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