Intelligence of machines/algorithms in predictive and prescriptive analysis
Simulates the human brain to assist in solving complex problems
The ability of machine algorithms to learn without any predefined code.
Type of machine learning where the concept of Artificial Neural Networks is implemented
The Science of getting insights from a dataset (group of data)
Algorithms to analyze and process human language consisting of grammatical and literary ambiguities
A *neural network trained to recognize cancer on an MRI scan may achieve a higher success rate than a human doctor. This system is certainly a cognitive system but is not artificially intelligent.
The provided dataset will consist of input features and the corresponding outcome(s), called as labels.
Dataset wont have any outcomes/labels.
Training an algorithm through rewarding for every correct outcome and penalizing for every incorrect outcome through negative points.
Regression is the process of prediction in which the model will be fed with values to find the output which is not discrete (finite number of classes) like classification, instead continuous between range.
$$ \bar{y} = \beta_0 + \beta_1\bar{x} + e $$
Where y is the prediction , x is the feature vector and $\beta_0$ and $\beta_1$ are estimators which get adjusted according to the data
OLS (Ordinary Least Squares)
The error function is the Euclidian perpendicular distance between the line and the actual point.