Machine learning, a subset of artificial intelligence, relies on data and algorithms to emulate human learning and improve accuracy over time. It includes various types such as supervised learning, which utilizes labeled datasets for classification and regression, unsupervised learning for clustering and pattern recognition, and reinforcement learning that employs trial and error for model improvement. The document further details algorithms for these techniques and contrasts traditional programming with machine learning workflows.

1. Machine Learning

2. Artificial Intelligence

3. Computer Vision Applications

4. Natural Language Processing Applications

5. Machine Learning
Machine learning is a branch of artificial
intelligence and computer science which focuses on
the use of data and algorithms to imitate the way
that humans learn, gradually improving its
accuracy.

6. Types of Machine Learning
Supervised
ML
Unsupervised
ML
Semi-
supervised
ML
Reinforcement
Learning

7. Supervised ML
Supervised machine learning also known
as supervised learning, is defined by its
use of labeled datasets to train algorithms
to classify data or predict outcomes
accurately.

8. Unsupervised ML
Unsupervised machine learning uses
machine learning algorithms to analyze
and cluster unlabeled datasets. These
algorithms discover hidden patterns or
data groupings without the need for
human intervention

9. Semi-supervised
learning:
Semi-supervised learning (SSL) is a
machine learning technique that uses a small
portion of labeled data and lots of unlabeled
data to train a predictive model.

10. Reinforcement Learning
Reinforcement machine learning is a
machine learning model that is similar to
supervised learning, but the algorithm
isn’t trained using sample data. This
model learns as it goes by using trial and
error. A sequence of successful outcomes
will be reinforced to develop the best
recommendation or policy for a given
problem.

11. Types of Supervised Learning
Classification
• The Classification algorithm is a Supervised
Learning technique that is used to identify the
category of new observations on the basis of
training data.
• In Classification, a program learns from the
given dataset or observations and then
classifies new observation into a number of
classes or groups.
• Such as, Yes or No, 0 or 1, Spam or Not
Spam, cat or dog, etc. Classes can be called
as targets/labels or categories.
Regression
• Regression analysis is a statistical method to
model the relationship between a dependent
(target) and independent (predictor) variables
with one or more independent variables.
• More specifically, Regression analysis helps
us to understand how the value of the
dependent variable is changing corresponding
to an independent variable when other
independent variables are held fixed.
• It predicts continuous/real values such
as temperature, age, salary, price, etc

12. Classification Algorithms:
• Logistic Regression
• Naive Bayes
• K-Nearest Neighbors
• Decision Tree
• Support Vector Machines
Regression Algorithms:
• Linear Regression
• Polynomial Regression
• Ridge Regression
• Lasso Regression
• SV Regression
• Decision tree for regression
• Random forest regression
• KNN regression

13. Unsupervised Learning Algorithms
• Clustering: Clustering is a method of grouping the
objects into clusters such that objects with most
similarities remains into a group and has less or no
similarities with the objects of another group. Cluster
analysis finds the commonalities between the data
objects and categorizes them as per the presence and
absence of those commonalities.
• Association: An association rule is an unsupervised
learning method which is used for finding the
relationships between variables in the large database.
It determines the set of items that occurs together in
the dataset. Association rule makes marketing
strategy more effective. Such as people who buy X
item (suppose a bread) are also tend to purchase Y
(Butter/Jam) item. A typical example of Association
rule is Market Basket Analysis.

14. Traditional programming vs ML

15. ML Project Workflow
• Data collection
• Data Preprocessing
• Split data into test and train
• Model selection
• Training
• Testing
• Deployment

16. Thank You