During COVID Lockdown, I undertook a few courses and projects on Coursera. While I was familiar with K-Means Clustering and PCA, I had lots to learn from this Guided Project. If you want to explore PCA more, this blog by Machine Learning Mastery was very helpful for me.
- Understand how to leverage the power of machine learning to transform marketing departments and perform customer segmentation
- Apply Python libraries to import and visualize dataset images.
- Understand the theory and intuition behind k-means clustering machine learning algorithm
- Learn how to obtain the optimal number of clusters using the elbow method
- Apply Scikit-Learn library to find the optimal number of clusters using elbow method
- Apply k-means in Scikit-Learn to perform customer segmentation
- Understand the theory and intuition behind Principal Component Analysis (PCA) algorithm
- Apply Principal Component Analysis (PCA) technique to perform dimensionality reduction and data visualization
- Compile and fit unsupervised machine learning models such as PCA and K-Means to training data
My major learning was all the awesome visualisations for the results and EDA associated with the task.