GDSC YCCE - Into the World of AI session Presentation

1. Hands-On Session
On Artificial Intelligence and Machine Learning
Into the
World of AI

2. Introduction to
AI/ML
Hands-on experience
with Machine Learning
Custom Gemini Model -
Making an AI Chatbot
Dive into the Google
Colab Environment
ML Template
Coding
AI/ML Integration
with Web Apps
Google AI Studio
Overview
Agenda

3. What is not
considered as AI?

4. What are LLMs and
Generative AI?
• A large language model (LLM) is a
language model notable for its ability to
achieve general-purpose language
generation and understanding.
• LLMs take a text prompt, from a user or
another program to generate text.

5. What is Transformer
Architecture?
• A transformer architecture is a type of
neural network architecture that
changes an input sequence into an
output sequence.
• It consists of two parts:
o An Encoder and
o A Decoder

6. How AI and ML are related ?

7. Machine Learning
• Machine Learning is a way to teach computers to
learn from experience and improve their
performance over time and make predictions.
• Machine learning systems first takes input to learn
and then make useful predictions and decision
about unseen data.

8. Flow of ML model

9. Types of ML models

10. Supervised vs Unsupervised Learning

11. Classification
• Primary objective of a classification
model in machine learning is to
categorize or label input data into
predefined classes or categories.
• Some Classification Algorithms :
Logistic Regression, KNN, Naïve Bayes,
SVM, etc.

12. Regression
• Regression analysis is used when
the goal is to understand the
relationship between dependent
and independent variables.
• Some Regression Algorithms :
Linear regression, Decision Tree,
Random Forest regression, etc.

13. Clustering
• Primary objective of clustering is to
gather similar points together, forming
distinct clusters.
• Some Clustering Algorithms :
K-means, Hierarchical clustering,
DBSCAN, etc.

14. Type of ML model should be used ?
2. For suggesting
spelling corrections
1. For Labelling Email as
spam or not-spam
3. For Estimating the
arrival of bus, based on
the time and traffic.

15. Hands-On ML ...

16. Various Python Libraries for ML
• NumPy: Numerical computing and efficient mathematical functions
• Pandas: Data manipulation and analysis, providing data structures
like Data Frames.
• Matplotlib: 2D plotting library for creating static, animated, and
interactive visualizations.
• Scikit-learn: For building and evaluating predictive models.
• TensorFlow: For building and training deep learning models.
• Pytorch: Deep learning library with a dynamic computational graph.

17. Machine Learning Template Codes

18. AI won’t Replace you ,
But…
Someone Using and Building AI
will…