In early August 2025, I successfully completed Coursera's Machine Learning Specialization course presented by world-renowned ML and AI expert, Adjunct Professor Andrew Ng of Stanford University, Chairman and co-founder of Coursera and founder of DeepLearning.AI.

I learned the following key skills, concepts and techniques on the course;

• How to build, train, evaluate, optimize both supervised linear and logistic regression ML models in Python using NumPy, Panda and scikit-learn modules in prediction and binary classification tasks.
• How to apply best industry practices for ML development and use state-of-the-art machine learning techniques for developing unsupervised ML models featuring clustering and anomaly detection solutions.
• How to build, train, evaluate and optimize the performance of deep-learning neural networks with TensorFlow to perform multi-class classification tasks and how to use decision trees and tree ensembles to build ML models for structured tabular input data.
• How to build recommender systems with a collaborative filtering approach, content-based deep learning systems and deep reinforcement learning models.

The Machine Learning Specialization course presents material in the following three machine learning topics;

• Supervised Machine Learning : Regression and Classification - Grades and Certificate
• Advanced Learning Algorithms - Grades and Certificate
• Unsupervised Machine Learning, Recommender Systems and Reinforcement Learning - Grades and Certificate

with each topic of the course having a series of graded and ungraded exercises, quizzes and programming assignments designed to test my understanding of the concepts and my ability to implement machine learning algorithms in the Python programming language, using industry-standard ML modules. I achieved 100% in all graded exercises and programming assignments in the material of each of the above topics as can be verified by clicking on the "Grades" links and awarded "Certificate" links.

My final Machine Learning Specialization course certificate can be viewed by clicking here.