Recap of Machine Learning News for May 2018

Recap of Machine Learning News for May 2018

Machine Learning News for May 2018

Machine Learning News 2018

ML Kit: Google brings machine learning APIs to mobile developers.,May 8 2018

Google has released a machine learning Kit which is in its beta stage. It currently supports iOS and Android through Firebase IDE and TensorFlow Lite. Using this ML Kit, developers can leverage the power of Google’s machine learning capabilities in their mobile apps. Following are some of the API’s available in ML Kit

1) Barcode Scanning

2) Text Recognition

3) Face Detection

4) Image Labeling to identify objects

5) Landmark Detection.

(Source- )

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Nearly 18,000 Competitors Convene in Fourth Data Science Bowl, Using AI To Accelerate Life-Saving Medical Research., May 8, 2018

Data Science Bowl is a 90 day competition where data scientists come together to solve some of the most challenging problems of the world. This year Data Science Bowl hosted 18,000 participants across the globe who worked 288,000 hours and submitted 68,000 algorithms to automate the detection of nuclei which is a time consuming but vital process. Detection of nuclei will allow researchers to locate each cell in the sample and how they respond to numerous treatments. With the help of these algorithms biologists can focus on many other aspects of their research. This will also help in developing new drugs which currently take around 10 years to be released in the market.


Businesses To Double Their Use Of Machine Learning By, May 13, 2018

Machine Learning technologies usage is going to increase 2 times by end of 2018 as predicted by Deloitte Global’ s Technology, Media and Telecommunication. Based on the same report, there are also predictions that use of new AI enabled chips in smartphones will increase the use of machine learning, which will boost processing power with less consumption of battery.There will be over $545 billion revenue generation from live and broadcast events as per Deloitte Global predictions. It also mentions that 50% of the adults in developed countries will have more than 1 online media subscriptions by the end of 2018 and this number will be doubled by the end of 2020.

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 Machine Learning Projects

Nextiva’s NextOS platform uses AI and Machine Learning to simplify business communications,, May 14, 2018

A leading communication company- Nextiva, has launched a new platform NextOS, that will harness the power of AI and Machine Learning for providing a holistic view of their customers communications across all segments. As of now, there is no product in the market which can provide this kind of information and according to a survey 50% of employees consider quitting their jobs due to these challenges and if platform like NextOS becomes successful, it could be a game changer in this segment. The most powerful part of NextOS is its use of AI in creating and automating work flows and building models around the data. With the launch of this platform, Nextiva has really expanded its base from a phone service-centric solution to a full-fledged suite of software which will provide a 360o view it customers data in very user-friendly way.

(Source -

Machine learning algorithm is claimed to predict which students will drop, May 14,2018

Machine Learning is going to change the whole world with its use in most challenging problems of the world. The advancement in ML has created many different fields where it can be applicable. One such unpredictable area where ML has been applied recently was in predicting the chances of students drop out from university in New Zealand and Australia. Data scientists of Jade Software used 15 years of students’ data like age, enrolment history, ethnicity, residence from campus, gpa and payment of studies and applied various tools of machine learning and built a tool in just 4 weeks as a research project. After various iterations, the machine learning algorithm was able to model data with 92 percent accuracy. Now, if the tool is fed with current student data, it can predict the probability of students dropping out, which is a very useful piece of information for universities, as they can focus more on those students or group of students.


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Relevant Projects

Build an Image Classifier for Plant Species Identification
In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.

Ensemble Machine Learning Project - All State Insurance Claims Severity Prediction
In this ensemble machine learning project, we will predict what kind of claims an insurance company will get. This is implemented in python using ensemble machine learning algorithms.

Customer Market Basket Analysis using Apriori and Fpgrowth algorithms
In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.

Sequence Classification with LSTM RNN in Python with Keras
In this project, we are going to work on Sequence to Sequence Prediction using IMDB Movie Review Dataset​ using Keras in Python.

Natural language processing Chatbot application using NLTK for text classification
In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.

Identifying Product Bundles from Sales Data Using R Language
In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.

Data Science Project in Python on BigMart Sales Prediction
The goal of this data science project is to build a predictive model and find out the sales of each product at a given Big Mart store.

Machine Learning or Predictive Models in IoT - Energy Prediction Use Case
In this machine learning and IoT project, we are going to test out the experimental data using various predictive models and train the models and break the energy usage.

Learn to prepare data for your next machine learning project
Text data requires special preparation before you can start using it for any machine learning project.In this ML project, you will learn about applying Machine Learning models to create classifiers and learn how to make sense of textual data.

Ecommerce product reviews - Pairwise ranking and sentiment analysis
This project analyzes a dataset containing ecommerce product reviews. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Reviews play a key role in product recommendation systems.