machine learning syllabus

Machine learning syllabus

Machine learning syllabus pdf: In this article we will share with you the syllabus for the machine learning for the aspirants. Here, we have covered the machine learning syllabus by two most popular book Machine learning topics covered by Machine Learning For Absolute Beginners book and Machine Learning by Peter Flach book. 

Machine learning syllabus 2020

Candidates can also refer to these books to study machine learning.

Machine learning syllabus by
Machine Learning For Absolute Beginners book

Machine learning syllabus: 

  • What is machine learning?
  • Machine learning categories
  • The machine learning toolbox
  • Data scrubbing
  • Setting up your data
  • Regression analysis
  • Clustering
  • Bias and variance
  • Support vector machines
  • Artificial neural networks
  • Decision trees
  • Ensemble modelling
  • Development environment
  • Building a model in python
  • Model optimization
  • Next steps
  • Further resources
  • Downloading datasets
  • Appendix: Introduction to python

Topic covered by
Machine Learning by Peter Flach

  • Preface 
  • Prologue: A machine learning sampler 
  • The ingredients of machine learning 
  • Binary classification and related tasks 
  • Beyond binary classification 
  • Concept learning 
  • Tree models 
  • Rule models 
  • Linear models 
  • Distance-based models 
  • Probabilistic models 
  • Features
  • Model ensembles
  • Machine learning experiments 
  • Epilogue: Where to go from here 
  • Important points to remember 
  • References

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