Machine Learning for Beginners- Part 2

Machine learning is the most trending topic in the information technology sector. This technology allows computers to access hidden insights and predict outcomes which leads to remarkable changes to businesses. In my last article, I tried to draw a basic outline of machine learning concept. In this article, I’ve discussed different types of machine learning techniques and some use cases as promised earlier.

Machine learning is the most trending topic in the information technology sector. This technology allows computers to access hidden insights and predict outcomes which leads to remarkable changes to businesses. In my last article, I tried to draw a basic outline of machine learning concept. In this article, I’ve discussed different types of machine learning techniques and some use cases as promised earlier.

So what are the different machine learning techniques?

Well, there are varieties of machine learning techniques. But in this blog let me discuss the  two very basic processes of machine learning approaches.

  1. Supervised Learning – Guided Training
  2. Unsupervised Learning – Self-sufficient in learning

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.

Supervised learning uses classification and regression techniques to develop predictive models.

Classification techniques predict discrete responses—for example, whether an email is genuine or spam, or whether a tumor is cancerous or benign. Classification models classify input data into categories. Typical applications include speech recognition, medical imaging, credit scoring etc.

Some common algorithms for performing classification: Support vector machine (SVM), boosted and bagged decision trees, k-nearest neighbor, Naïve Bayes, discriminant analysis, logistic regression, neural networks etc.

Regression techniques predict continuous responses—for example, changes in temperature or fluctuations in power demand. Typical applications include electricity load forecasting and algorithmic trading.

Some common regression algorithms: Linear model, nonlinear model, regularization, stepwise regression, boosted and bagged decision trees, neural networks, adaptive neuro-fuzzy learning etc.

Unsupervised Learning

In unsupervised learning, the model learns through observation and finds structures 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 cats or dogs, but it will separate all the cats from the dogs.

Clustering is the most common unsupervised learning technique. It is used for exploratory data analysis to find hidden patterns or groupings in data. Applications for cluster analysis include gene sequence analysis, market research, object recognition etc.  

Some common algorithms for performing clustering: k-means and k-medoids, Gaussian mixture models, hierarchical clustering, hidden Markov models, self-organizing maps, fuzzy c-means clustering, subtractive clustering etc.

Machine Learning Use Cases

Nowadays, most of the industries working with large amounts of data have recognized the value of machine learning technology. By gleaning insights from this data – often in real time – organizations are able to work more efficiently or gain an advantage over competitors. Here are some real-life examples.

Financial services

Banks and other fin-industry use machine learning technology for two key purposes:

  1. To identify important insights into data &
  2. To prevent fraud.

Data mining can also identify clients with high-risk profiles, or use cyber surveillance to pinpoint fraud.

Healthcare

Machine learning is already a trend in the healthcare industry, thanks to the advent of wearable devices and sensors. It benefits healthcare sector in many ways. Here are some examples,

  1. By checking patients health in real time
  2. By analyzing data for identifying trends or red flags that may lead to improve diagnosis & treatment

Drug discovery, robotic surgery are also some of the advanced area of machine learning in this sector. Yet many more approaches to come in the near future.

Government

Government agencies, such as public safety and utilities have a particular need for machine learning since they have multiple sources of data that can be mined for insights. Like

  1. Analyzing sensor data, for example, identifies ways to increase efficiency and save money.
  2. Machine learning can also help detect fraud and minimize identity theft.

Besides, it can also help to maintain the transparency of educational result. It can also prevent multiple voting by the same person at the time of election.

Marketing and sales

A few very important machine learning based marketing & sales benefits are the following-

  1. Recommend buyers similar products according to their previous purchase history in ecommerce sector.
  2. Help businesses to identify their potential customers for email campaign.
  3. Generate pay per click campaigns.

Overall, machine learning helps to increase the targeted customer outreach, improve the relevance of the audience, trigger a response or action, and create a great user experience for any business.

Transportation

Analyzing data to identify patterns and trends is the key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. Monitoring and managing transportation system helps this sector in following ways-

  1. Freight transportation operations management
  2. Air traffic control
  3. Monitoring and managing transportation system performance

The data analysis and modeling aspects of machine learning are also important tools for delivery companies, public transportation, and other transportation organizations.

Oil and gas

Machine learning helps oil & gas industry by-

1.Finding new energy sources

  1. Analyzing minerals in the ground
  2. Predicting refinery sensor failure, streamlining oil distribution to make it more efficient and cost-effective

These are some areas of using machine learning from where oil and gas industries are benefited. The number of machine learning use cases for this industry is vast and still expanding.

These are just few examples of industry-specific machine learning uses . So give you a simple answer, the scope of machine learning in the future is only limited by our imagination. Things that we once saw in movies and our wildest dreams become likely, thanks to machine learning.

And now, here is a complementary addition to this blog, I am adding a flowchart of nine easy steps to learn machine learning technology.

I hope this would help you to understand the basics of Machine Learning and let me know what you think about the future of machine learning and what’s your plan to add up on this technology.

Cheers!!!



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