# Machine Learning Models Explained
## What Is a model?
The output from model training may be used for inference, which means making predictions on new data. A model is a distilled representation of what a machine learning system has learned. Machine learning models are akin to mathematical functions -- they take a request in the form of input data, make a prediction on that input data, and then serve a response.
In [**supervised**](https://machine-learning.paperspace.com/supervised-unsupervised-and-reinforcement-learning#supervised) and [**unsupervised**](https://machine-learning.paperspace.com/supervised-unsupervised-and-reinforcement-learning#unsupervised) machine learning, the model describes the signal in the noise or the pattern detected from the training data.
In [**reinforcement learning**](https://machine-learning.paperspace.com/supervised-unsupervised-and-reinforcement-learning#reinforcement-learning), the model describes the best possible course of action given a specific situation.
The final set of trainable parameters (the information the model contains) depends on the specific type of model -- in deep neural networks, a model is the final state of the trained [weights](https://machine-learning.paperspace.com/wiki/weights-and-biases) of the network, in regression it contains coefficients, and in decision trees it contains the split locations.
## Algorithms
#### Neural Networks
There are many different types of models such as [GANs](https://machine-learning.paperspace.com/wiki/generative-adversarial-network-gan), [LSTMs](https://machine-learning.paperspace.com/wiki/long-short-term-memory-lstm) & [RNNs](https://machine-learning.paperspace.com/wiki/recurrent-neural-network-rnn), [CNNs](https://machine-learning.paperspace.com/wiki/convolutional-neural-network-cnn), Autoencoders, and [Deep Reinforcement Learning](https://machine-learning.paperspace.com/supervised-unsupervised-and-reinforcement-learning#reinforcement-learning) models. Deep neural networks are used for object detection, speech recognition and synthesis
, image processing, style transfer
, and machine translation, and can replace most classical machine learning algorithms (see below)
. This modern method can learn extremely complex patterns and is especially successful on unstructured datasets such as images, video, and audio.
#### **Ensemble Methods**
Ensemble techniques like [Random Forests](https://machine-learning.paperspace.com/wiki/random-forest) and [Gradient Boosting](https://machine-learning.paperspace.com/wiki/gradient-boosting) can achieve superior performance over classical machine learning techniques by aggregating weaker models and learning non-linear relationships.
#### Classical Machine Learning
Popular ML algorithms include: linear regression, logistic regression, SVMs, nearest neighbor, decision trees, PCA, naive Bayes classifier, and k-means clustering. Classical machine learning algorithms are used for a wide range of applications.
## Types of Supervised Learning Models
### Classification
Deep neural networks, classification trees (ensembles), and logistic regression (classical machine learning) are all used to perform regression tasks.
**Popular use cases:**
Spam filtering
, language detection
, a search of similar documents
, sentiment analysis
, recognition of handwritten characters, and fraud detection.
**Binary Classification Goal:** Predict a binary outcome.
#### Examples
* "Is this email spam or not spam?"
* "Is this user fraudulent or not?"
* "Is this picture a cat or not?"
**Multi-class Classification Goal:** Predict one out of two or more discrete outcomes.
#### Examples
* "Which genre does this user prefer?"
* "Is this mail is spam or important or a promotion?"
* "Is this picture a cat or a dog or a fox?"
### Regression
Deep neural networks, regression trees (ensembles), and linear regression (classical machine learning) are all used to perform regression tasks.
**Popular use cases:**
Forecasting stock prices, predicting sales volume, etc
.
**Goal:** Predict a numeric value.
#### Examples
* "What will the temperature be in NYC tomorrow?"
* "What is the price of house in this specific neighborhood?"
## Types of Unsupervised Learning models
### Neural Networks
Deep neural network architectures such as autoencoders and GANs can be applied to a wide variety of unsupervised learning problems.
### Clustering (Classical ML)
**Popular use cases:**
For customer segmentation
, labeling data
, detecting anomalous behavior, etc
.
**Popular algorithms:** K-means, Mean-Shift, DBSCAN
**Goal:** Group similar things together.
#### Examples
* "Cluster different news articles into different types of news"
### Association Rule (Classical ML)
**Popular use cases:**
Helping stores cross-sell products
, uncovering how items are related or complementary, and understanding which symptoms are likely to co-occur in a patient (comorbidity).
**Popular algorithms:** Apriori, Euclat, FP-growth
**Goal:** Infer patterns (associations) in data.
#### Examples
* "If you bought a phone, you are likely to buy a phone case."
### Dimensionality Reduction (Classical ML)
**Popular use-cases:** Recommender systems, topic modeling, modeling semantics, document search, face recognition, and anomaly detection.
**Popular algorithms:** Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA, pLSA, GLSA), and t-SNE.
**Goal:** To generalize data and distill the relevant information.
#### Examples
* "Intelligently group and combine similar features into higher-level abstractions."
## Types of Reinforcement Learning Models
**Popular use-cases:** Robotic motion, recommender systems, autonomous transport, text mining, trade execution in finance, and optimization for treatment policies in healthcare.
**Popular algorithms:** Q-Learning, SARSA, DQN, A3C
**Goal:** Perform complex tasks without training data.
#### Examples
* "Robotic motion control learned by trial and error."
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**Imitation Learning** is an exciting area in Reinforcement Learning, designed to overcome some of the challenges or shortcomings inherent in Reinforcement Learning techniques. These techniques are often used together.
{% endhint %}