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CodeSignal

Intro to Unsupervised Machine Learning

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Overview

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.

Syllabus

  • Unit 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
  • Unit 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
  • Unit 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
  • Unit 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
  • Unit 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
  • Unit 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
  • Unit 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
  • Unit 8: Understanding and Comparing Clustering and Dimension Reduction Techniques

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