Use-cases of Machine Learning in E-Commerce

Use Cases of Machine Learning in E-Commerce
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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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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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 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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Machine Learning with Mahout

[This blog is from KnowBigData.com. It is pretty old. Many things have changed since then. People have moved to MLLib. We have also moved to CloudxLab.com.]

What is Machine Learning?

Machine Learning is programming computers to optimize a Performance using example data or past experience, it is a branch of Artificial Intelligence.

Types of Machine Learning

Machine learning is broadly categorized into three buckets:

  • Supervised Learning – Using Labeled training data, to create a classifier that can predict the output for unseen inputs.
  • Unsupervised Learning – Using Unlabeled training data to create a function that can predict the output.
  • Semi-Supervised Learning – Make use of unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data.

Machine Learning Applications

  • Recommend Friends, Dates, Products to end-user.
  • Classify content into pre-defined groups.
  • Find Similar content based on Object Properties.
  • Identify key topics in large Collections of Text.
  • Detect Anomalies within given data.
  • Ranking Search Results with User Feedback Learning.
  • Classifying DNA sequences.
  • Sentiment Analysis/ Opinion Mining
  • Computer Vision.
  • Natural Language Processing,
  • BioInformatics.
  • Speech and HandWriting Recognition.

Mahout

Mahout – Keeper/Driver of Elephants. Mahout is a Scalable Machine Learning Library built on Hadoop, written in Java and its Driven by Ng et al.’s paper “MapReduce for Machine Learning on Multicore”. Development of Mahout Started as a Lucene sub-project and it became Apache TLP in Apr’10.

Topics Covered

  • Introduction to Machine Learning and Mahout
  • Machine Learning- Types
  • Machine Learning- Applications
  • Machine Learning- Tools
  • Mahout – Recommendation Example
  • Mahout – Use Cases
  • Mahout Live Example
  • Mahout – Other Recommender Algos

Machine Learning with Mahout Presentation

Machine Learning with Mahout Videohttps://www.youtube.com/embed/PZsTLIlSZhI

AutoQuiz: Generating ‘Fill in the Blank’ Type Questions with NLP

Can a machine create quiz which is good enough for testing a person’s knowledge of a subject?

So, last Friday, we wrote a program which can create simple ‘Fill in the blank’ type questions based on any valid English text.

This program basically figures out sentences in a text and then for each sentence it would first try to delete a proper noun and if there is no proper noun, it deletes a noun.

We are using textblob which is basically a wrapper over NLTK – The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing for English written in the Python programming language.

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