Machine learning depends on

• how smart the person on the computer is

• accessing an ever-growing database of increasingly complex information

• having a good machine

A set of instructions to follow in order to solve a problem...

• Algorithm

• Machine Learning

• Neuron

• Noural Network

Automatic recognition of patterns in data (text, handwriting, images, sound and video)

• Recognition

• Predictive Model

• Training

• Testing

A field in the dataset used in the machine learning algorithm. E.g. happy and sad

• Dataset

• unsupervised learning

• supervised learning

• Classifiers

an ML model needs this to generate data to learn from.

• Recognition

• Predictive models

• Training

• Testing

To identify and categorize opinions in text, in order to work out if the writer's attitude is positive, negative, or neutral.

• Decision tree learning

• Predictive models

• Reinforcement learning

• sentiment analysis

All data is labeled and the algorithms learn to predict the output from the input data

• Dataset

• unsupervised learning

• supervised learning

• Classifiers

Several sets of data related to each other used to make decisions in machine learning algorithms. E.g. comments to make you happy and comments to make you sad

• Dataset

• unsupervised learning

• supervised learning

• Classifiers

The data is split according to a certain requirements

• Decision tree learning

• Predictive models

• Reinforcement learning

• sentiment analysis

After training the ML model, we see how accurately it predicts the answer/responds. For example – does it cry when I say something mean to it?

• Recognition

• Predictive models

• Training

• Testing