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# Use cases for Machine learning
- URL: https://www.aveshneeiyer.com/deep-learning-and-machine-learning-what-is-the-difference/
- Published: 2022-01-16T19:43:32.000Z
- Updated: 2022-03-31T08:03:01.000Z
- Description: There are many use cases and business models for machine learning and deep learning: Processing of applications and verifying identity, drivers and passports as part of the automated verification process. Have a look at my blog n POC prototype for digital account opening using OCR.
- Author: Aveshnee iyer
- Tags: use cases, machine learning, Recommendation Engine, ocr, strategy

# 

There are many **use cases and business models** for machine learning and deep learning:

- Processing of applications and verifying identity, drivers and passports as part of the automated verification process. Have a look at my blog on [POC prototype for digital account opening](https://www.aveshneeiyer.com/document-classifier-id-drivers-license-passports/) where I use Optical Character Recognition to identify ID's, driver's licences and passports).
- Medical diagnosis for the health industry is a good way to help people maintain healthy lives by diagnosing conditions early. Using wearable tech to gather a large amount of data, machine and deep learning are able to make predictions on current and future health issues and help people to make better decisions around lifestyle.
- Recommendation engines eg. [Movie recommender system - Semantic similarity](https://www.aveshneeiyer.com/nlp/) using a set of historical data of users to make recommendations on similar content leveraging Natural Language Processing).
- Computer vision in retail or e-commerce for shopping, self-driving cars, health industry medical imaging, cancer cell detection.
- Financial and insurance services for better decision making on loan applications, insurance risk, fraud detection and customer service.

  
## **What is Machine Learning?**

**Machine Learning** learns from data points using algorithms to performs tasks and make accurate predictions. Within machine learning there are 3 main categories and various algorithms that can be applied:

1. **Supervised Learning** \- training data with right answers is used to train the computer to learn to predict correct answers to similar type of questions. eg. of a supervised learning algorithm include:

> Regression - linear regression (simple & multi linear regression), boosted decision tree.

> Classification - logistic regression, support vector machine, naive bayes.

**2\. Unsupervised Learning** \- does not learn from labeled data sets but rather attempts to find patterns from new unlabelled data to predict accurate results (labels). Unsupervised learning algos include:

> KMeans clustering

> Hierarchical clustering

**3\. Semi-supervised learning** \- distant supervision is required in the form of human intervention, so not fully independent as in unsupervised learning. A type of semi-supervised learning is reinforcement learning.

## **What is Deep Learning?**

In **deep learning** a computer system is able to use unstructured data to analyse videos, texts, speech whereas **machine learning** is unable to extend itself to this level of complexity and requires structured data for training before it can make predictions. **Deep learning aims to mimic the function and thinking patterns of the human brain by using neural networks to create a form of human intelligence.** It does not require structured data but learns from pattern recognition through neural networks and therefore quality of the data is very important.

**Deep learning** (deep neural networks) is used for more complex tasks. The number of node layers and depth determine the type of neural network. The different type of neural network algorithms are:

1. Convolutional Neural Network (CNN)
2. Recurrent Neural Networks (RNNs)
3. Long Short-Term Memory Networks (LSTMs)
4. Stacked Auto-Encoders.

> You can access an online course to learn more on machine learning and deep learning and its application. We can use various machine learning models to solve for a wide range of challenges but ultimately we need to experiment with user experience to gain higher adoption.

For more Follow me on Medium [https://medium.com/@aveshnee7](https://medium.com/@aveshnee7?ref=aveshneeiyer.com)