Why start with Weka over another tool like the R environment or Python for applied machine learning? In this post you will discover why Weka is the perfect platform for beginners interested in rapidly getting good at applied machine learning. Rapidly Accelerate Your Progress in Applied Machine Learning With Weka Photo by Jonathan Riddell, some rights reserved. When you start out in applied machine learning, there is so much to learn. Often you need to learn a new programming language, like python or more esoteric languages like Matlab or R. It is so much easier to learn one thing well rather than try, and possibly fail to learn a host of new things.
Another year has passed and humanity still remains in charge of the planet(better or worse?). Will robots take over in 2019? Well doesn't seem so in next year, but you always have the next year. Since the inception of AI, developers have been fascinated by the idea of self-learning programs, Natural Language Processing, and Generative Modeling is the most popular GitHub repositories among developers. All this interest means there are going to exciting developments in years to come.
Machine Learning and Artificial Intelligence, together with other advanced modelling techniques, are continuously evolving and you can find thousands of algorithms, tools, platforms, etc., making it challenging to identify and validate the correct approach, technologies and solutions to use in the Mining industry. Furthermore, the success of a data analytics solution can only be realized if it can be readily deployed, managed and operated. We look forward to seeing you there.
When businesses identify a problem that can be solved through machine learning, they brief the data scientists and analysts to create a predictive analytics solution. In many cases, the turnaround time for delivering a solution is pretty long. Even for experienced data scientists, evolving machine learning models that can accurately predict the results is always challenging and time-consuming. The complex workflow involved in machine learning models have multiple stages. Some of the significant steps include data acquisition, data exploration, feature engineering, model selection, experimentation and prediction.
Stradigi AI, a North American Artificial Intelligence software company, unveiled the new self-service version of its Machine Learning (ML) cloud SaaS platform, Kepler. Stradigi AI is enabling users with no previous ML and Deep Learning experience to reap the benefits of business-driving AI in hundreds of real-world applications. The Kepler platform provides end-to-end automation of complex data science processes with its new Automated Data Science Workflows. The automation features allow businesses to get AI projects to market on their own quickly, solving the most pressing use cases in the market today, including customer segmentation, churn prediction, demand forecasting, predictive maintenance, sentiment analysis, attribution modeling, workforce planning, pricing optimization, and more, across all business verticals. "[The Kepler platform] has been designed to enable non-coders without data science experience to get up and running quickly to make ML-driven predictions," said Krishna Roy, Senior Analyst, Data Science and Analytics at 451 Research.