This document provides an overview of trending topics in machine learning, including machine learning algorithms, supervised learning, unsupervised learning, reinforcement learning, computer vision, neural networks, predictive learning, and data mining. It classifies machine learning algorithms and learning methods, describes common applications and techniques, and provides brief definitions and examples for key concepts in machine learning.

1. Trending Topics in
Machine Learning

2. Machine Learning Algorithms
Machine Learning uses algorithm to turn a dataset into
model. Machine learning have the ability to calculate
complex data in a fast manner. We can easily make
decisions with less human intervention. Learning
algorithms are classified into 3 categories:
1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning

3. Supervised Learning
In supervised learning we use labelled datasets and
model learns about each data . Then ,after training
model is tested on the basis of test data . Supervised
Learning is further divided into 2 types :
● Regression
● Classification

4. Regression
It is used to determine the relationship between one
input variable and output variable . It is used for
prediction of continuous variables.
Classification
Classification is the process of grouping of objects and
ideas into preset categories. Classification is a form of
pattern recognition. One of the most common
applications of classification is for filtering emails into
“spam” or “non-spam”, used by email service providers.

5. Unsupervised Learning
In unsupervised learning , we not used any
labelled parameters and focus on discovering
hidden structures from unlabeled data to help
systems. Unsupervised learning further divideed
into 2 categories:
● Clustering
● Association

6. Clustering
Clustering ia collection of objects into
clusters with similarities in one group and
with no similarities in different group
Association
It is the method of determining the
relationship between different variables in
the database.

7. Reinforcement Learning
Reinforcement Learning is different from
supervised learning & unsupervised learning. We
can say that it is a process of trial and error finally
delivering results. In this learning agent
automatically learns with experience without any
label data.

8. Computer Vision
Using computer vision technology we drive
decisions using contextual image data.The main
goal of computer vision technology user can easily
interpret higher level concepts by the use of raw
image.

9. Neural Networks
Neural Network is also called deep neural
network .It consists of at least 3 layers of
neurons-input layer, hidden layer, output layer.
Neural network is tool used in many different
machine learning algorithms to process data input
that machines can easily understand.

10. Predictive Learning
Predictive analytics use techniques including ML,
predictive modelling and data mining to predict
future results. In predictive machine learning we
analyze the historical data to find patterns and
behaviours.

11. Data Mining
Data mining helpful in extracting information
from large amount of data for predicting the
results on a certain data. The softwares used in
data mining -clustering & segmentation software,
classification, association, text analysis, statistical
analysis.

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