Machine learning is the field of study that allows computers to learn without being explicitly programmed. The document discusses several types of machine learning including supervised learning techniques like classification and regression algorithms, unsupervised learning techniques like clustering, and reinforcement learning. It provides examples of applications for each type of machine learning such as spam filtering, loan approval, tumor prediction, and product recommendations.
3. Artificial Intelligence
• Definition
It refers to the simulation of human
intelligence in machines that are programmed to
think like humans and mimic their actions.
• “The study of the modeling of human mental
functions by computer programs.”—Collins
Dictionary
5. Machine Learning
Introduction
Machine learning is the field of study that gives computers the ability to learn without
being explicitly programmed.
In simple term, Machine Learning means making prediction based on data
9. UNSUPERVISED LEARNING
in which models are not supervised using training dataset.
models itself find the hidden patterns and insights from the given data.
“Unsupervised learning is a type of machine learning in which models are trained
using unlabeled dataset and are allowed to act on that data without any supervision
10. Reinforcement Learning
the agent learns automatically using feedbacks without any labeled data
The agent interacts with the environment by performing some actions, and
based on those actions, the state of the agent gets changed, and it also receives a
reward or penalty as feedback.
Agent learns and explores the environment.
he agent learns that what actions lead to positive feedback or rewards and what
actions lead to negative feedback penalty.
USECASES
• Robot Navigation
• Game Playing
• Automobile Manufacturing company
12. CLASSIFICATION
1.KNN ALGORITHM
K-NN algorithm stores all the available
data and classifies a new data point based
on the similarity.
USECASES:
SPAM Filtering
Banking system: Loan approval
13. 2.Logistic Regression
Logistic regression predicts the output of a categorical dependent variable.
It can be either Yes or No, 0 or 1, true or False, etc. but instead of giving the exact
value as 0 and 1, it gives the probabilistic values which lie between 0 and 1.
In Logistic regression, we fit an "S" shaped logistic function, which predicts two
maximum values (0 or 1).
The value of the logistic regression must be between 0 and 1, which cannot go
beyond this limit, so it forms a curve like the "S" form. The S-form curve is called
the Sigmoid function or the logistic function.
USECASES
Tumour Prediction
Credit card Fraud
14. Regression and Approaches
Regression analysis is a statistical method to model the relationship
between a dependent (target) and independent (predictor) variables with
one or more independent variables.
it predicts continuous/real values such as temperature, age, salary,
price, etc.
1.Linear Regression
shows the linear relationship between the independent variable (X-axis)
and the dependent variable (Y-axis).
USECASES
• Salary forecasting
• Real Estate Predictors
15. Unsupervised learning (K-Means Clustering
Algorithm)
• groups the unlabeled dataset into different clusters..
• centroid-based algorithm, where each cluster is associated with a
centroid.
• The main aim of this algorithm is to minimize the sum of distances
between the data point and their corresponding clusters.
• The algorithm takes the unlabeled dataset as input, divides the dataset
into k-number of clusters, and repeats the process until it does not find
the best clusters.
• USECASES
• Amazon and Netflix Recommendations
16. Association
How does Association Rule Learning work?
Association rule learning works on the concept of If and Else Statement,
such as if A then B.
Usecases:
Medical Diagnosis: With the help of association rules, patients
can be cured easily, as it helps in identifying the probability of illness for a
particular disease
