Posts

Python or Matlab: Winning the ML/AI Challenges

Diversified applications of ML/AI are demanding ease of programming platform, access & rapid development. Python (Open Source)stands first in the  row to support with its vast & extensive libraries such as sklearn, matplotlib, tensorflow, keras, pytorch etc., making Python the most widely used prog language in ML/AI, Data Science & Data Analytics area. Matlab being the proprietary platform, places constraint due to its availability, integration issues & package development. Matlab is preferred  in academic  fraternity  for research  with traditional misperception about its better performance but Python can do much better than Matlab in ML/AI.  @SKPandey

Machine Learning and AI : Web Repositories of Projects and Contributions

Open source platforms and resources  are key to learning ML & AI. Machine Learning and AI projects in Python are  available on Github. Understanding  ML & AI algorithm implementations is must for professionals .

IoT : Crowd-Sensing

IoT : Crowd-Sensing One very interesting aspect to the development of IoT when it comes to machine learning is the emergence of crowd-sensing. Crowd-sensing exists under two different forms: voluntary, when users voluntarily contribute information, and opportunistic, when data is collected automatically without explicit user intervention. This is one way that IoT data can contribute not only to the development or the improvement of IoT applications, but can also be used as input for other, non-IoT, applications. IoT actually allows collection of very unique datasets in a way that has never been achieved before. Because the data generated by each device is usually at a human-scale, it becomes feasible for a user to label or validate it. It also becomes possible to gather data closest to where the users are: this is what Google does when they ask users to take a picture of a restaurant they are currently dining at, or to answer a few questions regarding the amenities. This is the f

 On-site and Off-site SEO(Search Engine Optimization)  :

 On-sitea and Off-site SEO(Search Engine Optimization)  :  On-site SEO refer to strategies that are implemented within the web programming language, and appear on the website itself.These can include strategies to aid search engines in indexing and ranking the website in question, as well as encourage visits, especially repeat visits. Some common examples of on-site strategies include frequently publishing sticky content, properly using metadata, and ensuring site navigation is simple for humans and search engine crawlers. Off-site SEO strategies are all those SEO strategies that are implemented off-site, such as those which incorporate third-party websites.Many off-site SEO strategies involve driving traffic to the website; however, some involve enhancing a website’s overall visibility in search results, while others involve enhancing its index-ability by search engine crawlers . Common examples of off-site strategies include backlinking and email marketing. It is important to not

About SKPSoft

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SKPSoft SKPSoft Consultancy Services came into existence in 2001. We provide diversified platform for the IT Training, Consultancy and Applications Development. It also provides IT professionals to work together. We support Professionals to expose their achievements and contribute to the global society. It supports the companies/individual in Digital Online Marketing/Adv.  Expertise in e-Content and web-content development and management. We also work on web profile organization/person) management. It's unique platform for Trainers and Trainees both. More Details:   http://www.skpsoft.org