Intro to Unsupervised Machine Learning
This advanced course explores unsupervised machine learning, emphasizing dimensionality reduction and clustering methods. Using the Iris dataset, you will apply different methods and interpret the practical implications of the clusters identified.
Lessons and practices
Lesson 1: Exploring and Visualizing the Iris Dataset
Analyzing the Iris Dataset
Exploring and Visualizing the Iris Dataset with Preprocessing and Pair Plot
Focusing on Specific Features in Violin Plots
Standardizing the Dataset with Missing Values
Creating Box Plot for Iris Dataset Features
Handling Missing Values and Standardization in Iris Dataset
Lesson 2: Unraveling the Knots of K-means Clustering
Visualizing Optimal Number of Clusters for Iris Dataset
Adjusting the Number of Clusters in K-means Clustering
Identifying Optimal Number of Clusters in K-means Clustering
Determining Optimal Number of Clusters with the Elbow Method
Determining Optimal Number of Clusters in K-means Clustering from Scratch
Lesson 3: Unsupervised Learning: Hands-on with K-means Clustering
Applying K-Means Clustering to Iris DataSet and Evaluating Its Quality
Change Features for K-Means Clustering
Debug the Iris Cluster Beamer
KMeans Cluster Debugging Mission on Iris Dataset
Implement Visualization for KMeans Clustering
Final Challenge: Implement and Evaluate KMeans Clustering from Scratch
Lesson 4: Exploring and Implementing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm
Visualizing DBSCAN Clustering on Iris Petal Measurements
Fine-tuning DBSCAN Parameters for Iris Dataset Clustering
Debugging DBSCAN Clustering of Iris Dataset
Identifying Noisy Points in DBSCAN Clusters
DBSCAN Clustering and Visualization of Iris Dataset from Scratch
Lesson 5: Introduction to Principal Component Analysis and Dimensionality Reduction
Visualizing Iris Dataset with PCA
Color Coding the Iris Classes Using PCA
Visualizing Principal Component Analysis Results for the Iris Dataset
Applying Principal Component Analysis on Standardized Iris Data
Visualizing PCA Transformed Iris Dataset from Scratch
Lesson 6: Unveiling Independent Component Analysis: Theory, Implementation, and Insights
Visualizing Iris Dataset using Independent Component Analysis (ICA)
Exploring the Independent Components Further in ICA
Debugging Independent Component Analysis Visualization
Implementing Independent Component Analysis on the Iris dataset
Applying Independent Component Analysis on the Iris Dataset
Lesson 7: Unveiling High-Dimensional Data: An Introduction to t-Distributed Stochastic Neighbor Embedding (t-SNE)
Visualizing Iris Dataset with t-SNE
Adjusting t-SNE Perplexity to Emphasize Local Structure
Fixing t-SNE Visualization for Iris Data Analysis
Adding t-SNE Transformation to Visualize Iris Dataset
Implement t-SNE on Iris Dataset and Visualize the Results
Lesson 8: Understanding and Comparing Clustering and Dimension Reduction Techniques
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