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What are the scope and limitations in machine learning ?

Scope of Machine Learning:

  • Prediction: ML excels at analyzing data to predict future trends and outcomes. This is used in finance, weather forecasting, and even product recommendations.
  • Understanding complex data: Machine learning can process massive amounts of data, including images, text, and speech, to identify patterns and relationships that humans might miss. This is what powers applications like computer vision and natural language processing.
  • Automation: ML algorithms can automate tasks that are repetitive or require complex decision-making. This is leading to advancements in areas like robotics and self-driving cars.

Limitations of Machine Learning:

  • Data dependence: ML algorithms are only as good as the data they’re trained on. Insufficient data or poor quality data can lead to inaccurate predictions or biased results.
  • Interpretability: Some ML models, especially complex ones, can be difficult to understand. This makes it challenging to pinpoint why a particular prediction was made, which can be a concern in high-stakes applications.
  • Security and bias: ML models can be vulnerable to hacking or manipulation. Additionally, biased data can lead to biased algorithms, which can perpetuate societal problems.

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