private Markdown File: Quizzes for each video group (graded)
    • private PDF: Lecture slides
    • private Folder: Week's Jupyter Notebooks names "Labs"
    • private optional Folder: Programming Assignment (graded)

Certificates

Final Certificate, specialization grade: 100%.

• Supervised Machine Learning: Regression and Classification
  • Certificate link
  • 100% on all Quizzes
  • Week 2 Linear Regression Lab: 100%
  • Week 3 Logistic Regression Lab: 100%
  • Final Grade: 100%
• Advanced Learning Algorithms
  • Certificate link
  • 100% on all Quizzes
  • Week 1 Binary Classification Lab: 100%
  • Week 2 Multiclass Classification Lab: 100%
  • Week 3 "Applying Machine Learning" Lab: 100%
  • Week 4 Decision Tree Lab: 100%
  • Final Grade: 100%
• Unsupervised Learning, Recommenders, Reinforcement Learning
  • Certificate link
  • 100% on all Quizzes
  • Week 1 Assignment 1 K-Means Lab: 100%
  • Week 1 Assignment 2 Anomaly Detection Lab: 100%
  • Week 2 Assignment 1 Collaborative Recommender Systems Lab: 100%
  • Week 2 Assignment 2 Content Based Filtering Lab: 100%
  • Week 3 Lunar Lander Lab: 100%
  • Final Grade: 100%