Intro. to Data Science and Machine Learning
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Introduction to Data
Data science is a field of study that focuses on techniques and algorithms to extract knowledge from data. The area combines data mining and machine learning with data-specific domains. This section focuses on defining "data" before going to any complicated topic.
Understanding the Data Space
Objects and features of a data table
Video - 00:7:19 mins
Quiz on Objects and features
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What are "space" and a "high-dimensional space"?
Video - 00:7:13 mins
What is a dimension?
Reading
Quiz on Data space
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What is a vector in Machine Learning and Data Science?
Video - 00:4:16 mins
Quiz on Vectors!
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Proximity in Machine Learning and Data Science Context
Many machine learning algorithms rely on distance or similarity between objects/data points. Video lectures in this section focus on standard proximity measures used in machine learning and data science. The section also explains how to use proximity measures to examine the neighborhood of a given point.
Notions of proximity: distance and similarity
Video - 00:6:24 mins
Euclidean and Manhattan Distances
Video - 00:9:21 mins
String distance measures: Edit and Hamming distances
Video - 00:13:02 mins
Jaccard Similarity or Index or Coefficient
Video - 00:12:49 mins
Cosine Similarity Between Two Vectors
Video - 00:11:12 mins
Tanimoto Similarity between Two Vectors
Video - 00:5:16 mins
k-nearest neighbors
Video - 00:5:51 mins
k-nearest neighbors using your own distance functions
Video - 00:6:18 mins
Clustering Algorithms
A significant aspect of data science involves exploratory analysis, where scientists and practitioners apply statistical techniques to gain insights into data. One approach to exploration is identifying clusters—groups of data points with similar characteristics. This section provides an overview of clustering algorithms.
Introduction to Clustering Algorithms
Video - 00:25:36 mins
Quiz on Intro to Clustering
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k-means clustering algorithm
Video - 00:48:19 mins
Quiz on k-means
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Hierarchical Agglomerative Clustering
Reading
Quiz on Hierarchical Clustering Algorithm
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Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
Reading
Quiz on DBSCAN
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Quiz 2 on DBSCAN
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Clustering Evaluation
Video - 00:4:33 mins
Quiz on Clustering Evaluation Intro.
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True Positive, False Positive, True Negative, and False Negative
Video - 00:3:36 mins
Quiz on true positive, false positive, true negative, and false negative
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Supervised Evaluation of Clustering
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Rand Index for Clustering Evaluation Part 1
Video - 00:5:54 mins
Rand Index for Clustering Evaluation Part 2
Video - 00:6:32 mins
Purity for Clustering Evaluation
Video - 00:12:14 mins
Unsupervised Evaluation of Clustering
Reading
Sum of Squared Distance from Centroids
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Average Silhouette Coefficient (ASC)
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Other Methods for Clustering Evaluation
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Outlier detection
Linear Regression-based Prediction
Classification
PageRank
A Brief History of the PageRank Algorithm
Video - 00:3:55 mins
Creating the Transition Matrix for the Internet: How Google Search Works
Video - 00:12:18 mins
Iterating Over the PageRank Vector to Rank Web Pages: How Google Search Works
Video - 00:13:06 mins
Pseudocode, Spider Trap, and How Google Search Engine Works
Video - 00:11:25 mins
Random Surfer Model and Spider Trap: How Google Search Works with Spider Traps
Video - 00:9:05 mins
Teleportation Details with Pseudocode: How Google Solves the Spider Trap Issue
Video - 00:12:11 mins
Implementing PageRank Algorithm in Python | Handling Spider Traps and Teleportation
Video - 00:8:38 mins
PageRank: Exam/Test Preparation 1
Video - 00:2:21 mins
PageRank: Exam/Test Preparation 2
Video - 00:3:21 mins
Mathematical Optimization
Mathematical Optimization – The Hidden Engine Behind Data Science
Video - 00:3:24 mins
The Core of Mathematical Optimization
Video - 00:9:27 mins
Quiz on Core of Optimization
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How Is Mathematical Optimization Connected to Data Science?
Video - 00:8:12 mins
Local and Global Optima
Video - 00:6:43 mins
Quiz on Local vs. Global Minima
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Essential Objective Function Properties for Optimization
Video - 00:12:33 mins
Quiz on Conditions for Optimization
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Common Optimization Methods and their Dependence on Mathematical Properties
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