Machine Learning

  Unit I (B) The Machine Learning Landscape 1.      Main Challenges of Machine Learning 2.      Insufficient Quantity of Training Data 3.      Nonrepresentative Training Data 4.      Poor-Quality Data 5.      Irrelevant Features 6.      Overfitting the Training Data 7.      Underfitting the Training Data 8.      Stepping Back 9.      Testing and Validating  

                                                                                      Unit I (B) The Machine Learning Landscape 1.       7.      Main Challenges of Machine Learning 8.      Insufficient Quantity of Training Data 9.      Nonrepresentative Training Data 10.   Poor-Quality Data 11.   Irrelevant Features 12.   Overfitting the Training Data 13.   Underfitting the Training Data 14.   Stepping Back 15.   Testing and Validating  

  Main Challenges of Machine Learning Main task is to select a learning algorithm and train it on some data,  the two things that can go wrong are “bad algorithm” and “bad data.”  Insufficient Quantity of Training Data Non-Representative Training-Data Poor-Quality Data  Irrelevant Features Overfitting The Training Data Under Fitting of Training Data YouTube Link:  https://www.youtube.com/watch?v=7qLek-ZV7J4