3. MACHINE LEARNING
Machine learning is a subfield of computer science that
explores the study and construction of algorithms that can
learn from and make predictions on data.
• Such algorithms operate by building a model from example
inputs in order to make data- driven predictions or
decisions, rather than following strictly static program
5. SUPERVISED LEARNING
Supervised learning is the task of inferring a function from
labeled training data. The training data consist of a set of
In supervised learning, each example is a pair consisting of
an input object and a desired output value. A supervised
learning algorithm analyzes the training data and produces
an inferred function.
7. SUPERVISED LEARNING PROCESS: TWO STEPS
Learning (training): Learn a model using the training data
Testing: Test the model using unseen test data to assess
the model accuracy
Accuracy= No. of correct classifications / Total no of test cases
8. SUPERVISED LEARNING
Supervised learning problems can be further grouped into
regression and classification problems.
• Classification: A classification problem is when the output
variable is a category, such as “red” or “blue” or “disease”
and “no disease”.
• Regression: A regression problem is when the output
variable is a real value, such as “dollars” or “weight”.
9. LIST OF COMMON SUPERVISED MACHINE LEARNING
• Decision Trees
• K Nearest Neighbors
• Linear SVC (Support vector Classifier)
• Logistic Regression
• Linear Regression
10. ADVANTAGES OF SUPERVISED LEARNING
It allows you to be very specific about the definition of the labels.
You are able to determine the number of classes you want to
The input data is very well known and is labeled.
The results produced by the supervised method are more
11. DISADVANTAGES OF SUPERVISED LEARNING
Supervised learning can be a complex method.
Supervised learning needed a lot of computation time for
If you have a dynamic big and growing data, you are not sure of
the labels to predefine the
12. UNSUPERVISED LEARNING
Unsupervised learning is where you only have input data (X)
and no corresponding output variables. The goal for
unsupervised learning is to model the underlying structure or
distribution in the data in order to learn more about the data.
• These are called unsupervised learning because unlike
supervised learning above there is no correct answers and
there is no teacher. Algorithms are left to their own devises to
discover and present the interesting structure in the data.
14. UNSUPERVISED LEARNING
Unsupervised learning problems can be further grouped into clustering
and association problems.
Clustering: A clustering problem is where you want to discover
the inherent groupings in the data, such as grouping
customers by purchasing behavior.
Association: An association rule learning problem is where you
want to discover rules that describe large portions of your
data, such as people that buy X also tend to buy Y
15. LIST OF COMMON SUPERVISED MACHINE LEARNING
• K-means clustering
• K-NN (k nearest neighbors)
• Dimensionality Reduction
• Hierarchical clustering
16. ADVANTAGES OF SUPERVISED LEARNING
Less complexity in comparison with supervised learning.
It is often easier to get unlabeled data.
Takes place in real time such that all the input data to be
analyzed and labeled in the presence of learners.
17. DISADVANTAGES OF SUPERVISED LEARNING
You cannot get very specific about the definition of the data
sorting and the output.
Less accuracy of the results.
The results of the analysis cannot be ascertained.