Use-cases of Machine Learning in E-Commerce

What computing did to the usual industry earlier, Machine Learning is doing the same to usual rule-based computing now. It is eating the market of the same. Earlier, in organizations, there used to be separate groups for Image Processing, Audio Processing, Analytics and Predictions. Now, these groups are merged because machine learning is basically overlapping with every domain of computing. Let us discuss how machine learning is impacting e-commerce in particular.

The first use case of Machine Learning that became really popular was Amazon Recommendations. Afterwards, the Netflix launched a challenge of Movie Recommendations which gave birth to Kaggle, now an online platform of various machine learning challenges.

Before I dive deep into the details further, lets quickly brief the terms that are found often confusing. AI stands for Artificial Intelligence which means being able to display human-like intelligence. AI is basically an objective. Machine learning is making computers learn based on historical or empirical data instead of explicitly writing the rules. Artificial Neural networks are the computing constructs designed on a similar structure like the animal brain. Deep Learning is a branch of machine learning where we use a complex Artificial Neural network for predictions.

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What are the pre-requisites to learn big data?

Pre-requisites for Big Data Hadoop

We, at CloudxLab, keep getting a lot of questions online, sometimes offline, asking us

“I want to learn big data. But, just don’t know whether I am eligible or not.”

“I am so and so, can I learn big data?”

We have compiled the most common questions here. And, we will answer each one of them.

So, here we go.

What are those questions?

  1. I am from a non-technical background. Can I learn big data?
  2. Do I need to know programming languages such as Java, Python, PHP, etc.?
  3. Or, since it is big data, do I need to know any other relational databases such as Oracle or in general do I need to be well versed with SQL?
  4. And also, do I need to know the Unix or Linux?

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Top Machine Learning Interview Questions for 2018 (Part-1)

 

These Machine Learning Interview Questions, are the real questions that are asked in the top interviews.

For hiring machine learning engineers or data scientists, the typical process has multiple rounds.

  1. A basic screening round – The objective is to check the minimum fitness in this round.
  2. Algorithm Design Round – Some companies have this round but most don’t. This involves checking the coding / algorithmic skills of the interviewee.
  3. ML Case Study – In this round, you are given a case study problem of machine learning on the lines of Kaggle. You have to solve it in an hour.
  4. Bar Raiser / Hiring Manager  – This interview is generally with the most senior person in the team or a very senior person from another team (at Amazon it is called Bar raiser round) who will check if the candidate fits in the company-wide technical capabilities. This is generally the last round.

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Financial Aid, Scholarship Test & Free Resources

Financial Aid

At CloudxLab, we have always believed in quality education must be affordable for everyone so that we can help learners achieving career goals and build innovative products.

If you can’t afford to pay for a course, you can apply for financial aid using this form. Learners with Financial Aid in a course will be able to access all of the course content and complete all work required to earn a certificate. Financial Aid only applies to the course that the Financial Aid application was approved for. Most courses offer Financial Aid, but Financial Aid may not be available for certain courses. It will take a minimum of 7 days for us to review your financial aid application. When your application is reviewed, you’ll get an email letting you know whether it’s been approved or denied.

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Scholarship Test for Machine Learning Course

After receiving a huge response in our last scholarship test, we are once again back with a basic conceptual test to attain scholarship for our upcoming Specialization course on Machine Learning and Deep Learning.

Concepts to be tested: Linear algebra, probability theory, statistics, multivariable calculus, algorithms and complexity, aptitude and Data Interpretation.

  • Date and Time: September 2, 2018, 8:00 am PDT (8:30 pm IST)
  • Type: objective (MCQ)
  • Number of questions: 25
  • Duration: 90 minutes
  • Mode: Online

If you have a good aptitude and general problem-solving skills, this test is for you. So, go ahead and earn what you deserve.

If you have any questions on the test or if anything else comes up, just click here to let us know. We’re always happy to help.

 

How to Teach Online Effectively

 

I founded KnowBigData.com in 2014 after working in Amazon. Teaching is my passion, and technology, specifically large-scale computing my forte, thanks to my working experience with Amazon, InMobi, D. E. Shaw and my own startup tBits Global. Therefore, I wanted to help people learn technology online. I launched KnowBigData.com, an online instructor-led training on MongoDB followed by Big Data and Machine learning. Eventually, we innovated a lot in learning and shaped KnowBigData into Cloudxlab.com which is currently a major gamified learning environment for Machine Learning, AI, and Big Data.

