MACHINE LEARNING AND ITS CASE STUDIES:-

What is Machine Learning?

Machine Learning is a branch of science that deals with programming the systems in such a way that they automatically learn and improve with experience. Here, learning means recognizing and understanding the input data and making wise decisions based on the supplied data.

ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives computer The Ability To Learn that makes it more similar to humans.

How Machine Learning is different from Traditional Programming?

Well, in Traditional Programming, we would feed the input data and a well written and tested program into a machine to generate output. When it comes to Machine Learning, input data along with the output associated with the data is fed into the machine during the learning phase, and it works out a program for itself.

History of Machine Learning:-

Machine Learning can automate many tasks, especially the ones that only humans can perform with their intelligence..

How does Machine Learning Work?

Machine Learning algorithm is trained using a training data set to create a model. When new input data is introduced to the ML algorithm, it makes a prediction on the basis of the model.

The prediction is evaluated for accuracy and if the accuracy is acceptable, the Machine Learning algorithm is deployed. If the accuracy is not acceptable, the Machine Learning algorithm is trained again and again with an augmented training data set.

This is just a very high-level example as there are many factors and other steps involved.

?Features of Machine Learning:-

• Automation: Nowadays in your Gmail account, there is a spam folder that contains all the spam emails. You might be wondering how does Gmail know that all these emails are spam? This is the work of Machine Learning. It recognises the spam emails and thus, it is easy to automate this process. The ability to automate repetitive tasks is one of the biggest characteristics of machine learning. 
• Improved Customer Experience: Have you ever noticed that whenever you open any shopping site or see any ads on the internet, they are mostly about something that you recently searched for? This is because machine learning has enabled us to make amazing recommendation systems that are accurate. They help us in customizing the user experience.
• Automated Data Visualization: As we all know that data is increasing day by day. Take an example of companies like Google, Twitter, Facebook. How much data are they generating per day? We can use this data and visualize the notable relationships, thus giving businesses the ability to make better decisions that can actually benefit both companies as well as customers.
• Business Intelligence: Machine learning characteristics, when merged with big data analytics can help companies to find solutions to the problems that can help the businesses to grow and generate more profit.

What is the best language for Machine Learning?

Although there are many languages that can be used for machine learning, according to me, Python is hands down the best programming language for Machine Learning applications. Other programming languages that could to use for Machine Learning Applications are R, C++, JavaScript, Java, C#, Julia, Shell, TypeScript, and Scala. R is also a really good language to get started with Machine Learning.

Types Of Machine Learning:-

 Supervised Learning – Train Me!

? Unsupervised Learning – I am self sufficient in learning!

? Reinforcement Learning – My life My rules! (Hit & Trial)

What is Supervised Learning?

Supervised Learning is the one, where you can consider the learning is guided by a teacher. We have a dataset which acts as a teacher and its role is to train the model or the machine. Once the model gets trained it can start making a prediction or decision when new data is given to it.

In Supervised Learning, we have two sets of variables. One is called the target variable, or labels (the variable we want to predict) and features(variables that help us to predict target variables). 

We can group the supervised learning problems as:

Regression problems – Used to predict future values and the model is trained with the historical data. E.g., Predicting the future price of a house.

Classification problems – Various labels train the algorithm to identify items within a specific category. E.g., Dog or cat( as mentioned in the above example), Apple or an orange, Beer or wine or water.

What is Unsupervised Learning?

This approach is the one where we have no target variables, and we have only the input variable(features) at hand. The algorithm learns by itself and discovers an impressive structure in the data. 

Once the model is given a dataset, it automatically finds patterns and relationships in the dataset by creating clusters in it. What it cannot do is add labels to the cluster, like it cannot say this a group of apples or mangoes, but it will separate all the apples from mangoes.

Suppose we presented images of apples, bananas and mangoes to the model, so what it does, based on some patterns and relationships it creates clusters and divides the dataset into those clusters. Now if a new data is fed to the model, it adds it to one of the created clusters.

We can group the unsupervised learning problems as:

Clustering: This means bundling the input variables with the same characteristics together. E.g., grouping users based on search history

Association: Here, we discover the rules that govern meaningful associations among the data set. E.g., People who watch ‘X’ will also watch ‘Y’.

What is Reinforcement Learning?

