Revolutionary Machines

Machines ? Oh ! How ? Really ? Yes , Machine ! Machine Learning is the tool through which Machines became Intelligent. Now Do you know How Machine Learning revolutionised Industry ? 

What do machines need to learn ? Data. Now In this world of Big Data where even Peta bytes of Data is even less, How Machine learning revolutionised Data Science ? 

Someone will tell me Machine learning is part of Data Science! Yeah somewhat it is true but it actually isn’t true. Machine Learning is subset of Artificial Intelligence and yeah Deep learning is subset of Machine Learning so What about Data Science ? Data Science is multidisciplinary Field which intersect Artificial Intelligence , Machine learning and Deep Learning.

Due to Sudden rise in adoption of Machine learning , Data Science became sexiest field in Current.but still Now What’s next in Machine Learning ? How it will revolutionise Industry ?

1. Finding answer for “ How to train Machine learning Model from less Data ? “
2. Advancement in Feature Engineering 
3. Emphasising more on Quality of Data rather than Quantity.
4. Turning Data into information with less computational resources
5. Advanced Statistical Modelling with Differentially Private Machine Learning Algorithms
6. Enhancing Application Specific Approach for Statistical Modelling rather than conventional Optimistic approach for model selection.
7. Reinforcement Learning Integration with other Machine learning models for better efficiency and speed.
8. Application driven approach rather than Data intensive and Algorithmic perspective conventional approach.
9. Less Data Intensive training and testing using more robust boosting and Ensemble various models to make more accurate predictive analysis.
10. Heterogeneous Parallel Computing approach with Cloud Computing and Distributive environment.
11. Encourage use of GPU for Deep Networks in Deep Learning.
12. The fact that the model most often predicts the wrong thing to start with (mostly because for the ‘right’ thing you have no data) All the Data and Still Not Enough! 
13. Models being evaluated outside the context of their use. You want to evaluate how the outcome improves after you take some actions based on the predictions, not just the predictions.
14. Huge sampling problems relative to the problem one really needs to solve; people build models on the data they have, not on the data they should use and more problematically evaluate them on a highly non-representative sample.

I hope that machine learning will answer such problems and make Data Science more relevant to DATA itself !