It's critical to understand what you're worth as you navigate the field Coursera released this article regarding the latest trends. From the article: "In 2022, Glassdoor ranked data scientists as the third-best job in America [1]. If you enjoy analyzing data to identify patterns and solve problems, this career path holds plenty of well-paid opportunities for you. You can use this article to find out how much a data scientist makes and how you can increase your salary in this role. Between 2021 and 2031, the US Bureau of Labor Statistics estimates the employment rate for data scientists to grow by 21 percent by 2030 [2].

How I use ChatGPT in my data science work (4-5 hours per week time savings): I told myself I wouldn't make content around this, but here I am. I feel like I'm selling out. On the other hand, I've found ChatGPT useful and I feel like it is important to share how this tool has benefited me in my work. I usually have to look up code for this or sift through docs to create exact specifications I'm looking for. With ChatGPT, I can put into words what I'm trying to do and it gives me pretty decent examples to work with.

PLEASE share on your #LinkedIn timeline- it means THE WORLD to me! #AI/ #ArtificialIntelligence: #ChatGPT? Generative #AI jargon, explained!!! via Fast Company! https://bit.ly/3I9fZbI Quote: "While ChatGPT and text-to-image tools are among the buzziest developments in tech right now, comprehending what they are and how they work can be an exercise in frustration. The field of AI is a rabbit hole of technical and mathematical jargon, and simple explanations of even the most fundamental concepts is in short supply. As a result, tools like ChatGPT and Stable Diffusion can feel like mystical black boxes, and it's easy to lose track of the differences between them and the companies involved. To help make sense of it all, here's a plain English glossary of notable AI terms, products, and companies, along with links to where you can learn more. BASIC AI TERMS AI: Short for artificial intelligence, this broadly refers to the idea of computers that can learn and make decisions in a human-like way. Machine learning: A subfield of artificial intelligence, this is the practice of teaching computers to recognize patterns through data and algorithms. It differs from traditional programming in that the computer doesn't need to be explicitly coded to address every potential scenario. Neural network: A type of machine learning model that mimics the neurons in the human brain, using a network of nodes to process data through algorithms. This allows the computer to make connections between lots of different data points and learn which ones are the most important when responding to query. Deep learning: Describes a neural network whose data passes through several layers of processing--some of which are hidden from the programmer--before arriving at a response. AI tools such as ChatGPT and Stable Diffusion are examples of applications that use deep learning techniques."

When most people think of artificial intelligence (AI), they think of smarty-pants robots that can service our every whim. While real robots may be in the cards, the future of AI will also revolutionize the way we work (in real life and in the metaverse). In fact, AI is already in your workplace: You use AI when you use Google Maps to find your way to an off-site meeting (perhaps in a self-driving car?), or when you use spell-check for a report. The current state of AI and the future of AI goes far beyond simplifying mundane tasks, however. Artificial intelligence, or computers that are taught to

It is just me or Hugging Face is changing the game for Machine Learning? Their pretrained ML model library is beyond anything I have seen so far: https://lnkd.in/eB-4JkVF. Currently, there are 70,000 pretrained models available! I recently implemented a text summarizer using one of their models for the app I am building, and I was impressed by the simplicity of the implementation and the efficiency of the resulting product! I have been knowing HuggingFace for quite a while now, but I am late to the game realizing what they had to offer!

Building a High-Performance Data and AI Organization is a great read this weekend if you'd like to get insights from a combined 351 CDOs, CAOs, CIO, and CTOs who spread across North America, Europe and Asia-Pacific, and cover 14 sectors while running orgs generating at least $1B in annual revenue. We don't have to reinvent the wheel here. Let's learn from those who've tried it before, and adjust as needed to increase our chance of success. The key insights regarding the difficulties companies face when scaling ML use cases: No central place to store and discover ML models Numerous types of deployments and error prone hand-offs between data science and production Lack of ML expertise A plethora of tools and frameworks Hard to explain and govern ML models Outdated models because of infrequently refreshed data Access to relevant quality data Are you experiencing any of these? You can find more insights in the full doc below.

"Explanation methods that help users understand and trust machine-learning models often describe how much certain features used in the model contribute to its prediction. For example, if a model predicts a patient's risk of developing cardiac disease, a physician might want to know how strongly the patient's heart rate data influences that prediction. But if those features are so complex or convoluted that the user can't understand them, does the explanation method do any good? MIT researchers are striving to improve the interpretability of features so decision makers will be more comfortable using the outputs of machine-learning models. Drawing on years of field work, they developed a taxonomy to help developers craft features that will be easier for their target audience to understand." #Analytics #ArtificialIntelligence #BigData #Coding #Data #ML #MLOps #NLP #WomenWhoCode

In each of these iterations, certain parameters are tweaked continuously by developers. Any parameter manually selected based on learning from previous experiments qualify to be called a model hyper-parameter. These parameters represent intuitive decisions whose value cannot be estimated from data or from ML theory. The hyper-parameters are knobs that you tweak during each iteration of training a model to improve the accuracy in the predictions made by the model. The hyper-parameters are variables that govern the training process itself.

In modern times, Google is everywhere!!! And while Machine Learning has long been a part of Google, now it seems that ML is everywhere! From Google Search to Google Photos to even Google Translate, everything uses Machine Learning.