Data Analysis Using Machine Learning Techniques

1.

Data Analysis using
Machine Learning
techniques
STUDENT NAME: AZIZBEK NASRIDDINOV
ENROLLMENT NUMBER: A85204919034
GUIDE NAME: JITENDAR TANWAR
GROUP: BSC(IT) SECTION 1 SEMESTER 5

2.

Contents
1. What’s Machine Learning?
2. What’s Data analysis?
3. Data analysis in practice with examples
4. Machine Learning and Data Analysis in practice
5. Machine Learning in Healthcare with examples
6. Real-Time data ingestion with ML
7. Smart Health records with ML

3.

What’s Machine Learning?
Machine Learning is a discipline of Artificial Intelligence and
Computer Science that focuses on automating the process of creating
analytical models. And based on concepts like systems' ability to learn
from data that they absorb, pattern recognition, and creating systems
capable of making judgments without or with minimal human intervention.
Machine learning is evolving and becoming a more versatile and powerful
technology as time goes on. Machine Learning began with the discovery
of patterns and the question of whether computers might learn new skills
without being instructed to do so by programmers.

4.

What’s Data analysis?
Data analysis is a process of inspecting, cleansing, transforming, and
modelling data with the goal of discovering useful information, informing
conclusions, and supporting decision-making. Data analysis has multiple
facets and approaches, encompassing diverse techniques under a
variety of names, and is used in different business, science, and social
science domains.
Data analysis plays a role in making decisions more scientific and helping
businesses operate more effectively

5.

Data analysis in practice with
examples
1. Data analysis is used in Healthcare industry for treating
patients. Modern appliances can utilize the data analytics
information to track the condition of patients.
2. For security purposes?
3. In transportation
4. Websites can use Data analysis to gather information (Context
Management)
5. Data Analysis with AI for processing data
6. Real-Time data ingestion
7. With Clump AI to arrange data and so on

6.

Machine Learning and Data
Analysis in practice
1. WordStream provides search marketing management software and
services as well as tools for PPC, SEO, and social
2. Twitter uses machine learning technology and AI to evaluate tweets in
real time and score them using various metrics to display tweets that have
the potential to drive the most engagement
1. Google is researching nearly every aspect of machine learning and is
making developments in “classical algorithms” and other applications like
natural language processing, speech translation, and search ranking
and prediction systems
3. Edgecase uses machine learning to analyze customer behaviors and
actions to provide a better experience for shoppers who may not know
what they want to buy, in an effort to make casual online browsing more
similar to a traditional retail experience

7.

Machine Learning in Healthcare
with examples
1. Clinical Decision Support Systems
2. Shrewd Recordkeeping
3. AI in Medical Imaging
4. Customized Medicine
5. Conduct Adjustments
6. Prescient Approach to Treatment
7. Information Collection
8. Older and Low-Mobility Groups Care
9. Automated Surgery
10.Drug Discovery and Production
11.Smart Health records

8.

Real-Time data ingestion with ML
Streaming Regression
Takes the labeled informative elements. Model gets trained on each
clump of the information stream. It tends to be called rehashed time to
prepare on different stream.
It likewise take labeled relevant informative items and tells the model to
make forecast on the info stream. On each passing window, model
variable gets refreshed also uncovered the most recent prepared model.
This empowers client to utilize the model in different applications or
save at an outside area. Like group execution, streaming model can
be configured with step size and number of emphasize. Toward the
beginning of the preparation, beginning weight vectors are set to zero
vector or as an arbitrary vector.

9.

Streamline architecture

10.

Event generation and learning

11.

Smart Health records with ML
Smart health pipeline

12.

The increase of age average led to an increase in the demand of
providing and improving the service of healthcare. The advancing of
the information and communication technology (ICT) led to the
development of smart cities which have a lot of components.
One of those components is Smart Health (s-Health), which is used in
improving healthcare by providing many services such as patient monitoring,
early diagnosis of diseases and so on. Nowadays there are many machine
learning techniques that can facilitates s-Health services

13.

Thank You