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Machine Learning
How does Machine Learning work
• A machine learning system builds prediction models, learns from previous data, and predicts the
output of new data whenever it receives it.
• The amount of data helps to build a better model that accurately predicts the output, which in turn
affects the accuracy of the predicted output.
Let's say we have a complex problem in which we need to make predictions. Instead of writing
code, we just need to feed the data to generic algorithms, which build the logic based on the data
and predict the output. Our perspective on the issue has changed as a result of machine learning.
4/21
Features of Machine Learning:
• Machine learning uses data to detect various patterns in a given dataset.
• It can learn from past data and improve automatically.
• It is a data-driven technology.(strategic decisions are based on the analysis
and interpretation of data)
• Machine learning is much similar to data mining as it also deals with the
huge amount of the data.
Need for Machine Learning
 Rapid increment in the production of data.
 Solving complex problems, which are difficult for a human
 Decision making in various sector including finance
 Finding hidden patterns and extracting useful information
from data.
Classification of Machine Learning
Supervised Learning
In supervised learning, sample labeled data are provided to the machine learning system
for training, and the system then predicts the output based on the training data.
 Unsupervised learning is a learning method in which a machine learns without any supervision.
 The training is provided to the machine with the set of data that has not been labeled,
classified, or categorized, and the algorithm needs to act on that data without any supervision.
 The goal of unsupervised learning is to restructure the input data into new features or a group
of objects with similar patterns.
Unsupervised Learning
Difference between Supervised and Unsupervised
Learning
 Goals
 Applications
 Complexity
 Drawbacks
Reinforcement Learning
 Reinforcement learning is a feedback-based learning method, in which a
learning agent gets a reward for each right action and gets a penalty for
each wrong action.
 The agent learns automatically with these feedbacks and improves its
performance.
Reinforcement Learning
Key components of AI
Agent
State
Environment
Action
Reward
Penalty
Applications of Machine learning
THANK YOU

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Machine Learning for AIML course UG.pptx

  • 2. How does Machine Learning work • A machine learning system builds prediction models, learns from previous data, and predicts the output of new data whenever it receives it. • The amount of data helps to build a better model that accurately predicts the output, which in turn affects the accuracy of the predicted output. Let's say we have a complex problem in which we need to make predictions. Instead of writing code, we just need to feed the data to generic algorithms, which build the logic based on the data and predict the output. Our perspective on the issue has changed as a result of machine learning.
  • 3. 4/21 Features of Machine Learning: • Machine learning uses data to detect various patterns in a given dataset. • It can learn from past data and improve automatically. • It is a data-driven technology.(strategic decisions are based on the analysis and interpretation of data) • Machine learning is much similar to data mining as it also deals with the huge amount of the data.
  • 4. Need for Machine Learning  Rapid increment in the production of data.  Solving complex problems, which are difficult for a human  Decision making in various sector including finance  Finding hidden patterns and extracting useful information from data.
  • 6. Supervised Learning In supervised learning, sample labeled data are provided to the machine learning system for training, and the system then predicts the output based on the training data.
  • 7.  Unsupervised learning is a learning method in which a machine learns without any supervision.  The training is provided to the machine with the set of data that has not been labeled, classified, or categorized, and the algorithm needs to act on that data without any supervision.  The goal of unsupervised learning is to restructure the input data into new features or a group of objects with similar patterns. Unsupervised Learning
  • 8. Difference between Supervised and Unsupervised Learning  Goals  Applications  Complexity  Drawbacks
  • 9. Reinforcement Learning  Reinforcement learning is a feedback-based learning method, in which a learning agent gets a reward for each right action and gets a penalty for each wrong action.  The agent learns automatically with these feedbacks and improves its performance.
  • 10. Reinforcement Learning Key components of AI Agent State Environment Action Reward Penalty