Introduction to Machine Learning | Tutorial | Applications
Last updated on Wed 18 Mar 2020
Overview of Machine Learning and know few facts about Machine Learning, it's applications and advantages. This tutorial gives you the steps to lead success in machine learning.
What is Machine Learning?
Machine Learning is a field of artificial intelligence (AI) where the systems will be given the ability to learn things automatically and make decisions with very less human intervention.
Python, Java, and R are the most popular skills when it comes to machine learning and data science jobs.
These are the industries where machine learning is making a revolution:
Education – This is going to bring a revolution in the education industry and the methods of teaching. Machine Learning makes it possible to observe every student and tailor the teaching methods according to the individual. Devices like Smart Whiteboards have already made their way into this field.
Transportation – Self Driving Cars is the word that comes to our mind when we think of AI in transportation. Ride-sharing giant, Uber has already rolled out self-driving cars but with some defects which usually happens and is putting a great deal of effort in improvising it. Machine Learning/AI is going to affect many transport companies in the near future.
Health Care – Machine Learning can be used in the diagnosis and treatment of illness. Although robot surgeons operated remotely by human surgeons are introduced some time back, it still needs to go much further. Introduction of robot surgeons can minimize the work and with the big data being given to the robots, it would be easy to perform some operation based on the similar incidents that occurred past.
Business and Marketing – Many Social media sites and e-commerce giants like Facebook, Amazon, etc. have already been using AI algorithms to detect consumer behavior and match content with their interest and needs. Most of us are familiar with Google’s AI, which is using Machine Learning to constantly improve search results.
The chatbot is another example which is reducing human work. It sends automated messages. Most of the service-based companies are now taking to chatbots reducing the number of human labors.
Financial Services – Clinic's Finnie app will provide you all the financial advice and answers in the banking industry. This app is regarded as one of the most advanced intelligent assistant technologies in the banking industry. If we talk about AI in financial services, then we must know about a clinic which has been contributing a lot to the financial industry.
Feedzai is another platform which helps in fraud detection. Oh! This is incredible. It watches millions of unusual transactional behavior and spending patterns.
Many complex algorithms are being used in the banking industry that acts as financial advisories taking many factors into consideration, thus replacing the physical advisories.
I’ve mentioned only very few applications. Apart from the above, there is a lot to come around in the near future.
There is no need to open our jaw even if we say Machine Learning can change our lifestyle in the near future.
Few Facts about machine learning:
Do you know?
- 20 percent of the C-Suite (across 10 countries and 14 different industries) is using machine learning
- Investment in AI by different countries is disproportionate; the U.S is the major investor as of now.
- Google’s Deep Learning can benefit health and save billions.
- Chatbots will power 85 percent of customer service by 2020
- €150 billion saved on healthcare by 2025
- Amazon has reduced ‘click to ship’ time down to 15 minutes from around 75-90 minutes.
- Internet-based Entertainment Company; Netflix is investing heavily to keep users engaged.
Above all Machine learning still requires humans.
- The operating speed of Machine learning algorithms is very fast.
- Machine learning consumes and produces real-time data at incredible speeds. For instance, in the case of grocery and department stores, machine learning customizes new offers very fast which means what it shows now would be different after 1 hr.
- Some advertising platforms like Google and Facebook are using machine learning technology to grasp users’ internet behavior and tailor ads according to their search history.
- Machine learning efficiently uses the resources that include some resources which lead to automation of tasks.
- We cannot say that machine learning algorithms always work in every single case.
- A major challenge of machine learning is Acquisition. Since a large amount of data is required to get the precise solution, and input data is different for different methods, caution to be taken while giving data as input before processing. It would lead to failure otherwise.
- Natural Language processing is another major hurdle here. Although research is under process, it has to reach far more beyond.
- McAfee said that the actual problem comes when machine learning commits errors. It would be very difficult to diagnose and correct the error since it has to go through many complexities.
Top companies using Machine Learning / AI
10 Steps that lead to success in machine learning
- The very first step is to understand what Machine Learning actually is.
- The more data an algorithm has, the more accurate it becomes. So, collect more data and avoid sampling if possible.
- Selecting the best method from machine learning for a given problem will determine our success in most cases.
- Improper data collection will decrease the chance of building good generalizable machine learning models.
- Choosing a method and the parameters related to it also plays a vital role.
- Selecting the appropriate objective function for optimization also plays a key step in success.
- Understand the generalization (how good a model performs on unseen data) error before deployment.
- Should constantly measure the effectiveness of the implementation and should note the changes that affect the system in a good or bad way.
- Should know how to handle semi-structured and unstructured data, such as text, images, etc.
- Don’t just depend on one tool but try different and use a few tools.