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How To Optimise A Neural Network?

When we are solving an industry problem involving neural networks, very often we end up with bad performance. Here are some suggestions on what should be done in order to improve the performance.

Is your model underfitting or overfitting?

You must break down the input data set into two parts – training and test. The general practice is to have 80% for training and 20% for testing.

You should train your neural network with the training set and test with the testing set. This sounds like common sense but we often skip it.

Compare the performance (MSE in case of regression and accuracy/f1/recall/precision in case of classification) of your model with the training set and with the test set.

If it is performing badly for both test and training it is underfitting and if it is performing great for the training set but not test set, it is overfitting.

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10 Things to Look for When Choosing a Big Data course / Institute

Every now and then, I keep seeing a new company coming up with Hadoop classes/courses. Also, my friends keep asking me which of these courses is good to take. I gave them a few tips to choose the best course suitable for them. Here are the few tips to decide which course you should attend to:

1. Does the instructor have domain expertise?

Know your instructor. You must know about the instructor’s background. Has (s)he done any big data related work? I have seen a lot of instructors who just attend a course somewhere and become instructors.

If the instructor never worked in the domain, do not take such classes. Also, avoid training institutes that do not tell you details about the instructor.

2. Is the instructor hands on? When did she/he code last time?

In the domain of technology, there is a humongous difference between one instructor who is hands-on in coding and another who is delivering based on theoretical knowledge. Also, know when the instructor worked on codes the last time. If instructor never coded, do not attend the class.

3. Does the instructor encourage & answer your questions?

There are many recorded free videos available across the internet. The only reason you would go for live classes would be to get your questions answered and doubts cleared immediately.

If the instructor does not encourage questions and answers, such classes are fairly useless.

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6 Reasons Why Big Data Career is a Smart Choice

Confused whether to take up a career in Big Data or not? Planning to invest your time in getting certified and to acquire expertise in related frameworks like Hadoop, Spark etc. and worried whether you are making a huge mistake? Just spend a few minutes reading this blog and you will get six reasons on why you are making a smart choice by selecting a career in big data.

Why Big Data?

There are several people out there who believe that Big Data is the next big thing which would help companies to spring up above others and help them position themselves as the best in class in their respective sectors.

Companies these days generate a gigantic amount of information irrespective of which industry they belong to and there is a need to store these data which are being generated so that they can be processed and not miss out on important information which could lead to a new breakthrough in their respective sector.  Atul Butte, of Stanford School of Medicine, has stressed the importance of data by saying “Hiding within those mounds of data is the knowledge that could change the life of a patient, or change the world”. And this is where Big Data analytics play a very crucial role.

With the use of Big Data platforms, a gigantic amount of data can be brought together and be processed to develop patterns which would help the company in making better decisions which would help them to grow, increase their productivity and to help create value to their products and services.

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One Day Machine Learning Bootcamp at IITB

Our past two Bootcamp on Machine Learning at National Singapore University and RV College of Engineering were very interesting and all the attendees found it very useful. These feedbacks prompted us to have more Bootcamps like these.

Thanks to Prof. Alankar, who invited us to conduct yet another Machine Learning Bootcamp at Indian Institute of Technology, Bombay. Before we move on to the details of Bootcamp, let us give you a brief introduction to Prof. Alankar. He is an Assistant Professor at IIT Bombay in Mechanical Engineering Department and works in the area of Multiscale Modeling of Deformation. He is a graduate of IIT Roorkee, holds a masters degree from University of British Columbia (Canada) and doctoral degree from Washington State University (USA). He has previously worked at Max-Planck Institute (Germany), Los Alamos National Laboratory (USA) and Modumetal, Inc (USA).

Machine Learning Bootcamp

So it all happened on Mar 17 where Machine Learning enthusiasts, which includes professors and students from every branch of IIT, gathered to attend the one day workshop on Machine Learning. The presenter was none other than Mr. Sandeep Giri, who has over 15 years of experience in the domain of Machine learning and Big Data technologies. He has worked in companies like Amazon, InMobi, and D. E. Shaw.

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