Machine Learning

  UNIT-1 The Machine Learning Landscape Short Answer Questions -------------------------------------------------------------------------------------------------------   1.      Define Machine Learning. 2.      Define Unsupervised Learning. 3.      Explain Supervised Learning Algorithm. 4.      List the different Machine Learning Challenges. 5.      What is the purpose of the Machine Learning. 6.      List the different Machine Learning Applications. 7.      Define Reinforcement Learning. 8.      Define batch learning 9.      Define online learning 10.   List the different machine learning systems 11.   Machine learning types. 12.   List the supervised learning algorithms 13.   List the unsupervised learning algorithms 14.   What is overfitting 15.   What is underfitting 16.   What is poor quality data 17.   What is irrelevant feature 18.   Insufficient quantity of training data 19.   Non representative training data 20.   Testing 21.  

  Types of Machine Learning Systems There are so many different types of Machine Learning systems that it is useful to classify them in broad categories, based on the following criteria: •         Whether or not they are trained with human supervision a.       supervised, b.      unsupervised, c.       semi supervised, and d.      Reinforcement Learning •         Whether or not they can learn incrementally on the fly a.       online learning b.      batch learning •         Whether they work by simply comparing new data points to known data points , or instead by detecting patterns in the training data and building a predictive model , much like scientists do a.       instance-based b.      model-based learning These criteria are not exclusive; you can combine them in any way you like. ·        For example, a state-of-the-art spam filter may learn on the fly using a deep neural network model trained using examples of spam and ham; ·        this makes i