# 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**** **and **unsupervised**** **machine learning, the model describes the signal in the noise or the pattern detected from the training data.

In **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 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, LSTMs & RNNs, CNNs, Autoencoders, and Deep 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 Methods**

Ensemble techniques like Random Forests and 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."

**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.

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