An exhaustive course covering Regression, Classifications, Clustering, Text Mining, Sequence analysis, Sentiment analysis, Text analysis, using tools like, R Studio, Jupyter Note book, Orange, KNIME, Big ML and RapidMiner
Applied statistics with lots of fun and exercises related to Mean, Median, Mode, Std. Deviation, Probability, Conditional probability, Bell Curve, Discrete and Continuous variables, Z-order and Z-order tables.
This is a hands-on course for big data using Cloudera cluster and Hue to work with Hive database, create datamart for sample data engineering tasks like design of back office and client databases, facts , dimension tables , joining, creating views for ease of view
LinkedIn Learning path that offers the fundamental stages of data science work, from Statistics and Systems Engineering to Data Mining and Machine Learning, source, explore, and communicate with data through graphs and statistics.
A basic course in Data Science. Key features include evaluating different sources of data, including metrics and APIs,exploring data through graphs and statistics,discovering how data scientists use programming languages such as R, Python, and SQL,assessing the role of mathematics, such as algebra, in data science,applied statistics, such as confidence intervals,machine learning, such as artificial neural networks, in data science and define the components of effective data visualization.
Applied statistics with lots of fun and exercises related to Mean, Median, Mode, Std. Deviation, Probability, Conditional probability, Bell Curve, Discrete and Continuous variables, Z-order and Z-order tables.
This is a hands-on course for big data using Cloudera cluster and Hue to work with Hive database, create datamart for sample data engineering tasks like design of back office and client databases, facts , dimension tables , joining, creating views for ease of view
LinkedIn Learning path that offers the fundamental stages of data science work, from Statistics and Systems Engineering to Data Mining and Machine Learning, source, explore, and communicate with data through graphs and statistics.
A basic course in Data Science. Key features include evaluating different sources of data, including metrics and APIs,exploring data through graphs and statistics,discovering how data scientists use programming languages such as R, Python, and SQL,assessing the role of mathematics, such as algebra, in data science,applied statistics, such as confidence intervals,machine learning, such as artificial neural networks, in data science and define the components of effective data visualization.
Introduces the concepts related to edge analytics, and explains how edge analytics fit alongside big data, traditional data warehousing, and other analytical environments, demonstrates sample architectures and core technologies such as intelligent digital video and sensor-produced data. Review specific solutions from Intel, Cisco, and other vendors, and shows how edge analytics can be applied in four different use cases: retail, manufacturing, IT security and systems management, and energy exploration.
LinkedIn course that offers process of turning "facts and figures" into "story" to engage and fulfill our human expectation for information, by showing you how to think about, and craft, stories from data by examining many compelling stories in detail
In this course, I learnt about:
Define innovation.
Explain the principle of function follows form.
Describe the closed-world principle.
List characteristics of innovative products and services.
Explain the subtraction technique.
Identify techniques for breaking structural fixedness.
Apply task unification.
Identify types of dependencies.
Build a pilot program.
This course shows how to use analytics to make data-driven decisions and gain competitive advantage,explore the differences between predictive and prescriptive analytics, and find out how to formulate questions,variety of simple techniques: averages, sampling, cherry picking, forecasting, and correlation and causality
NexGen Solutions for cloud platforms, powered by GenQAIVijayananda Mohire
This is our next generation solutions powered by emerging technologies like AI, quantum computing, Blockchain, quantum cryptography etc. We have various offers that can help improved productivity, help automate and improve ease of doing business. We offer cloud based solutions and have a Hub to interface major cloud platforms.
Introduces the concepts related to edge analytics, and explains how edge analytics fit alongside big data, traditional data warehousing, and other analytical environments, demonstrates sample architectures and core technologies such as intelligent digital video and sensor-produced data. Review specific solutions from Intel, Cisco, and other vendors, and shows how edge analytics can be applied in four different use cases: retail, manufacturing, IT security and systems management, and energy exploration.
LinkedIn course that offers process of turning "facts and figures" into "story" to engage and fulfill our human expectation for information, by showing you how to think about, and craft, stories from data by examining many compelling stories in detail
In this course, I learnt about:
Define innovation.
Explain the principle of function follows form.
Describe the closed-world principle.
List characteristics of innovative products and services.
Explain the subtraction technique.
Identify techniques for breaking structural fixedness.
Apply task unification.
Identify types of dependencies.
Build a pilot program.
This course shows how to use analytics to make data-driven decisions and gain competitive advantage,explore the differences between predictive and prescriptive analytics, and find out how to formulate questions,variety of simple techniques: averages, sampling, cherry picking, forecasting, and correlation and causality
NexGen Solutions for cloud platforms, powered by GenQAIVijayananda Mohire
This is our next generation solutions powered by emerging technologies like AI, quantum computing, Blockchain, quantum cryptography etc. We have various offers that can help improved productivity, help automate and improve ease of doing business. We offer cloud based solutions and have a Hub to interface major cloud platforms.
This is our project work at our startup for Data Science. This is part of our internal training and focused on data management for AI, ML and Generative AI apps
This is our contributions to the Data Science projects, as developed in our startup. These are part of partner trainings and in-house design and development and testing of the course material and concepts in Data Science and Engineering. It covers Data ingestion, data wrangling, feature engineering, data analysis, data storage, data extraction, querying data, formatting and visualizing data for various dashboards.Data is prepared for accurate ML model predictions and Generative AI apps
Considering the need and demand for high quality digital platforms that can help clients to get the most of the newer technology, we have proposed an IT Hub that allows for rapid on boarding of clients to various modules on a need basis, allowing them to subscribe to modules they need only. We have various modules.
