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

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: SciPy, NumPy(bottom language: C and Fortran)
   • Drawing Tools Library:Matplotlib
   • Data analysis library: Pandas

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.