DAY - 1 - Lesson 1 - Introduction to Machine Learning

1. Introduction to
ML
Explore ML

2. What is Machine Learning?

3. Machine Learning systems take inputs
(data) to make useful predictions and
decisions about previously unseen
pieces of data.
ML Extended
PROPRIETARY + CONFIDENTIAL

4. Machine learning is a specific field of AI
where a system learns to find patterns in
examples in order to make predictions.
ML Extended
PROPRIETARY + CONFIDENTIAL

5. Computers learning how to do a task
without being explicitly programmed to
do so.
ML Extended
PROPRIETARY + CONFIDENTIAL

6. Example

7. Smart Camera
Open Smart Camera task and point your
camera to any objects around you.
Eg: Bottle, Table, a pot, book etc
See if the camera is able to identify the
object you are pointing to.

8. Machine Learning systems might:
● Label or classify data
● Predict numerical values
● Cluster similar pieces of data together
● Infer association patterns in data
● Create complex outputs

9. Machine Learning could be
used for early dementia
diagnosis
Automating drone-based wildlife
surveys saves time and money
"Machine Learning: Why or Why not?"
Read a couple of news articles
involving applications of ML.
1. Would a traditional programming
solution be more efficient?
2. Could a human perform the same
task in less time?
3. What are the benefits of a Machine
Learning model in these instances?

10. Idea to
Implementation

11. Collect
Data
Train and
Test Model
Define
Objective
Predict and
Evaluate
Focus on
User
Machine Learning Process

12. Model learns patterns
from unlabelled data.
Machine Learning
Supervised Unsupervised
Model is trained on
labeled data
stop_sign_4
stop_sign_1 stop_sign_2
stop_sign_3

13. Loss(Example)

14. See it in action!
Image label verification
Supervised learning
Visit https://crowdsource.app to try these tasks
Semantic Similarity
Unsupervised learning

15. Examples

16. Examples

17. Examples

18. Examples

19. Predicting the Price of a House
Features
● Location
● Number of bedrooms
● Size of property
● Number of light switches?
● Color of house?

20. Recommending which video a user should watch next
Features
● Topic
● Popularity of a video/Number of views
● Creator of video
● Length of video?
● Age of video?

21. Thank You