What is Machine Learning?
In simple words, Machine learning is an application of artificial intelligence which gives systems the ability to learn things automatically and improve from experience without being programmed explicitly. Machine learning concentrates on the event of laptop programs that may access information and use it to be told for themselves.
Supervised machine learning builds a model that makes predictions established on evidence in the presence of uncertainty. A supervised learning algorithm takes a familiar set of input data and known responses to the data (output) and trains a model in a certain way to generate reasonable predictions for the response to new data. Use supervised learning if you have known input data for the output you are trying to predict. Supervised learning uses two techniques classification and regression to develop predictive models.
Classification techniques predict separate responses—for example, whether an email is from a genuine resource or spam, or whether a tumor is cancerous or benign. Classification models segregate input data into categories. Typical applications include medical imaging, text to speech, and credit scoring, etc.
Use classification if your data can be labeled, classified, or separated into specific groups or classes. For example, applications for hand-writing recognition use classification to recognize letters and numbers.
Common algorithms for performing classification comprise support vector machine (SVM), decision trees, k-nearest neighbor, Naïve Bayes, discriminant analysis, logistic regression, and neural networks.
Regression techniques predict constant responses—for example, changes in temperature or fluctuations in power demand. Typical applications include electricity load forecasting and algorithmic trading, etc. Suppose you are working with a data range or if the nature of your response is a real number, such as temperature or the time until failure for a piece of equipment then you can use regression techniques. Common regression algorithms comprise linear model, nonlinear model, regularization, stepwise regression, boosted and bagged decision trees, neural networks, and adaptive neuro-fuzzy learning.
Unsupervised learning finds hidden patterns or inseparable structures in data. It is used to draw conclusions from datasets consisting of input data without tagged responses. Clustering or grouping is the most common unsupervised learning technique. It is used for exploratory data analysis to search hidden patterns or groupings in data. Applications for cluster analysis include gene sequence analysis, market research, and object recognition, etc.
For instance, if a mobile phone company wants to optimize the locations where they build mobile phone towers, they can use machine learning to estimate the number of clusters of people depending on their towers. A phone can only talk to one tower at a time, so the team uses clustering algorithms to design the best placement of cell towers to optimize signal reception for groups, or clusters, of their customers.
Common algorithms for performing clustering comprise k-means and k-medoids, hierarchical clustering, Gaussian mixture models, hidden Markov models, self-organizing maps, fuzzy c-means clustering, and subtractive clustering.
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Applications of Machine Learning:-
Automatic Friend Tagging Suggestions on Facebook or any other social media platform is one of the most common applications of Machine Learning is. How Facebook does that? Well, Facebook uses face detection and image recognition to automatically search the face of the person which matches their Database and hence suggests us to tag that person based on DeepFace.
If you have ever used an app to book a cab, you are already using Machine Learning to some level. It supplies a personalized application that is unique to you. Automatically discovers your location and provides options to either go home or office or any other frequent place which you have visited before based on your History and Patterns.
Suppose you check a product on Amazon, but you do not buy it then and there. But the next day, you’re watching videos on YouTube and suddenly you see an ad for the same product. You switch to Facebook, there also you see a similar ad. So how does this happen?
Well, this happens because Google keeps your search history, and recommends ads based on your search history. This is one of the interesting applications of Machine Learning. In fact, Amazon generates 35% of its revenue by Product Recommendation.
4.Virtual Personal Assistant
As the name implies, Virtual Personal Assistants assist in searching useful information, when asked through text or voice. Some of the major applications of Machine Learning here are:
- Speech Recognition
- Speech to Text Conversion
- Natural Language Processing(NLP)
- Text to Speech Conversion
All you need to do is ask a simple question like “Show me my schedule for tomorrow” or “Show my upcoming Flights“. For answering, your personal assistant searches for information or recalls your related queries to collect information. Recently personal assistants are being used in Chatbots which are being implemented in various food ordering apps, online training websites and also in Commuting apps.
Well, here is one of the Interesting applications of Machine Learning. It’s here and people are using it already. Machine Learning plays a very pivotal role in Self Driving Cars and I’m sure you guys might have heard about Tesla. The leader in this business and their current Artificial Intelligence is driven by hardware manufacturer NVIDIA, which is built on the Unsupervised Learning Algorithm.
NVIDIA articulate that they didn’t train their model to detect people or any object as such. The model works on Deep Learning and it crowdsources data from all of its vehicles and its drivers. It uses internal and external sensors which are a part of the Internet of Things. According to the data collected by McKinsey, the automotive data will hold a tremendous value of $750 Billion.
Remember the time when you traveled to a completely new place and you find it difficult to communicate with the locals or finding local spots where everything is written in a different language.
Well, forget those old days. Google’s Google Neural Machine Translation is a Neural Machine Learning that works on numerous languages and dictionaries, uses Natural Language Processing to supply the most accurate translation of any sentence or words. Since the tone of the words also matters a lot, it uses various techniques like POS Tagging, NER (Named Entity Recognition) and Chunking. It is one of the coolest and most widely used Applications of Machine Learning.
7.Online Video Streaming
With over 100+ million subscribers, there is no doubt that Netflix is the leader of the online streaming world. Netflix’s speedy rise has all movie industrialists taken aback – forcing them to ask, “How on earth could one single website take on Hollywood?”. The simple answer is Machine Learning.
The Netflix algorithm continuously collects massive amounts of data about users’ activities like:
- Browsing and Scrolling Behavior
- When you pause, rewind, or fast forward
- What day you watch web content (TV Shows on Weekdays and Movies on Weekends)
- The Date and Time you watch
- When you pause and quit content (and if you ever come back)
- The ratings Given by subscribers (about 4 million per day), Searches (about 3 million per day)
And a lot more. They gather this data for each and every subscriber they have and use their Recommender System and a lot of Machine Learning Applications. That’s why they have such a huge customer retention rate.
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- animated graphics
- photo slideshows
- autocomplete text suggestions
- interactive forms
TensorFlow.js is an open-source library that permits you to execute machine learning programs completely in the browser. It is the successor of Deeplearn.js, which is no longer in use. TensorFlow.js betters the functionalities of Deeplearn.js and permits you to make the most of the browser for a deeper machine learning experience. With the library, you can use flexible and instinctive APIs to define, train, and deploy models from scratch right in the browser. Furthermore, it automatically provides support for WebGL and Node.js.
Keras.js is one more trending open-source framework that permits you to run machine learning models in the browser. It provides GPU mode support using WebGL. Suppose If you have models in Node.js, you will run them only in CPU mode. Keras.js also provides support for models which are trained by using any backend framework, such as the Microsoft Cognitive Toolkit (CNTK).
Shah Rukh Patwekar | Software Developer