An inspiring speaker is somebody who offers talks about conveying a technology vision for a consortium of organizations and more to the world. These speakers provide thought leadership, set out a strategy inspiring group of people in working towards a genuine goal. The talks from such speakers provide techniques, methodologies, real-life examples, and case studies. Though there are dozens of speakers out there, some speakers just grasp us in ways that no other speakers are able to. We all have certain speakers we turn to when the mood and motivation strikes. History has seen many famous public speakers, back in the day. Some of such popular speakers include Eric Schmidt, Marissa Mayer, Steve Woznaik, Steve Jobs, and many others. These speakers have been lauded for years as those who were able to bring massive changes in the way the world functioned with just their words. They would make speeches so moving that it would embalm hope and the will to fight for their rights, in the hearts of their listeners.
We also have several well-known speakers now, who have been celebrated for their manner of speaking and being able to grasp the audience for long periods of time. Such speaker is Ganapathi Pulipaka. He has a particular presenting style that is well-connected with audience, bringing positive outlook in the audience, as he is good in subject matter of machine learning and inspiring the audience in equal amounts. He pushes the audience from their comfort-zones by bringing the innovation. He takes them on a journey of hopeless hardship to success. He leaves the audience with goosebumps and in awe. In a recent research conducted by Stevenson University with the participants from the University of Kentucky, the research took note of each experience of goosebumps and awe was in second frequency. A remarkable performance of speech was the most common selected trigger of awe by the participants. The research scholar specializing in machine learning has been invited to speak in several Robotics, IoT, and machine learning conferences. He is a keynote speaker and also a Global Premier Speaker. He has been invited to provide his expertise in several data science events such as Robotics and Artificial Intelligence conferences held in Los Angeles on May 2018. He has also made it to the speakers’ list of a VIP Only Executive AI Conference for Venture Beat Magazine, Transform 2019 and Intercon AI conference, IDEAS conference at LA, ODSC West Conference at San Francisco, and Global Data conference at Santa Clara, CA. Dr. Ganapathi Pulipaka believes, that data science goes beyond data literacy, as most of the kids of the current generation are already familiar with data from iPhones, iPads, and Android devices from the childhood, unlike the previous generation.
As an outstanding quality of a speaker is that he has full awareness of the people who want to be trained in data science. This allows him to serve his audience in a better way by understanding what would help him to retain his audience and what would distract them from the message. He is able to keep the attention of his audience on him at all times with innovation, no matter how dry the subject can get. When it comes to Ganapathi Pulipaka, the subject of machine learning can never seem boring.
An inspiring speaker is someone who speaks about what their audience wants to hear. They consider the perspective of the audience and seek to serve their aims mostly. They are able to talk on topics that their audience wants to hear about, and so they become respected amongst them, and their peers.
Pulipaka knows how to present what he wants to say in a way that would keep the audience engaged and interested in the most related topics. He understands that there will not always be topics that the audience has never heard of, but the information that he is able to bring to the table will keep the attention of the listeners.
To be able to go out of your comfort zone to serve your audience is another great quality of an inspiring speaker. This is the quality that brings the edginess and power to any public speaker as we can see how important the topic is to you.
IntroductionArtificial Intelligence and data wisdom are good fields of operations, systems, and further that end at replicating mortal intelligence through machines.
AI represents action-planned feedback of perception.Data Science uses different corridors of this pattern or circle to interrupt specific problems.
Perception, data scientists attempt to identify patterns with the assistance of the info.
planning, there are two aspects Artificial Intelligence Artificial Intelligence (AI) is that the reproduction of human knowledge measures by machines, particularly PC frameworks.
Explicit utilizations of AI contain master frameworks, normal language preparing, discourse acknowledgment, and machine vision.Artificial Intelligence is critical because it can give endeavors experiences into their activities that they could not have known about already and since, now and again, AI can execute assignments better than people.
Especially with regards to dull, thorough assignments like examining enormous quantities of authoritative archives to ensure significant fields are filled in appropriately, AI apparatuses regularly complete positions rapidly and with somewhat a few blunders.Data scienceData science, analytics, and machine learning demand are growing at an astronomical rate and top companies are now trying to find professionals in Data science who can sift through the goldmine of knowledge and help them to require business decisions efficiently.Data science may be a concept wont to tackle big data and includes data cleansing, preparation, and analysis.
Data analytics involves a four-stage process that builds on itself and uses machine learning (ML) and AI in the final phases to predict outcomes and suggest options to respond to those predictions.
In this article, we shall focus on the 4 types of data analytics we encounter in data science: DescriptiveDiagnosticPredictivePrescriptive Descriptive Analytics:This is the most common of all forms.
In describing the past, descriptive analytics lays the groundwork for how these events may affect future outcomes.
Then queries, reports, and data visualizations can further yield deeper insight.
Diagnostic data analytics help answer why something occurred.
Prescriptive Analytics:The final phase, prescriptive analytics, attempts to find the best course of action, solution or outcome among various choices, building off descriptive analytics’ insight into what happened and predictive analytics’ forecast of what might happen.
The process of data annotation, for speech, image, video, audio, or text, is a highly specialized task that needs expertise.
When we talk about high quality data, it means that the labels are both accurate and consistent.
By outsourcing data annotation services, companies save time and effort freeing up their in-house data scientists to focus on areas they are experienced in instead of annotating data.Here are a few of data annotation techniques used for specific AI initiatives.Audio Transcription for Speech Recognition Audio transcription is used to train speech recognition models.
High-accuracy audio transcription enables interactions to accurately happen between human speech and AI-based models like smart TVs, phones, virtual assistants, watches, computers, or other in-home or on-the-road technology.Human speech must be accurately recognized to understand not only what words are being spoken but also what they mean.
Text annotation detects and labels words depending on a predetermined set of categories required by an AI company.
Text annotation for Natural Language Processing or NLP helps machines predict and understand the human language more easily.
What is Machine Learning?Machine Learning is a branch of Artificial Intelligence(AI), in which we make our machines learn as humans learn from past data, gradually surpassing humans in predictions.
Machine Learning has gradually seen a massive increase and its applications are seen in the day-to-day things we use like Netflix.
The term ‘Machine Learning’ originally was invented based on a model of ‘Brain Cell Infection’.
The model was created in 1949 by Donald Hebb in a book titled ‘The Organisation of Behaviour’, which presents Hebb’s theories on neuron excitement and communication between neurons.
Later, an American Pioneer Arthur Samuel coined the term ‘Machine Learning in 1959.
Arthur Samuel defined Machine Learning as “ a field of study that gives computers the ability to learn without being explicitly programmed”.
Artificial Intelligence is a string software tool that eliminates repetitive tasks in the workforce and elevates human intelligence with creativity and innovation.
Here are potential reasons to prove the legitimacy of AI and ML.
