Artificial Intelligence

AI is about building intelligent computer programs that carry out tasks like:

  • Visual perception
  • Speech recognition
  • Sentiment analysis

Machine learning

  1. The process of using algorithms to tell you something interesting about your data without writing code specific to the problem
  2. Instead of writing code, you feed a machine data and it builds its own logical function based on this data.
  3. The more data you feed your model, the stronger it gets.
  4. The better the quality of your data, the better your model.

Deep learning

  1. The algorithm can only be as good as the data that goes into training it.
  2. Performance of machine learning algorithms can weaken when key information   is buried in unstructured data.
  3. Deep learning is great at automatically learning the best features from noisy data
  • Deep learning uses complex algorithms to perform tasks in domains where it actually learns the domain with little or no human supervision.
  • It learns how to learn. For example, consumer apps like Google use deep learning to power facial recognition in photos.

Natural Language Processing
Natural Language Processing (NLP) is a form of machine learning that recognizes language, its many usage and grammar rules by finding patterns within large data sets.

  • NLP can perform sentiment analysis, where algorithms look for patterns in social media postings to understand how customers feel about a specific brand or product.
  • NLP handles speech recognition, providing a text summary derived from “listening” to an audio clip of a human speaking.
  • NLP conducts question answering, typically handling those questions with a specific answer (for example, What is the square root of 4?), but also exploring how to handle more complex and open-ended questions.

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