Machine Learning (AWS ML Scholarship)

 What is Machine Learning: 

Machine learning (ML) is a modern software development technique and a type of artificial intelligence (AI) that enables computers to solve problems by using examples of real-world data. It allows computers to automatically learn and improve from experience without being explicitly programmed to do so.

 is a modern software development technique that enables computers to solve problems by using examples of real-world data. 

a new field created at the intersection of statistics, applied math, and computer science. Because of the rapid and recent growth of machine learning, each of these fields might use slightly different formal definitions of the same terms.

 

Type of ML:

• In supervised learning, every training sample from the dataset has a corresponding label or output value associated with it. As a result, the algorithm learns to predict labels or output values. We will explore this in-depth in this lesson.
• In unsupervised learning, there are no labels for the training data. A machine learning algorithm tries to learn the underlying patterns or distributions that govern the data. We will explore this in-depth in this lesson.
• In reinforcement learning, the algorithm figures out which actions to take in a situation to maximize a reward (in the form of a number) on the way to reaching a specific goal. This is a completely different approach than supervised and unsupervised learning. We will dive deep into this in the next lesson.
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  All task solved using machine learning need three components: 

  • A machine learning model
  • A model training algorithm
  • A model inference algorithm
    

 

A model is an extremely generic program, made specific by the data used to train it.

Model training algorithms work through an interactive process where the current model iteration is analyzed to determine what changes can be made to get closer to the goal. Those changes are made and the iteration continues until the model is evaluated to meet the goals.

Model inference is when the trained model is used to generate predictions.

 

Think back to the clay teapot analogy. Is it true or false that you always need to have an idea of what you're making when you're handling your raw block of clay?  

False

Five Machine Learning Steps:

• Clustering. Unsupervised learning task that helps to determine if there are any naturally occurring groupings in the data.
• A categorical label has a discrete set of possible values, such as "is a cat" and "is not a cat."
• A continuous (regression) label does not have a discrete set of possible values, which means possibly an unlimited number of possibilities.
• Discrete: A term taken from statistics referring to an outcome taking on only a finite number of values (such as days of the week).
• A label refers to data that already contains the solution.
• Using unlabeled data means you don't need to provide the model with any kind of label or solution while the model is being trained.

• Remember: Classification tasks involve predicting some unknown categorical attribute about your data.

  Regression tasks involve predicting some unknown continuous attribute about your data.

  Clustering tasks involve exploring how your data might be grouped together.

   

 

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About Inas AL-Kamachy