This story was originally published on HackerNoon at: https://hackernoon.com/my-notes-on-mae-vs-mse-error-metrics. We will focus on MSE and MAE metrics, which are frequently used model evaluation metrics in regression models. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-science, #metrics, #linear-regression, #error-metrics, #machine-learning, #regularization, #normal-distribution, #residuals, #hackernoon-es, and more. This story was written by: @sengul. Learn more about this writer by checking @sengul's about page, and for more stories, please visit hackernoon.com. We will focus on MSE and MAE metrics, which are frequently used model evaluation metrics in regression models. MAE is the average distance between the real data and the predicted data, but fails to punish large errors in prediction. MSE measures the average squared difference between the estimated values and the actual value. L1 and L2 Regularization is a technique used to reduce the complexity of the model. It does this by penalizing the loss function by regularizing the function of the function.

This story was originally published on HackerNoon at: https://hackernoon.com/assessing-your-organizations-customer-data-maturity. Investing in customer data is a top priority for marketing leaders. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #customer-data, #customer-data-management, #customer-data-maturity, #database, #data-analysis, #data-visualization, #data-protection, #good-company, #hackernoon-es, and more. This story was written by: @mparticle. Learn more about this writer by checking @mparticle's about page, and for more stories, please visit hackernoon.com. Marketing departments are still trying to make sense of customer data they’re collecting, reduce manual work required to support customer data processes, and figure out how to use customer data to increase customer value. The Gartner CMO Council report surveyed 300 marketing leaders across industries and geographies and found that 80% of leaders said data, analytics and insights are “very important to winning and retaining customers,” nearly two-thirds were only moderately confident (or worse) in their data systems. To prevent your customer data set from becoming a data swamp, lay a solid, scalable customer data infrastructure at the heart of your martech stack.

This story was originally published on HackerNoon at: https://hackernoon.com/from-hadoop-to-cloud-why-and-how-to-decouple-storage-and-compute-in-big-data-platforms. This article reviews the Hadoop architecture, discusses the importance and feasibility of storage-compute decoupling, and explores available market solutions. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data, #open-source, #big-data, #distributed-systems, #distributed-file-systems, #object-storage, #cloud-native, #software-architecture, and more. This story was written by: @suave. Learn more about this writer by checking @suave's about page, and for more stories, please visit hackernoon.com. Initially, Hadoop integrated storage and compute, but the emergence of cloud computing led to a separation of these components. Object storage emerged as an alternative to HDFS but had limitations. To complement these limitations, JuiceFS, an open source distributed file system, offers cost-effective solutions for data-intensive scenarios like computation, analysis, and training. The decision to adopt storage-compute separation depends on factors like scalability, performance, cost, and compatibility.

This story was originally published on HackerNoon at: https://hackernoon.com/embracing-the-shift-future-of-work-in-the-era-of-automation. Embracing the changing face of work: understanding automation's influence and equipping yourself with skills for the future. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-analysis, #automation, #software-development, #ai, #future-of-work, #artificial-intelligence, #work, #machine-learning, and more. This story was written by: @vikrantbhalodia. Learn more about this writer by checking @vikrantbhalodia's about page, and for more stories, please visit hackernoon.com. The landscape of work is rapidly shifting due to automation across the industries. While certain jobs will be rendered obsolete by automation, employment prospects, such as custom software development, will emerge. The key for professionals lies in the adaptability and the development of skills that are highly sought after in the emerging future.

This story was originally published on HackerNoon at: https://hackernoon.com/is-data-licensing-the-key-to-the-privacy-personalization-paradox. Consumers want both privacy and personalization. Is it possible? Of course. As data becomes an asset for consumers, data licenses may bring both worlds together Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data, #personalization, #privacy, #data-privacy, #customer-loyalty, #digital-asset, #data-science, #user-data-privacy, and more. This story was written by: @shanefaria. Learn more about this writer by checking @shanefaria's about page, and for more stories, please visit hackernoon.com. Consumers want personalization AND they want privacy. But they're still willing to share data for personalized experiences, perks, deals, and benefits. As data becomes viewed as an asset by consumers and privacy policies, laws, and regulations become more prominent, we risk throwing the baby out with the bathwater without a logical and efficient way to exchange data between businesses and consumers. Data licenses may provide a solution.

