What I learned
I learned K-Means, Gaussian anomaly detection, PCA, collaborative filtering, neural content-based recommenders, state-action value functions, and deep Q-learning. The projects turned abstract ideas into visual and measurable systems.
Course · Machine Learning Specialization · Course 3
Unsupervised Learning, Recommenders, Reinforcement Learning
This was the most milestone-rich part of the Machine Learning Specialization for my page because it opened three important directions: discovering structure without labels, building recommender systems, and learning from reward feedback.
ClusteringAnomaly detectionPCARecommendersReinforcement learning
Why it mattered in my path
This course expanded my ML thinking beyond classification accuracy: recommendation, hidden structure, dimensionality reduction, and sequential decision making became part of my toolkit.
Expanded learning page
What this course added to my engineering stack.
Unsupervised Learning, Recommenders, Reinforcement Learning was not just a certificate item. I used it as one layer in a longer path: understand the concept, implement it in code, test it on assignments or notebooks, then connect the idea to future portfolio systems.
Why I studied it
This was the most milestone-rich part of the Machine Learning Specialization for my page because it opened three important directions: discovering structure without labels, building recommender systems, and learning from reward feedback. The reason it matters on this page is that it shows the exact stage where my learning moved forward, instead of presenting education as a flat list of names.
What I focused on
The most important layer was not memorizing definitions; it was learning how the course concepts behave when they are turned into working code, trained models, evaluation outputs, and notebooks.
clusteringPCAanomaly detectionrecommender systemsvalue functionsdeep reinforcement learning
How I used it
I treated the course as a practical loop: watch the theory, re-implement the assignment logic, inspect outputs, record results, and then keep the code in a public repository as evidence of the learning process.
How it connects
This course expanded my ML thinking beyond classification accuracy: recommendation, hidden structure, dimensionality reduction, and sequential decision making became part of my toolkit. This page also connects to standalone milestone pages, so the course is not represented only by certificate text; it is represented through the labs, models, notebooks, results, and media produced during the learning path.
Theorymathematical or conceptual model
Implementationnotebooks, functions, model code
Evidencecertificate, repo, outputs, milestones
Engineering useapplied later in apps and research
Learning notes
I learned K-Means, Gaussian anomaly detection, PCA, collaborative filtering, neural content-based recommenders, state-action value functions, and deep Q-learning. The projects turned abstract ideas into visual and measurable systems. I also used the course to improve how I explain technical decisions: why a model is chosen, what assumptions it makes, where it fails, and what the next improvement should be. That explanation layer is important because my goal is end-to-end AI engineering, not only passing assignments.
Evidence and navigation
- Official course page is linked for source context.
- GitHub repository is linked where I have public code.
- Certificate or badge appears in the showcase when available.
- Milestone cards open separate pages when the course has larger labs or projects.
Connected milestone pages
Unsupervised Learning, Recommenders, Reinforcement Learning
Anomaly Detection
A Gaussian anomaly detection system for server monitoring, using throughput and latency to identify abnormal machines by probability thresholding.
Open milestone
Unsupervised Learning, Recommenders, Reinforcement Learning
Collaborative Filtering Recommender System
A movie recommender that learns latent movie and user vectors from a rating matrix, then predicts personalized preferences.
Open milestone
Unsupervised Learning, Recommenders, Reinforcement Learning
Deep Learning for Content-Based Filtering
A two-tower neural recommender that combines user features and movie features into learned embeddings, then predicts ratings and similar items.
Open milestone
Unsupervised Learning, Recommenders, Reinforcement Learning
K-Means Clustering and Image Compression
A clustering milestone that implements K-Means from scratch, then uses it to compress an RGB image by replacing thousands of colors with centroid colors.
Open milestone
Unsupervised Learning, Recommenders, Reinforcement Learning
PCA Exploratory Data Analysis
A dimensionality-reduction milestone using PCA to reveal hidden structure that is not visible from raw pairwise feature plots.
Open milestone
Unsupervised Learning, Recommenders, Reinforcement Learning
Deep Q-Learning Lunar Lander
A Deep Q-Learning agent trained to land a LunarLander safely using rewards, a Q-network, a target network, and experience replay.
Open milestone
Unsupervised Learning, Recommenders, Reinforcement Learning
State-Action Value Function Explorer
A compact Mars-rover-style value-function example for understanding how rewards, discount factor, and missteps change action preference.
Open milestone