Artificial Intelligence and Machine Learning
                     
Introduction:
In recent years, two closely related fields 
that have garnered significant attention 
and prominence are artificial intelligence 
(AI) and machine learning (ML). 
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Narrow or Weak AI: 
 Systems like virtual assistants (like Siri and 
Alexa), recommendation engines (like 
Netflix), and self-driving cars are made for 
specialized tasks.
Strong AI: 
 Also known as general AI, refers to systems 
that possess intelligence comparable to that 
of humans and are able to comprehend and 
learn from a variety of data types in 
addition to carrying out a broad range of 
tasks. 
 The long-term objective of creating general 
artificial intelligence is still mostly 
theoretical.
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Machine Intelligence (ML):
The goal of the AI subfield of machine learning 
is to create models and algorithms that let 
computers learn from data and make decisions 
or predictions without needing to be explicitly 
programmed.
Three primary categories can be used to group 
ML algorithms:
 Supervised learning : teaches a model to 
make predictions or classifications based on 
input features by training it on labeled data.
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 Unsupervised Learning: This method looks 
for structures or patterns in data without 
the need for labeled examples.
      Dimensionality reduction and clustering 
are common techniques.
 Reinforcement learning: This technique 
teaches agents how to act in a certain order 
within a given environment in order to 
maximize a reward. 
This is frequently utilized in video games and 
robotics.
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 Data: 
 To train AI and ML models, high-quality, 
pertinent data is essential.
 Algorithms: 
 A variety of algorithms, including decision 
trees, neural networks, and linear 
regression, are used for different tasks.
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 Training: 
 Data is fed into machine learning models, 
and their internal parameters are adjusted 
to help them learn.
Evaluation: 
 Depending on the task, metrics such as 
accuracy, precision, recall, and F1-score are 
used to assess models.
Deep Learning:
 A branch of machine learning that handles 
complicated tasks, frequently involving big 
datasets, by using multi-layered artificial 
Source: udacity.com neural networks. 7
Natural Language Processing (NLP): 
 A branch of AI that focuses on the 
interaction between computers and human 
languages, enabling applications like 
chatbots and language translation.
Computer Vision: 
 The application of AI and ML to image and 
video analysis, used in tasks like object 
recognition and autonomous vehicles.
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Applications: 
 Artificial intelligence (AI) and machine 
learning (ML) have many uses in a variety 
of fields, such as healthcare (drug discovery 
and diagnosis), finance (fraud detection and 
trading), marketing (recommendation 
systems), and many more.
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Conclusion:
 AI and ML have enormous potential to 
change society, and both are still 
developing. But they also bring up social 
and ethical questions that require careful 
consideration, such as those involving 
prejudice, privacy, and job displacement.
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