Intro. to Data Science and Machine Learning

Categories: AI and Data Science
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About Course

This course provides a foundational introduction to the principles and techniques of data science and machine learning, guiding students from understanding the nature of data to applying essential algorithms. Key topics include concepts like features, high-dimensional data, mathematical spaces, vectors, proximity and distance measures, nearest-neighbor search, clustering, and classification. The course also includes transformative algorithms like PageRank, demonstrating how matrix operations revolutionized search engines. With a strong emphasis on core concepts, this course equips students to analyze data effectively and tackle real-world challenges.

Terms of Service related to the course.

What Will You Learn?

  • Data-related nomenclature
  • Proximity measures
  • Clustering algorithms
  • Classification algorithms
  • PageRank algorithm
  • Outlier detection

Course Content

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.

  • What is data?
    06:07
  • The simplest form of data
  • Can data speak?
  • Quiz on Intro to Data

Understanding the Data Space

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.

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.

Outlier detection

Linear Regression-based Prediction

Classification

PageRank

Mathematical Optimization

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