MACHINE LEARNING COURSE

Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks. A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers; but not all machine learning is statistical learning. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. Some implementations of machine learning use data and neural networks in a way that mimics the working of a biological brain. In its application across business problems, machine learning is also referred to as predictive analytics.

Syllabus of this course :)

  • 1. Overview
  • 2. History and relationships to other fields
  • 3. Theory
  • 4. Approaches
  • 5. Applications
  • 6. Limitations
  • 7. Model assessments
  • 8. Ethics
  • 9. Hardware
  • 10. Software
  • 11. Journals
  • 12. Conferences
  • 13. See also
  • 14. References
  • 15. Further reading
  • 16. External links







  • For more to know about this course :)