In this approach, machine learning models are trained to make a series of decisions based on the rewards and feedback they receive for their actions. It follows the concept of hit and trial method. The agent is rewarded or penalized with a point for a correct or a wrong answer, and on the basis of the positive reward points gained the model trains itself. And again once trained it gets ready to predict the new data presented to it. The machine learns to achieve a goal in complex and uncertain situations and is rewarded each time it achieves it during the learning period. 

Reinforcement learning is different from supervised learning in the sense that there is no answer available, so the reinforcement agent decides the steps to perform a task. The machine learns from its own experiences when there is no training data set present.

Top Machine Learning Use Cases – ML in Real life is no less than a dream!

 Google says that use cases mean, the specific situation in which a product or service could potentially be used. So, be it banking, energy, fin-tech, healthcare, insurance, marketing and public sector to name a few, everywhere machine learning is used. 

• “Hey Google/ Siri What’s the weather like today?”..
• Getting a cab is no more a task..
• Lunch time! A big thank you to online food delivery portals..
• Face unlock in smartphones- Faster than the speed of the light..
• Play Store Recommendations taking a toll..
• Explore the market with Google by your side..
• Spam Mail Alert..
• Help you network to the best, with the best..
• Personalized marketing..

Advantages and Disadvantages of Machine Learning:-

Every coin has two faces, each face has its own property and features. It’s time to uncover the faces of ML. A very powerful tool that holds the potential to revolutionize the way things work.

So, let’s discuss the Advantages and Disadvantages of Machine Learning.

Machine Learning Case Studies:-

Finding Other Planets in the Universe — Planetary Spectrum Generator:

I am sure I don’t need to tell you that the universe is huge! NASA believes that there are around 100 billion stars in the galaxy and out of them about 40 billion may have life. This is not science fiction, NASA actually believes we may find aliens one day! But for discovering aliens, NASA first needs to discover more and more new planets in different solar systems. Once these exoplanets are discovered, then NASA measures the atmospheric spectrum of these planets to find if there is any possibility of life.

While these steps are complicated enough, the problem is that there is no real data available for experimentation! So NASA scientists just generate the required data and that’s where Machine Learning comes in. The Planetary Spectrum Generator is a tool that NASA uses to create 3-D orbits and atmospheric properties of the exoplanets they find. To create a working model for the solar system, scientists use linear regression as well as convolutional neural networks. Then further fine-tuning is conducted on the model before it is ready for training.

The above image demonstrates the results generated for an exoplanet that demonstrate the amount of water and methane in the atmosphere. As you can see in the CH4 and H2O graph, the black lines denote the predictions that were made using Machine Learning and the red lines indicate the actual findings. As you can see the trained ML model is quite accurate in this situation!

Machine Learning Case Study on Yelp:

While Yelp might not seem to be a tech company at first glance, it is taking advantage of machine learning to improve users’ experience.

Yelp’s machine learning algorithms help the company’s human staff to collect, categorize, and label images more efficiently. Since images are almost as vital to Yelp as user reviews themselves, it is always trying to improve how it handles image processing. Through this, the company is serving millions of its users now.

For an entire generation today, taking photos of their food has become second nature and thanks to these people because of whom Yelp has such a huge database of photos. Its software uses techniques for analysis of the image to identify color, texture, and shape. It means that it can recognize the presence of say, pizzas, or whether a restaurant has outdoor seating.

As a result, the company is now able to predict attributes like ‘good for kids’ and ‘classy ambiance’ with more than 80% accuracy. It is also planning to use this information to auto-caption images and improve search recommendations in the future..

Megatrend of Machine Learning and Process Improvement 

Machine learning is very important and useful to Yelp, both on the consumer side – finding relevant businesses through reviews and encouraging useful reviews – and on the advertising side – displaying relevant ads to users – as most of their revenue is generated through advertising.

Yelp’s foray into machine learning was in 2015 with deep learning-power image analysis which identifies color, texture and shape of objects in user submitted photographs with 83% accuracy and uses the identifying traits to sort them into categories. Once the reviewers’ photographs have been categorized (broad categories such as food, drinks, menu, interior), Yelp has developed deep convolutional neural networks to recognize the classes and sort the photographs that are then displayed to users (see example in exhibit 1). Subsequently, Yelp expanded its machine learning to a custom ads platform whereby advertisers can opt to have “two step” AI system recommend photos and review content to use in banner ads targeting users. This machine learning system increased the rate people click on ads by at least 15%.

That's All About Machine Learning From My Side!!

Thanks For Reading!!!!