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Day: April 18, 2018

6 Ways in Which Artificial Intelligence is Revolutionizing the Real Estate Business

Posted on by Imarticus Learning

Real estate is not simply buying and selling lands and houses – there is a whole, large industry dedicated behind those acts. It is a given that the age of technology has also affected something that seems so analogous, so much that a huge part of that process has also digitised today. Artificial Intelligence is currently playing a large role in making the work of the decision-makers in the industry easier – let us take a look at how AI plays a huge role.

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A large problem for real estate agents was the act of finding or recommending the perfect plot of real estate for a customer. This might not have been clear even after lengthy talks on the subject, but AI has been making that much easier. Since machine learning and AI feeds on data, the larger the amount of data, the better – they can now find the perfect piece of land.

Availability

The making and use of chatbots have now become an established part of many data science courses, and these can help real estate developers too. Chatbots can be used to learn about customer preference round the clock, and customers now have something to answer their simple questions and grievances at any time of the day.

 Prediction

AI is also able to make a large number of meaningful predictions, from the data provided to it. This can be beneficial in various models, like tracing the price of a particular plot or the changes in customer preferences.

 Automation

Real estate is an extremely documentation-heavy field, so it can be a chore to carry out all the documentation constantly. In this work, the use of AI could lead to quicker uploading and scanning of jobs and documents.

Virtual Tours

Another one of the path breaking implementations, people can now visit any plot of land on offer from any point in the world – virtually, of course. Virtual tours of the homes in question are truly making a huge difference in customer satisfaction and business development.

Disaster Management

Using AI systems, it is now possible for planners to run simulations and build proper disaster-resistant houses so that such natural disasters are mitigated when the inevitable occurs.
If you want to learn more about such important tools like AI or business analytics, you should definitely check out the business analytics training courses offered by Imarticus Learning!

What is The Easiest Way To Learn Machine Learning?

Posted on by Imarticus Learning

Machine Learning is applied to enable machines to process and make decisions by figuring patterns without explicit programming. This can be achieved via multiple techniques one of them is through training machines on a large dataset called training dataset that is used to create models to help machines in making decisions when exposed to real-time data.

There is no shortcut to learning, and when it comes to Machine Learning the process is definitely not quick but if you are inclined to Artificial Intelligence then there is a smarter way of ensuring quality learning with little investment.

Also Read: Future of Machine Learning in India

Machine Learning is about optimization and to optimize data mining learners should have a decent level of programming knowledge and skills. There are many languages that provide Machine Learning capabilities and there are various online courses available to learn them, but it is imperative to choose a language you already have some background with to make sure you pick up fast.

Python is easy to learn and is optimal for data manipulation and repeated tasks while R caret is a little elusive but is good for ad-hoc analysis and exploring datasets.


Before you really embark on your journey to become a Machine Learning specialist you need to understand the concepts of Machine Learning and invest in the theory of it via specific online courses like Machine Learning course from Andrew Ng and Learning from Data course by Prof. Yaser Abu-Mostafa.

Learning from videos has proven to be more efficient and quick, although the power of books should never be undermined since in this article our focus is to make learning quicker I recommend videos and slideshows over books and papers.
As you acquire deeper knowledge of Machine Learning you would come across various Machine Learning algorithms, these are broadly classified into three categories based on the amount of “feedback” provided to a system to enforce learning, these categories are:

1.) Supervised Learning.
2.) Un-Supervised Learning.
3.) Reinforcement Learning.

To acquire a better understanding of these algorithms you need to have the fundamental knowledge of Linear algebra, Probability theory, Optimization, Calculus and Multivariable calculus etc.

Machine Learning works on raw unstructured big data so it is important for you to understand data statistics including descriptive and inferential statistics.
You also need to have a deep understanding of various Data Cleaning techniques and different stages of data explorations to deal with a large number of unstructured data bits. Most of the times Machine Learning systems need to process incomplete or damaged/scrambled data, for such scenarios handy knowledge of techniques like Variable Identification, Univariate and Multivariate analysis, Missing values treatment, Outlier treatment becomes very useful.
Once you have undergone the basic courses for Machine Learning foundation building it is time to practice what you have learned, Kaggle Knowledge competition is a good place to start. By experimenting more you can polish your skills well and know your level, and shortcomings on which you can work on. Popular Machine Learning communities to help you further in learning are as follows:

  1. https://machinelearningmastery.com/
  2. https://stats.stackexchange.com/
  3. https://www.reddit.com/r/MachineLearning/
  4. https://www.reddit.com/r/datascience/

 
Related Article: What are The Skills You Need to Become a Machine Learning Engineer?