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.- F
**eatures 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`

**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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## Dataset Division,Model fit,Model Indicators, Feature Engineering in Machine Learning - projectsflix · January 5, 2021 at 6:50 pm

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