Learning Machine Learning
Machine Learning is a type of Artificial Intelligence that provide computers the ability to learn or to improve without being explicitly programmed.
Instead of writing a brand new algorithm, machine learning tools enable systems to develop and refine algorithms, by finding patterns in huge amounts of data. It focuses on the development of computer programs that can access data and use it learn for themselves.
Machine learning methods includes observation of data and set of instructions, which helps systems to learn without interaction or assistance.
Types of Machine Learning
The models which use past data for observation or for processing output are called supervised models. This type of models are already trained models with training dataset, they can present the output by processing the training dataset. In general, they are used for classification of objects, categories and etc.
Example: A machine learning model for classification of vehicles can be supervised models as a dataset for classification of vehicles category can be found easily and it will not change in decades.
When sufficient data is not available in training dataset or processing output requires more human interaction such as to observe habits, interests, activities, the model improves the prediction over time by learning all those things.
Example: A machine learning model predicts more accurate combination of products to customers buying either product.
It is more like unsupervised learning model, major difference between both of them is Reinforcement learning model can present more accurate result after continuous processing of input data with less human interaction/assistance.
Example: A machine learning model to predict the profit/loss of some organization or someone’s income or savings by few input parameters.
Applications of Machine Learning
Banking & Financial Services
Bankers can use machine learning for identifying customers based on their track record to set credit limits, to grant loans, to avail rewards, and for other services like preventing frauds, investment trends and for trading prediction.
Doctors can use patients symptoms to compare with other patients already recorded symptoms to identify disease and suitable treatment.
Retailers use machine learning for classifiication of most selling products and less selling products, selling trends by brands, combination of products to suggest more products that results in more selling, also it is used for customer loyality programs.
Oil and Gas
Engineers use it to find new source of energy and minerals. It is also used to detect/predict sensor failure and streamlined oil distribution to make it more efficient and cost-effective.
They use multiple source of data, to be mined and get insights for more efficiency and to detect frauds. They also use it for maintaining equal ratio between supply and demand of public safety and utilities.
Marketing and Sales
eCommerce sites use ML with your buying history, wishlist, watched items and suggests users more relevant/presonalized items and offers to users.
On demand cab services use ML to identify rush in the city, loyalty points for users and drivers. Real time traffic maps predicts better route with real time updates based on insights.
Machine Learning and Apple
Apple has a long history with machine learning. Apple has been using machine learning with their products since a decade or more. E.g. next word prediction on keyboard, face detection on Photos, etc.
Apple unveiled NSLinguisticTagger with iOS 5 for Natural Language Processing (NLP). For better performance and low level access Metal was introduced then after they brought Basic Neural Network Subroutines (BNNS) in Accelerate framework.
In 2017, Apple introduced a new framework called Core ML to integrate already trained models (supervised models) in the app along with Vision. It allows various range of models for integration as it uses Accelerate and Metal to optimize the performance of Core ML. It enables users to process the data on the device itself without worrying about leaving the data to be analyzed.
To incorporate AI in the app, Developers don’t need to be experts in AI or ML to deliver an experience powered by AI and ML withing their app. According to apple, they will take care of technical side of incorporating ML, which allows developers to focus only on building user experiences.
Apple also listed a few domains that can be used with apps:
- Real Time Image Recognition
- Sentiment Analysis
- Search Ranking
- Speaker Identification
- Text Prediction
- Handwriting Recognition
- Machine Translation
- Face Detection
- Music Tagging
- Entity Recognition
- Style Transfer
- Image Captioning
- Emotion Detection
- Text Summarization
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