This document offers a high level overview of our IT Hub that offers various modules allowing for clients to onboard faster and get the benefits of a large set of vendor products, tools, IDE related to AI, Quantum and Generative AI technologies.
This is my hands-on projects in quantum technologies. These are few of the key projects that I worked with that demonstrates my skills in using various concepts, tools, IDE and deriving the solutions by using quantum principles like superposition, and entanglement along with quantum circuits in realizing the concepts
This is my journey taken from year 2012 on wards, after graduation in my MS with major in AI. I have taken various certification courses, trainings, hands-on labs; few key ones are from Google, and Microsoft.
Agricultural and allied industries play a vital role in the progress of a nation and sustainable economic growth. Farmers play a vital role in this progress. Their hard work and efforts need to be praised and possibly offer them various tools and digital assets that can automate some of their various repetitive tasks such as back office operations, crop monitoring, and post-harvesting routines that might divert the attention of farmers from their core job.
We, at Bhadale IT have developed various products and services that are revolutionary and can offer effective solutions with our industrial partnerships with digital technology leaders like Intel and Microsoft. We have drafted this solution brief to illustrate our products and service offerings for the agricultural industry. We can tailor make highly customized solutions to meet individual project and farmer needs that can include use of various technologies like artificial intelligence, machine learning, data science and related machinery like drones and geo-spatial datasets and various information that can offer precise farming techniques and use of technology in improving production, improvised use of fertilizers, organic farming and reduced crop loss due to rodents, insects and regional diseases.
The focus of this solution is for farmers to adopt and migrate to digital cloud platform to Microsoft Azure that can boost quality and quantity of crop production and improve their supply chain and offer faster and mature downstream business operations.
This is our cloud offerings based on our partnership and relationship with Intel and Microsoft. We offer highly optimized Intel motherboards, memory, and software stack that is best suited for Azure cloud platform and can handle various types of models (IaaS, PaaS, SaaS) and Azure workloads in the public or private cloud.
Explore the fundamentals of GitHub Copilot and its potential to enhance productivity and foster innovation for both individual developers and businesses. Discover how to implement it within your organization and unleash its power for your own projects.
In this learning path, you'll:
Gain a comprehensive understanding of the distinctions between GitHub Copilot for Individuals, GitHub Copilot for Business, and GitHub Copilot X.
Explore various use cases for GitHub Copilot for Business, including real-life examples showcasing how customers have leveraged it to boost their productivity.
Receive step-by-step instructions on enabling GitHub Copilot for Individuals and GitHub Copilot for Business, ensuring a seamless integration into your workflows.
Practical ChatGPT From Use Cases to Prompt Engineering & Ethical ImplicationsVijayananda Mohire
This journey provides learners with a thorough exploration of ChatGPT, starting with an introduction to large language models and their capabilities, the series progresses through practical applications, advanced techniques, industry impacts, and important ethical considerations. Each course aims to equip learners with an in-depth understanding of the model, its functionality, and its wide-ranging applications.
Red Hat Enterprise Linux (RHEL) and Hybrid Cloud Infrastructure. Products that are developed for multi-cloud hybrid platform enabling seamless integration and portability of workloads across Red Hat and partner Infrastructure, public and private clouds.
Learners will be exposed to the foundations of Red Hat, Red Hat Enterprise Linux (RHEL) portfolio including Hybrid Cloud Infrastructure, how to identify target customers, distinguish Red Hat solutions from the competition, review key use cases, align to the sales conversation framework for positioning the solutions, and much more!
Upon completing this learning path, learners will receive the Red Hat Sales Specialist - Red Hat Enterprise Linux accreditation and be prepared to advance to the Red Hat Sales Specialist - Red Hat Enterprise Linux II learning path
This is my annual learning at Red Hat related to accreditation and courses at Red Hat partner training portal.
Learners will be exposed to the foundations of Red Hat, Red Hat Enterprise Linux (RHEL) portfolio including Hybrid Cloud Infrastructure, how to identify target customers, distinguish Red Hat solutions from the competition, review key use cases, align to the sales conversation framework for positioning the solutions, and much more!
Generative AI is a cutting-edge technology that will transform nearly every business function, ranging from content creation and product design, to improving customer experience and marketing new ideas. While the benefits of Generative AI are immense, the technology has its limitations and poses some ethical considerations. In this Journey, learners of all levels will develop a shared understanding of what Generative AI is, the guardrails for use and identify of how to use, build and experiment with the technology in a responsible manner. Learners will also develop skills for leading through this disruption with empathy, while cultivating the human skills to sustain the transformation
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
1. Certificate of Completion
Congratulations, Vijayananda Mohire
Data Science Foundations: Data Mining
Course completed on Dec 11, 2020 at 06:22AM UTC • 4 hours 40 min
By continuing to learn, you have expanded your perspective, sharpened your
skills, and made yourself even more in demand.
Head of Content Strategy, Learning
LinkedIn Learning
1000 W Maude Ave
Sunnyvale, CA 94085
Certificate Id: ATbjs_eKV8Gzr6bahz1y_PjjA6hw