Machine Learning

Issued by: Stanford University on Coursera
Instructors: Andrew Ng
Coursework Completed: 2021
Certificate Issued: June 15, 2024

Course Overview:
This renowned Machine Learning course, taught by Andrew Ng of Stanford University, is one of the most influential MOOCs in the field. It provides a comprehensive introduction to machine learning, data mining, and statistical pattern recognition. The course emphasizes both the theoretical foundations and practical implementation of core machine learning algorithms, with all programming assignments completed in Octave/MATLAB—building algorithms from scratch to deepen understanding.

I completed all coursework and assignments in 2021, gaining hands-on experience with the material at that time. However, I only obtained the official certificate in 2024.

Key Skills Acquired:

• Core Algorithms: Linear Regression, Logistic Regression, Neural Networks, Support Vector Machines (SVMs)
• Unsupervised Learning: K-Means Clustering, Principal Component Analysis (PCA), Anomaly Detection
• Practical Applications: Recommender Systems (Collaborative Filtering), Photo OCR
• ML Best Practices: Bias/Variance Tradeoff, Regularization, Evaluation Metrics (e.g., F1 score), Learning Curves, Error Analysis
• Programming & Math: Implemented algorithms in Octave/MATLAB; applied linear algebra and calculus concepts essential for ML

Completing this course provided me with a strong theoretical and practical foundation in machine learning, including the mathematical principles behind key algorithms and the experience of implementing them from the ground up. This knowledge has been instrumental in my ongoing work and understanding of AI systems.

You can view the certificate here.