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Hi everyone! I'm designing an introductory machine learning class for junior and senior computer science students and need your help. For ML hiring managers: what technical and non-technical skills do you look for in new hires? For my technical ML geek colleagues: what fundamental skills and topics are essential for students to master to build a solid ML foundation and succeed in their careers? Your insights will help shape a curriculum that prepares students for the future. Thanks in advance for your input! #MachineLearning #Education #CareerDevelopment
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Certified MERN Stack & Next.js Developer || Dedicated to Frontend Engineering excellence || I help you scale your business with scalable, user-centric Mobile App Solutions || GenAI Enthusiast
#ai_fun_news Devin AI is hiring Human Software engineers🤣🤣 #Devin The AI Software Engineer Founded by Cognition Labs. Which is hiring human software engineers😂🤣. So be easy AI is not going to replace human beings. AI is just an assistant for the software engineers. Real developers should be happy to see such AI tools because these tools will boost their development speed. Now software engineers can build more software and can earn more revenue.
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"🤔 Confused about the differences between Software Engineering, IT, Computer Science, Data Science, and AI? Check out this handy table to understand the focus, key skills, and typical job positions in each field! 🌟 Whether you're coding, managing networks, diving into algorithms, crunching data, or creating intelligent systems, there's a perfect tech career path for everyone! 🚀 #TechCareers #SoftwareEngineering #IT #ComputerScience #DataScience #AI"
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Machine Learning Engineer Keylent, Inc. Request-ID: 22207-1 *THIS ROLE IS HYBRID IN CHARLOTTE, NC* MAX BILL RATE: $70 Job Title: Machine Learning Engineer Experience: 10 to 11 yrs Top Requirement for Role: Client engineer with good hands-on skills in Python and other Client Ops related tools & Tech. Roles & Responsibilities: - Analyzing the AI/Client algorithms that could be used to solve a given problem and ranking them by their success probability. - Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world. - Verifying data quality, and/or ensuring it via data cleaning. - Supervising the data acquisition process if more data is needed. - Finding available datasets online that could be used for training. - Defining validation strategies -Defining the preprocessing or feature engineering to be done on a given dataset -Analyzing the errors of the model and designing strategies to overcome them -Depl... See the full job description on illbeback: https://lnkd.in/dKyntpyq #AI #ML #AIJobs #AITalent #hiring #MachineLearning #Charlotte
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I am currently partnered with a start-up incubated at Stanford, who are catalyzing the use of Neural Interfaces in the AI-driven space. 🎯 Taking a human-centric approach, the team are working on the solution to the amount of time that is lost on technology which could be better integrated into our daily lives. I am looking for US-based software engineers who have experience working on bio-signals software with data science & any ML tech. React out or tag those in your network below. 📲 #machinelearning #software #startup #ai #datascience
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Is AI like Devin set to replace software engineering jobs? ... Doubtful. 🚫❌ While Devin demonstrates impressive capabilities, there's a crucial reason why it won't eliminate software engineering roles anytime soon, if ever: 👇 Savvy businesses understand the significant cost of hiring an underperforming software engineer and prioritize avoiding such missteps, even if it means potentially overlooking strong candidates. (Hint: this is why coding interviews are notoriously challenging; explore popular platforms for preparation.) Likewise, the potential consequences of Devin making mistakes or encountering roadblocks that stall development far outweigh the benefits of replacing human engineers. Any business serious about its longevity won't gamble on replacing its software engineers with AI like Devin. Instead, it will harness AI to enhance their abilities, accelerating development and broadening projects, ultimately driving the demand for more skilled software engineers. Software engineers, keep coding. #softwareengineering #programming #coding #ai #devin
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Founder, HumaLitix🔹Data & AI🔹I/O Psychology & Behavioural Science🔹Transforming Organisations with People-Centric Data and AI adoption solutions
The real risk for Software Engineers is not AI taking away jobs! The real risk is: * Where computer science / core software skills still matter, the complexity and entry barrier is going up rapidly. If you want to compete in this space, better up your game! (Math, Infra, Algorithms, Models, Data Engineering, scalability etc) * Where the software complexity is lower (read where mediocre skills suffice), AI will eventually do the bulk of heavy lifting with code, debugging etc, and the real play will be in understanding the business / use cases / end user experience etc. Which means domain experts from the specific industry / domain (assisted by AI) will have a competitive edge! If you are not truly passionate about computer science / engineering, mediocre software skills no longer suffice. Build business / domain skills to stay relevant! #artificialintelligence #datascience #futureofwork #worklifetales
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Transitioning from software engineering to machine learning is going to be a fascinating journey of exploration and growth. While my background in software engineering provides a solid foundation in programming and problem-solving, delving into the realm of machine learning is set to open up a whole new world of possibilities. This shift will allow me to combine my technical skills with a deep curiosity for data analysis and pattern recognition, enabling me to tackle complex problems in innovative ways. Embracing this career change is going to be both challenging and rewarding, as I continue to expand my knowledge and expertise in this exciting field.
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Having worked with many teams over time, I have realized that building a successful AI product requires a combination of ML Engineers and Software Engineers. They both bring a wealth of complementary skills to the table. While software developers excel in coding, debugging, and building applications, ML engineers focus on creating models, understanding algorithms, and managing large datasets. The overlap? Both need strong programming skills, but their approaches differ. What do you think is the most crucial skill for each role? #TechSkills #MLEngineering #SoftwareDevelopment
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Software Engineer vs ML Engineer: A Career Guide for 2024 https://lnkd.in/gvysN6T9 Both Software Engineering and Machine Learning Engineering offer exciting career prospects, each with its unique challenges and rewards. Aspiring professionals should assess their interests, skills, and long-term goals to make an informed decision. #SoftwareEngineer #MachineLearningEngineer #TechIndustry #ProgrammingLanguages #MachineLearning #AI #AINews #AnalyticsInsight #AnalyticsInsightMagazine
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