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Data Science

11293 learners

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.

Numpy

Pandas

Python

Scikit-learn

See path

8 lessons

37 practices

5 hours

Machine Learning and Predictive Modeling

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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