What are Machine Learning Prerequisites and Machine Learning Terminologies for Beginners?

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If you are keen to know the theory behind the algorithms and how they work, Knowing Below mentioned mathematical chapters and having knowledge of Python programming language is advantageous.

Also Read:What is Machine Learning in Artificial Intelligence and What are it’s applications?

Machine learning mathematics foundation

  • Calculus
  • Statistics
  • probability theory
  • Linear algebra
  • Optimization theory

Mathematical tools

  1. Matlab

Python language

  1. Executable pseudo code
  2. Python is popular: it is widely used, has many code examples, and is rich in module libraries. The development cycle is short.
  3. Features of the Python language:
    • clear and concise
    • easy to understand
  4. Disadvantages of the Python language:
    • The only downside is the performance issue
  5. Python related libraries
    • Scientific function library: SciPyNumPy(bottom language: C and Fortran)
    • Drawing Tools Library:Matplotlib
    • Data analysis libraryPandas

Machine learning terminology for beginners:

  • Model: computer-level cognition
  • Learning algorithm, a method of generating a model from data
  • Data set: a collection of records
  • Example: A description of an object
  • Sample: also called an example
  • Attribute: An aspect of an object’s performance or characteristics
  • Feature: same attribute
  • Attribute value: the value on the attribute
  • Attribute space: the space where the attribute is expanded
  • Sample space / sample space (samplespace): same attribute space
  • Feature vector: Each point corresponds to a coordinate vector in the attribute space, and an example is called a feature vector.
  • Dimension: describes the number of sample parameters (that is, the space is several dimensions)
  • Learning/training: learning from data
  • Training data: data used during training
  • Training sample: each sample used for training
  • Training set: A collection of training samples
  • Hypothesis: The learning model corresponds to some potential rule about data.
  • The ground-truth: the underlying law of existence
  • Learner (learner): Another term for a model that instantiates a learning algorithm in a given data and parameter space.
  • Prediction: the property of a thing
  • Label: Information about the results of the example, such as I am a “good guy.”
  • Example (example): Example of owning a tag
  • Label space: a collection of all tags
  • Classification: A prediction is a discrete value, such as a learning task that divides people into good people and bad people.
  • Regression: The predicted value is a continuous value, for example, your good person reaches 0.9, 0.6 or the like.
  • Binary classification: a classification task involving only two categories
  • Positive class: one of the two categories
  • Negative class: another one in the second category
  • Multi-class classification: classification involving multiple categories
  • Testing: The process of predicting a sample after learning the model
  • Test sample: the sample being predicted
  • Clustering: divides objects in the training set into groups
  • Cluster: Each group is called a cluster
  • Supervised learning: paradigm – classification and regression
  • Unsupervised learning: paradigm–clustering
  • Unseen instance: “new sample”, untrained sample
  • Generalization ability: the ability of the learned model to apply to new samples
  • Distribution: A law of obedience of the entire sample space of a sample space
  • Independent and identically distributed (i, i, d.): Each sample obtained is independently sampled from this distribution.