Here is an excellent learning path published on the Analyticsvidhya site by the author NSS:
Here is an interesting article on AnalyticsVidhya which talks about the skills in demand and the salaries they could command. This has more to do with the Indian market scenario.
Kunal Jain is the original author of this article.
Here is a very interesting salary survey about professionals working in the field of Data Science:
Here are some insights from this survey. The list of important tasks done by a typical data scientist on his job are as below. We need to ignore Cluster 1 as that subset represents non-data scientists:
As you can see in the above chart the most significant activities that a Data Scientist involves himself is in Exploratory Data Analysis/EDA, Data Cleaning/Wrangling, Feature Extraction, Creating Visualization, Conducting data analysis to answer research questions, Developing Prototype models, Implementing models/algorithms into production and finally Communicating findings to business decision-makers
Predominant tools include Python, SQL, Excel, R, Scikit-learn, Matplotlib and ggplot.
Here is an interesting article on the ML patterns that have been there for a while – and the probable way forward:
What will it take for AI to become mainstream in business? The convergence of different research approaches—and lots of human intelligence.
We’re in the midst of a breakthrough decade for artificial intelligence (AI): More sophisticated neural networks paired with sufficient voice recognition training data brought Amazon Echo and Google Home into scores of households. Deep learning’s improved accuracy in image, voice, and other pattern recognition have made Bing Translator and Google Translate go-to services. And enhancements in image recognition have made Facebook Picture Search and the AI in Google Photos possible. Collectively, these have put machine recognition capabilities in the hands of consumers in a big way.
What will it take to make similar inroads in business? Quality training data, digital data processing, and data science expertise. It will also require a lot of human intelligence, such as language-savvy domain experts who refine computable, logically consistent business context to allow logical reasoning. Business leaders will have to take the time to teach machines and incorporate machine intelligence into more processes, starting with narrow domains.
Some in the statistically oriented machine learning research “tribes”—the Connectionists, the Bayesians and the Analogizers, for example —will worry that “human-in-the-loop” methods advocated by the Symbolists aren’t scalable. However, we expect these human-to-machine feedback loops, that blend methods of several tribes, will become a lot more common inside the enterprise over the next few years.
See what that evolution might look like below. For an overview of machine learning, see the first infographic in our series. And for a better understanding of how its algorithms are used, see our machine learning methods infographic.
The article is by Alan Morrison and Anand Rao and the source is http://usblogs.pwc.com/emerging-technology/machine-learning-evolution-infographic/
Dave Burke, VP of engineering at Google, announced a new version of Tensorflow optimised for mobile phones.
This new library, called Tensorflow Lite, would enable developers to run their artificial intelligence applications in real time on the phones of users. According to Burke, the library is designed to be fast and small while still enabling state-of-the-art techniques. It will be released later this year as part of the open source Tensorflow project.
At the moment, most artificial intelligence processing happens on servers of software as a service providers. By making this library available Google hopes to offload some of these processes to the phone of the user. This would save both processing power and data. It would also ensure that the data of the user remains private, and would not require an internet connection anymore.
Tensorflow Lite is the second deep learning tool that will become available on mobile phones. In November 2016 Facebook already announced its own framework: Caffe2Go. This framework has been used for real time style-transfer: adding art-like filters to your mobile phone.
Burke also announced that he and his team are adding functionality to communicate with processors designed for neural networks. These networks use a lot of the same type of arithmetic, which can be optimised using GPUs and Googles own Tensor Processing Unit (TPU). At the moment phones are already using neural networks, for example in the Google Translate app. Training these networks is still too heavy for mobile phones.
Here is an interesting article on “Evolution Strategies” as a scalable alternative to Reinforced Learning. A new field beyond the traditional modes of Structured, Unstructured and Reinforcement Learning.