3. • “AI is the new
electricity.”
• “The key to
successful AI is not
artificial but human
intelligence.” Andrew Ng
Founder, Deeplearning.AI
and Coursera
4.
5. What is Machine Learning?
A subfield of artificial intelligence that
allows computers to learn from data
without being explicitly programmed.
Machines learn from patterns and
relationships in data, enabling them to
make predictions, decisions, and automate
tasks. Deep Learning
Machine Learning
Artificial Intellegence
6. Why is Machine
Learning Important?
1. Benefits:Increased efficiency,
accuracy, personalization,
automation and innovation.
2. Impact on various
industries:Healthcare, finance,
manufacturing, retail, and more.
3. Applications:Fraud detection,
medical diagnosis, self-driving
cars, personalized
recommendations, and intelligent
assistants.
12. Types of Machine Learning
• Supervised learning: Labeled data, learning a mapping function from inputs to
outputs.
• Examples: Classification (spam detection, image recognition), regression (predicting
house prices, market trends).
• Unsupervised learning: Unlabeled data, discovering hidden patterns and
structures.
• Examples: Clustering (grouping customers, genes), dimensionality reduction
(compressing data).
• Reinforcement learning: Trial and error, learning through interactions with an
environment.
• Examples: Playing games, robot control, resource allocation.
13. The Machine Learning Workflow
• Data collection and preparation:
Gathering and cleaning data for training and testing.
• Model selection and training:
Choosing an appropriate algorithm and training it on the prepared data.
• Evaluation and refinement:
Assessing the model's performance and making adjustments to improve its
accuracy.
• Deployment and monitoring:
Putting the model into production and monitoring its performance over time.
14.
15. चलो हाथ साफ करत
े ह
ैं …
I mean let’s do some
hands on 🤖🏻💻
GHRCEM, Pune