Machine Learning Google Developer Student Club

1. By Anirban Bhattarjee (AI/ML HEAD)
Khushi Debbarma (AI/ML CO- HEAD)
Sudipta Sarkar and Debarchito

2. • WHAT IS AN AI
• APPLICATIONS OF AI
• COMPUTER VISION
• NLP
• ROBOTICS
• MACHINE LEARNING
• DATA:- THE FUEL OF AL ENGINES
• BASICS OF MACHINE LEARNING
• PROGRAMMING LANGUAGES FOR ML
• DEEP LEARNING

3. Artificial Intelligence (AI) refers to the
simulation of human intelligence in
machines that are programmed to think,
learn, and problem-solve like humans.

5. FINANCE
HEALTHCARE

6. MARKETING
TRANSPORTATION

7. Briefly elaborate on what you want to discuss.

8. A field that enables machines to
interpret and make decisions
based on visual data, such as
images and videos

10. A branch of AI that deals with the interaction
between computers and human language,
enabling machines to understand, interpret,
and generate human-like text or speech

11. The integration of AI and mechanical systems
to create intelligent robots capable of
performing physical tasks.

12. A subset of AI that focuses on the
development of algorithms and statistical
models that enable computers to improve
their performance on a specific task over time
without explicit programming.

15. Data preprocessing involves
cleaning the data to remove
any errors, inconsistencies,
or missing values
Data preprocessing includes
transforming the data into a
suitable format for analysis
and model training.
Data preprocessing also
involves normalizing the
data to ensure its
compatibility and quality for
analysis and model training.

16. Supervised learning is a type of machine
learning where the algorithm is trained using
labeled data. The labeled data consists of
input variables (also known as features) and
their corresponding output variables.

17. Unsupervised learning is a type of machine learning
where the model is not given any labeled data. Instead, it
must find patterns and relationships within the data on
its own. This can be useful when working with large
datasets where it may not be feasible to manually label
all of the data.

18. Reinforcement learning is a type of machine learning that
involves an agent learning to make decisions based on the
feedback it receives from its environment. The agent learns
through trial and error, receiving rewards or punishments for its
actions. This approach has been successful in a variety of
applications, including game playing, robotics, and autonomous
driving.