Machine Learning 1 : UNIT 1(B) PPTs: The Machine Learning Landscape PPTs

 

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

 

Machine Learning 1: UNIT-1(B) NOTES: The Machine Learning Landscape NOTEs

                                                                                     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 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