What is Machine Learning?
The word machine learning was introduced in 1959 by Arthur Samuel who was an American IBMer and explorer in the field of PC gaming and artificial intelligence. Geoffrey Everest Hinton is father of machine learning. As per experts the Machine Learning market is forecasted to be worth of $30.6 Billion by 2024.
Machine learning (ML) is defined as study of computer algorithms that could be improved automatically by experience and by the use of data. It is also a subset of artificial intelligence. ML algorithms creates a model based on sample data, which is also known as training data, to forecasts or decisions without being specifically programmed to do so. Machine learning algorithms are used in a varities of applications like, in medicine, email filtering, speech recognition, and computer vision, and so many, where to develop traditional algorithms to perform the important tasks is difficult or impossible.
In other words, Machine learning is a branch of artificial intelligence (AI) and computer science that aims on the usage of data and algorithms to copy the way that human learns, and slowly- slowly improving its accuracy.
Types of Machine learning
- supervised learning
- unsupervised learning
- reinforcement learning
What is the use of Machine Learning?
There are so many use cases of machine learning. Let’s check one by one what are those:
- Applications in Smartphones –
- Voice Assistants – You must have seen so amy voice assistants like Siri, Google Assistant, Alexa, cortana etc. All these are powered by machine learning algorithms. As well as all these voice assistants analyze speech by using Natural Language Processing and translate them into numbers using machine learning, and prepare a reply accordingly.
- Smartphone Cameras – Due to this machine learning algorithms we are able to click incredible images. They examine each pixel in provided image to find out objects, blur the background, and a complete host of tricks.
These machine learning algorithms do so many things to improvise the smartphone’s camera:
Object identification to find and single out the object(s) (or human) in the picture.
Fulfill the missing parts in the image.
Utilizing a specific type of neural network using GANs to improve the picture or broaden its limits by imagining what the image would look like, etc.
- Transportation Optimization – The applications of machine learning in the transport industries has gone completely to an entirely different level in the last decade.
- Transportation and Commuting – Ola
Dynamic valuing isn’t the main machine learning use case ride-hailing organizations like Ola use. They depend intensely on machine learning to recognize the most optimal route to get the traveler from point A to B. There are varity of machine learning techniques that focuses to optimize the route we take.
- Google Maps – Google uses a ton of machine learning algorithms to produce all these features that ahs given into Google map. Machine learning is deeply set into Google Maps that’s why the visibility of routes are getting smarter with each update.
- Popular Web Services –
- Email filtering – Machine learning are being used to review email’s subject line and categorize it accordingly so you get only those emails which is important to you and as per your convienance emails will come into your primary inbox. The machine learning algorithms quickly categorize the email into one of these three labels as soon as you receive an email i.e. Primary, Social, Promotions. We get a instant alert on the off chance that Gmail considers it a ‘primarily’ email.
- Google Search – Google search uses machine learning algorithm to analyze and understand the querries and likeness of users and as per these data Google algorithms shows the results and work.
- Google Translate – It won’t astonish you to realize that Google utilizes machine learning to understand the sentence sent by the client, and convert them to the mentioned language, and show the result. Machine learning is profoundly inserted in Google’s environment and we are largely profiting from that.
Luckily, we know how Google utilizes machine learning to control its Translate motor.
- Sales and Marketing –
- Personalized Marketing – Machine learning helps to address client fragments and tailor your marketing campaigns for those fragments. You can routinely check how your mission is doing through metrics like open rates, clickthrough rates, etc.
- Customer Support Queries – Machine learning algorithms identify the message and the fragment to redirect the query to the appropriate customer support person. They can then deal with the user accordingly.
- CyberSecurity –
- Video Surveillance – Organizations universally are using video surveillance for different tasks, such as recognizing intruders, detecting threats of violence, catching criminals, etc. All of this is not being done physically, however. That would be massively time taking. So instead, machine learning algorithms are being utilized for the software that is put inside these surveillance cameras. These machine learning algorithms use various PC vision techniques (like object identification) to recognize possible threats and nab criminals.
