Artificial Intelligence as a Service – AIaaS

Suddenly, artificial intelligence is everywhere. Are you AI ready if not then be ready to be read in history books. Are we not missing the fact that artificial intelligence is about the people, not the machines. Technology and non technology companies are now investing and brining out the real and materialistic values of Artificial Intelligence to the real world. Its almost after a frustrating and hard work of decade AI has started delivering values. Using the contemporary view of computing exemplified by recent models and results from non-uniform complexity theory has proven the fact. Investment in artificial intelligence is growing fast. Tech giants like Google, Microsoft, Apple and Baidu known for their dominance in digital technologies globally are spending couple of tens of billions united state dollars on AI with 90 percent of this spent on R&D and deployment, and 10 percent on AI acquisitions. It takes money to make money and right now a lot of that money is going into the development of artificial intelligence. Any intelligence level surpassing the human intelligence is called the superintelligence level which is still 50 years plus ahead.


Top 10 Machine Learning Algorithms for Beginners

1. Linear Regression
2. Logistic Regression
3. CART
4. Naïve Bayes
5. KNN
6. Apriori
7. K-means
8. PCA
9. Bagging with Random Forests
10. Boosting with AdaBoost


5 Free Resources for Furthering Your Understanding of Deep Learning

This post includes 5 specific video-based options for furthering your understanding of neural networks and deep learning, collectively consisting of many, many hours of insights.


Practical Machine Learning with R and Python – Part 3

In this post ‘Practical Machine Learning with R and Python – Part 3’, I discuss ‘Feature Selection’ methods. This post is a continuation of my 2 earlier posts 1.Practical Machine Learning with R and Python – Part 1 2.Practical Machine Learning with R and Python – Part 2 While applying Machine Learning techniques, the data set will usually include a large number of predictors for a target variable. It is quite likely, that not all the predictors or feature variables will have an impact on the output. Hence it is becomes necessary to choose only those features which influence the output variable thus simplifying to a reduced feature set on which to train the ML model on.


Kubernetes Cookbook

Learn container orchestration with Kubernetes, featuring easy-to-use recipes for Kubernetes installation, API access, monitoring, troubleshooting, and more.
Advertisements