5. "Field of study that gives computers the
ability to learn without being explicitly
Programmed"
-Arthur Samuel(1956)
6. General Terms
Data :- Data is information such as facts and numbers used to analyze
something or make decisions. It is driving force of ML.
Mostly in the form of :-
1. Text
2. Audio
3. Wave
4. Numbers
5. Image/values of pixel
7. General Terms
Dataset :- Data is stored in datasets.
Characteristics of datasets :-
1. Large in size
2. Highly diverse
3. More number of features
Features(input) :- A feature is an input variable.
Labels(output/answers) :-A label is the thing we're predicting.
8. "Field of study that
gives computers the
ability to learn
without being
explicitly
Programmed"
-Arthur Samuel(1956)
General Terms
Model :- A model defines the relationship between features and label.
Characteristics of model :-
1. mathematical construct that processes input data and returns output
2. discovers patterns through training
e.g.
1. linear regression model consists of a set of Weight and Bias
2. Neural Networks consists of set of hidden layer with one or more
neurons
11. Supervised Learning
Learns from being given “right answers”
Supervised learning models can make predictions after seeing lots of data with
the correct answers and then discovering the connections between the elements
in the data that produce the correct answers.
X Y
Input(features) Output (labels)
12.
13. Supervised Learning
Regression
A regression model predicts a numeric
value
Regression Predict a number from infinitely
many possible outputs
Classification
Classification models predict the
likelihood that something belongs to a
category.
Classificaton predicts categories from of
small possible output