This story was originally published on HackerNoon at: https://hackernoon.com/how-to-fix-zsh-command-not-found-python. This can happen on any system but does occur slightly more commonly on macOS since they removed native python support in macOS 12.3. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #python-programming, #python, #python-tutorials, #programming, #debug, #python-tips, #python-developers, #python3, #hackernoon-es, and more. This story was written by: @smpnjn. Learn more about this writer by checking @smpnjn's about page, and for more stories, please visit hackernoon.com. MacOS has removed native python support in MacOS 12.3.3. The issue is easy to fix. Make sure Python is installed and add python to zsh so that it will run upon typing the `python` command. If you are still facing issues, ensure that `python=$` where the $ sign should equal the path `Python` is installed on. If you still face the same issue, move to step 2. Add python to your zsh profile to run `/usr/bin/python3` when `python is run.

This story was originally published on HackerNoon at: https://hackernoon.com/random-forest-regression-in-r-code-and-interpretation. This story looks into random forest regression in R, focusing on understanding the output and variable importance. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-science, #random-forest, #regression, #variable-importance, #decision-tree, #ensemble-modeling, #blogging-fellowship, #hackernoon-top-story, #hackernoon-es, and more. This story was written by: @nikolao. Learn more about this writer by checking @nikolao's about page, and for more stories, please visit hackernoon.com. Random forest is one of the most popular algorithms for multiple machine learning tasks. This story looks into random forest regression in R, focusing on understanding the output and variable importance. The package with the original implemetation is called randomForest.

This story was originally published on HackerNoon at: https://hackernoon.com/9-best-data-engineering-courses-you-should-take-in-2022. In this listicle, you'll find some of the best data engineering courses, and career paths that can help you jumpstart your data engineering journey! Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-engineering, #data-warehouses, #aws-certification, #data-engineering-courses, #data-science, #artificial-intelligence, #hackernoon-top-story, #blogging-fellowship, and more. This story was written by: @balapriya. Learn more about this writer by checking @balapriya's about page, and for more stories, please visit hackernoon.com. Recently, data engineering has become an increasingly coveted space. With an average salary of over 112K USD, the demand for skilled data engineers is growing with every passing day. Data engineers combine their data and software engineering expertise to facilitate the data infrastructure of an organization. Are you an aspiring data engineer, or someone with experience in the data space—looking to pivot into data engineering?  In this list, you'll find some of the best data engineering courses and career paths that can help you jumpstart your data engineering journey!

This story was originally published on HackerNoon at: https://hackernoon.com/a-beginners-guide-to-understanding-unstructured-data-analysis-with-langchain-and-deepinfra. Let's learn how to extract insights from unstructured data with LangChain and DeepInfra. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-science, #ai, #artificial-intelligence, #guide, #tut, #python, #programming, #big-data, and more. This story was written by: @mikeyoung44. Learn more about this writer by checking @mikeyoung44's about page, and for more stories, please visit hackernoon.com. LangChain and DeepInfra are powerful tools for unstructured data analysis. We'll explore their capabilities, understand the importance of data-driven decisions, and learn how to extract valuable insights. Get ready to uncover hidden patterns and make informed choices using these powerful tools.

This story was originally published on HackerNoon at: https://hackernoon.com/how-to-plot-a-decision-boundary-for-machine-learning-algorithms-in-python-3o1n3w07. Classification algorithms learn how to assign class labels to examples (observations or data points), although their decisions can appear opaque. Check more stories related to data-science at: https://hackernoon.com/c/data-science. You can also check exclusive content about #data-science, #machine-learning, #python3, #python-programming, #python, #python-top-story, #python-tutorials, #python-developers, #hackernoon-es, and more. This story was written by: @kvssetty. Learn more about this writer by checking @kvssetty's about page, and for more stories, please visit hackernoon.com. How To Plot A Decision Boundary For Machine Learning Algorithms in Python is a popular diagnostic for understanding the decisions made by a classification algorithm is the decision surface. This is a plot that shows how a trained machine learning algorithm predicts a coarse grid across the input feature space. A decision surface plot is a powerful tool for understanding how a given model ‘sees’ the prediction task and how it has decided to divide up the feature space by class label. The complete source code is available at my git repository.