Machine learning is a branch of artificial intelligence that enables systems to learn from data and improve over time, with applications in various fields such as healthcare, finance, and social media. While it offers advantages like efficiency and adaptability, machine learning also presents challenges like high costs and potential job displacement. The intersection of machine learning and robotics is leading to advanced automation and improved operational efficiency across industries.

1. MACHINE
INTRODUCTION
TO

2. WHAT IS MACHINE
LEARNING ?
• 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.
• The term “Machine Learning”
was coined by Arthur Samuel, a
computer scientist at IBM .
• A type of artificial intelligence (AI)
focused on building computer systems
that can learn from data.

3. APPLICATIONS OF MACHINE LEARNING -
Facial recognition
Product recommendations
Email automation and spam filtering
Financial accuracy
Social media optimization
Healthcare advancement
Mobile voice to text and predictive text
Predictive analytics

4. TYPES OF MACHINE LEARNING-

5. ADVANTAGES OF MACHINE LEARNING-
• ML is efficient in pattern-finding.
• ML work is automated and does
• not require human intervention.
• ML algorithms are ever-evolving.
• ML can work with different types
of data – labeled or unlabeled,
visual or textual, ML algorithms
can handle them.

6. DISADVANTAGES OF MACHINE LEARNING -
 Time and Resources
 Results Interpretations
 High Error Chances
 Social Changes
 Elimination of Human
Interface
 Changing Nature of
Jobs
 Highly Expensive

7. MAIN BRANCHES OF MACHINE LEARNING -

8. PROGRAMMING LANGUAGE
FOR MACHINE LEARNING -
 Python
 Java
 C
 C++
 JavaScript
 C#
 GO
 Julia
 Scala

9. FUTURE OF MACHINE LEARNING
IN ROBOTICS -
• Computer Vision: Robots can identify and recognize
objects they meet, discern details, and learn how to
navigate or avoid specific items.
• Manipulation: AI helps robots gain the fine motor skills
needed to grasp objects without destroying the item.
• Motion Control and Navigation: Robots no longer need
humans to guide them along paths and process flows. AI
enables robots to analyze their environment and self-
navigate. This capability even applies to the virtual world
of software. AI helps robot software processes avoid flow
bottlenecks or process exceptions.

10. MACHINE LEARNING ROBOT
• Machine Learning is a sub-discipline within
Artificial Intelligence that allows a computer to
identify behaviour patterns within a massive
amount of data and develop predictive
analytics, improving the experience.
• A machine learning robot is a type of robot
that includes these machine learning
techniques to acquire knowledge and improve
its responsiveness, based on what it learns.
• The machine learning process allows robots to
recognize patterns that help them understand
their environment and perform specific tasks
more efficiently by applying what they learn.

11. BENEFITS OF MACHINE LEARNING IN
ROBOTICS
• Adaptability: ML enables robots to adapt to changing
environments and handle unforeseen circumstances. They
can learn from experience, detect patterns, and make
adjustments to optimize performance.
• Efficiency: ML algorithms empower robots to acquire efficient
strategies for task execution, enabling them to optimize
energy utilization, reduce wastage, and enhance overall
operational efficiency.
• Safety: ML enhances robot safety by enabling them to detect
and avoid obstacles, predict potential risks, and make real-
time decisions to ensure the safety of humans and the robot
itself.
• Precision: ML algorithms enable robots to perform tasks with
high precision and accuracy. They can learn from data,
improve their motor skills, and execute delicate operations.

12. FUTURE OF MACHINE LEARNING -
 Fine – tuned personalization
 Better search engine experiences
 Evolution of data teams
 No-code environment
 Rise of quantum computing

13. MACHINE LEARNING AND ROBOTICS
 The intersection of robotics and machine learning
introduces new possibilities for the autonomy of
mobile robots and for the intelligence of their task
execution.
 Machine learning robots are being used in a wide
range of applications, from inspection and
maintenance or surveillance to manufacturing and
healthcare.
 In the manufacturing industry, machine learning
robots can improve the efficiency and accuracy of
production processes by learning to perform
complex tasks more quickly and accurately.

14. THANK YOU
SUBMITTED BY:- ARYAN PANWAR
CLASS ROLL NO:- 20
STUDENT ID:- 23151325
BCA ( AI & DS )