- Machine Learning Against Bots
- Financial Domain –
- Catching Fraud in Banking – Have you at any point been a victim of credit card extortion? It’s a difficult experience to go through. The shock of the extortion is exacerbated by how many desks work the bank requests that you finish up. Fortunately, machine learning is settling various layers of this process. From fraud detection to extortion counteraction, machine learning algorithms are changing the way banks work to improvise the customer’s experience. Machine learning has definitely helped streamline the process. These algorithms can recognize fraudulent transactions and flag them so the bank can connect with the customers ASAP to validate if they made the transaction or not.
- Financial Advisory and Portfolio Management – So many budget management application are now accessible in the market, and these have machine learning-based services. Eva Money by Fintel Labs is one such innovative application for iOS and Android platforms. These applications utilize machine learning algorithms to allow the customers to keep track of their expenses, decide the spending patterns, give suggestions on better savings, and likewise. Robo-advisors is one of the latest trends for this machine learning use case. These are not the robots but the machine learning algorithms that tweak the financial portfolio according to income, risk resistance, and preferences. ML algorithms also shows suggestions on better trading, investments, saving schemes, etc.
- Healthcare –
- Skin Cancer Diagnosis – Convolutional Neural Network algorithms are broadly utilized in the healthcare sector to identify and arrange pictures. Healthcare is one field that has no room for mistakes. It is important for a system or a technology to give high levels of accuracy and validity in the outcomes. CNN’s are effective in skin cancer identification with high accuracy rates of up to 95% utilizing TensorFlow. Scikit-learn and Keras are other machine learning tools helpful in examining and identifying skin cancer utilizing the CNN procedure. Manual attempts and processes in the same method can have a maximum accuracy of 85%.
These ML models use hundreds and thousands of pictures of harmless and dangerous skin sores to give the results.
- Covid-19 Mortality Risk Predictor – Machine Learning strategies can be essentially useful in pandemic management. Coronavirus mortality risk indicator is one such machine learning use case in medical services. Timely forecast of patient mortality risk can cut down mortality with effective asset allocation and treatment.
Support Vector Machines, SVMs are machine learning algorithms that can be utilized for predictive modeling utilizing invasive research centers and noninvasive clinical data of the patients. Non-invasive features, for example, blood oxygen levels, patient age, previous medical conditions, etc., can be taken care of by the machine learning models to yield accurate forecasts.
Compare Artificial Intelligence Vs machine learning Certification
|ARTIFICIAL INTELLIGENCE||MACHINE LEARNING|
|AI stands for Artificial intelligence, where intelligence is defined as a ability to obtain and apply knowledge.||ML stands for Machine Learning which is defined as the acquisition of knowledge or skill from analysis data.|
|The goal of AI is to increase the chance of success.||The goal is to enhance accuracy, but it does not care about success.|
|It work as a computer program that does smart work better than human.||Here, machine learning works as a brain to AI that learns from data.|
|The aim is to copy natural intelligence to solve complex problems like human or better than human.||The aim is to learn from data on certain task to maximize the level of performance on that task.|
|AI is decision making.|
ML helps AI to learn new things from data.
|It’s a system which mimics humans to solve problems whether its a difficult or easy.||It comprises in creating self learning algorithms.|
|AI will go for finding the best solution.|
ML will go for solution whether it is best or not.
|AI leads to intelligence or wisdom.||ML leads to knowledge.|
List of Machine Learning Certification
- Machine Learning with TensorFlow on Google Cloud Platform Specialization
- AWS Certified Machine Learning-Specialty
- Master in Machine Learning Course-DevOpsSchool
- Microsoft Certified: Azure Data Scientist Associate
- Machine Learning with Python
- Machine Learning Stanford Online
- Machine Learning at Udacity
- Harvard University Machine Learning
Machine Learning Certification Path
All Toolsets of machine learning are –
- Scikit Learn
- Apache Mahout
- Rapid Miner
Machine Learning Certification Cost
- AWS Certified Machine Learning-Specialty – 300 USD
- Master in Machine Learning Course-DevOpsSchool – Rs 49,999
- Machine Learning Stanford Online – $5040
Best salary for Machine Learning Certified Professional
- Average salary is ₹671,548 per year, and with less than 1-year experience machine learning engineers earns around ₹500,000 per annum which is clearly one of the highest entry-level salaries in India.
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