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Practical Project Management for Machine Learning Projects


The last few years have seen a meteoric rise in disciplines related to using and exploring large quantities of data (Big Data), such as Artificial Intelligence and the Internet of Things. A part of the Artificial Intelligence domain, Machine Learning and Data Science in particular took hold in many corporations and started impacting the business outcomes. In turn, IT Project Managers are suddenly facing a different type of project they are asked to manage: the Machine Learning project. This course is addressed to experienced IT Project Managers who want to understand how to manage Machine Learning projects, what are the specific challenges they will face, and what are some best practices to help them successfully deliver business value.

Top 20 Python AI and Machine Learning Open Source Projects


Getting into Machine Learning and AI is not an easy task. Many aspiring professionals and enthusiasts find it hard to establish a proper path into the field, given the enormous amount of resources available today. The field is evolving constantly and it is crucial that we keep up with the pace of this rapid development. In order to cope with this overwhelming speed of evolution and innovation, a good way to stay updated and knowledgeable on the advances of ML, is to engage with the community by contributing to the many open-source projects and tools that are used daily by advanced professionals.

The augmented project manager


After seeing recent industry presentations on bots, machine learning and artificial intelligence (AI), I see the application of these technologies changing the practice of project management. The question is, is this future desirable or will we have a choice? Much of the daily work of a project manager has not dramatically changed over the last 30 years. We may use different management methodologies, but we spend a great deal of time manually collecting and disseminating information between the various roles on a project. This effort directly results from the need to fill the information gaps caused by systems that can't capture what is truly happening within the organization.

Top 20 Python Machine Learning Open Source Projects, updated


Continuing analysis from last year: Top 20 Python Machine Learning Open Source Projects, this year KDnuggets bring you latest top 20 Python Machine Learning Open Source Projects on Github. Strangely, some of the most active projects of last year have become stagnant and also some lost their position from top 20 (considering contributions and commits), whereas new 13 projects have entered into top 20. We can see in the following chart that PyMVPA has highest contribution rate compare to all top projects in the list. Surprisingly, Scikit-learn has low contribution rate, despite maximum no. of contributors compare to other projects. Reason behind this could be that, as PyMVPA is a new project and going through early phases of development, leading to many commits because of new ideas/features development, defect fixing, refactoring etc. Whereas, Scikit-learn is old and stable project leading to less no. of improvements or defect fixing.

Artificial Intelligence Projects


"Artificial Intelligence" was actually expressed back in 1956 by John McCarthy, a professor at Dartmouth. For years, it had been thought that computers would never match the facility of the human brain, but this has proven to not be the case. Well, some time ago we didn't have enough data and computation power, but now with Big Data coming into existence and with the arrival of GPUs, AI is feasible. Did you recognize that 90% of the world's data has been generated within the past two years alone? Computers can add up all this information more quickly.