GDSC BCE Patna ML Session Date : 15/04/2023

1. M A C H I N E
L E A R N I N G
By- Shivam Kumar

2. Introduction
Machine learning is a subfield of artificial
intelligence that enables computers to learn from
data and improve their performance at a given
task without being explicitly programmed to do
so. In other words, it allows machines to learn
from experience and improve their accuracy over
time.
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3. AI VS ML VS DL
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4. Machine Learning in daily life
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•In Social media platforms for personalize content feeds, recommend friends .
•Virtual assistants like Siri, Alexa use NLP and ML algorithms to understand.
•Google and Bing use machine learning to provide relevant search results
•.
•Uber and Lyft use machine learning algorithms to optimize ride matching and pricing.
•Amazon use machine learning to make product recommendations.
•Financial institutions use machine learning algorithms to detect and prevent fraud,
analyze credit risk, and make investment decisions.
•Healthcare providers use machine learning to analyze medical images, predict disease
outcomes, and personalize treatment plans

5. How To Start?
Q1) Which programming language? Python?
Here is the reason why python is preferred and why every course or tutorial choose
it.
1. Python is easy to learn
2. Python has a lot of libraries and framework.
3. Easy syntax and require less lines of code to implement a logic.
4. Community: Python has a large and active community of machine learning
practitioners, which means that there are more resources, tutorials, and
examples available online than for C++ or Java.

6. Roadmap

7. Types of Machine Learning
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1. Supervised Learning
2.Unsupervised Learning
3.Reinforcement Learning

8. Supervised Learning
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•In supervised learning, the
algorithm is trained on labeled
data, meaning the data is
already categorized or
classified. The algorithm learns
to predict the output based on
the input, and is then tested on
new data to evaluate its
accuracy.
•Used in predicting sales and
risk evaluation.

9. Unsupervised Learning
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•In unsupervised learning, the
algorithm is trained on
unlabeled data, meaning the
data is not categorized or
classified. The algorithm learns
to identify patterns or
structures in the data, and is
then used to cluster or group
the data into categories.
Examples include anomaly
detection and
recommendations system.

10. Reinforcement Learning
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•In reinforcement learning, the
algorithm learns through trial
and error, and receives
feedback in the form of
rewards or penalties based on
its actions. Examples include
game playing and robotics.

11. Use Cases of Some ML Algo
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1. KNN algorithm (supervised) is used in approving loan.
2. K Means Algorithm is used in clustering movie on the basis
of rating.
3. A priori algo is used in association between different objects.
4.Q learning algorithm (reinforcement)

12. Machine Learning Libraries
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1.Scikit-learn: This is one of the most popular machine learning
libraries, providing a wide range of algorithms for tasks such as
classification, regression, and clustering. NLP and Computer
Vision.
2.TensorFlow: Developed by Google. NLP, image and speech
recognition, autonomous driving and robotics.
3.PyTorch: Developed by Facebook, this is another open-
source library that is widely used for deep learning tasks such
as computer vision and natural language processing.

13. ML APIs for Developers
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ML APIs allow developers without ML expertise to access pre-
trained models for making predictions without worrying about
the model's technical details or the algorithms used to train it.
Some popular ML APIs are-
1. Google cloud machine learning APIs
2. Microsoft Azure Machine Learning
3. openAI

14. Application of Machine Learning
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Image and speech recognition
Natural language processing:
Predictive maintenance
Fraud detection
Recommendation systems:
Medical diagnosis

15. CrowdSource by Google.
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Crowdsource Android
and Web apps allow
users to answer quick
questions in a gamified
UI, and help generate
diverse training data for
machine learning (ML).

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17. Thank you
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