Chris Chang’s Post

Brilliant representation of ML algorithms and models #AI #ML

Machine Learning : Algoritmos e Modelos

In the ever-evolving landscape of data analytics, harnessing the power of machine learning models is crucial for optimization. As I dive deeper into the methodologies of linear and logistic regression, as well as decision trees, I've come to appreciate not just their theoretical foundations but their practical applications in real-world scenarios. Linear regression offers a straightforward approach to predicting outcomes based on continuous variables, providing valuable insights into trends and relationships. Logistic regression shines in scenarios requiring binary classification, enabling us to make informed decisions based on probabilities. Decision trees, with their intuitive structure, allow us to visualize decision-making processes and navigate complex datasets with ease. By breaking down information into a series of binary decisions, we can optimize our strategies effectively. Incorporating these models into our workflows not only enhances our analytical capabilities but also drives efficiency and precision in our decision-making processes. Let's continue to leverage these powerful tools to climb new heights in our projects and unlock innovative solutions for our challenges.

Gradient descent is the backbone of many optimization techniques in machine learning, but it can falter with sparse data or high-variance scenarios. These two situations pose unique challenges. With strategic adjustments, gradient descent can remain effective even under challenging data conditions. #datasparsity #gradientdescent #gradientdescenttechniques #highvariance #highvariancedata #machinelearningoptimization #optimizationchallenges #sparsedata

𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝 𝐒𝐜𝐚𝐥𝐞𝐫 𝐢𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠! 📊 Normalization and scaling are critical steps in preprocessing data for machine learning models. Here's an overview of the Standard Scaler, one of the most popular techniques for scaling features. 🛠 𝗪𝗵𝘆 𝗨𝘀𝗲 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱 𝗦𝗰𝗮𝗹𝗲𝗿? A standard scaler is used to standardize features by removing the mean and scaling it to unit variance. This is crucial because: - *Consistency:* Ensures that all features contribute equally to the model performance. - *Improved Performance:* Many algorithms, such as gradient descent-based models, perform better when features are standardized. - *Eliminates Bias:* Prevents larger-scale features from dominating the model training process. 🎯 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀: - *Fit and Transform on Training Data:* Always fit the scaler on the training data and then apply the same transformation to the test data. - *Pipeline Integration:* Use pipelines to automate the scaling process and prevent data leakage. - *Feature Importance:* Remember that scaling doesn't affect the inherent importance of features. 🌟 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗜𝗺𝗽𝗮𝗰𝘁: Properly scaled data can significantly enhance the performance and reliability of your machine-learning models, making them more robust and interpretable in applications such as financial forecasting, medical diagnosis, and customer segmentation.

𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 : 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 & 𝗠𝗼𝗱𝗲𝗹𝘀 - 𝗦𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Trains with labeled data (think answers in a textbook) for tasks like classification (sorting data) and regression (predicting values). - 𝗨𝗻𝘀𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Discovers patterns in unlabeled data, like a detective analyzing clues without a suspect. - 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲: Makes data-driven decisions by accounting for real-world uncertainty. - 𝗘𝗻𝘀𝗲𝗺𝗯𝗹𝗲 𝗠𝗼𝗱𝗲𝗹𝘀: Combines multiple models for improved performance. - 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀: Mimic the brain's structure for complex pattern recognition. - 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Learns by trial and error. - 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝘀: Create new data. - 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝗮𝗹𝗶𝘁𝘆 𝗥𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻: Simplifies complex data while retaining key information. 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗺𝗼𝗿𝗲 𝗶𝗻 𝘁𝗵𝗲 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 ! To get this Image in high quality: 👊 1. Like 2. Comment your thoughts 3. Join my newsletter or send me a DM "MLModel”

Introduction: Data is ubiquitous in our modern world, driving decision-making, innovation, and discovery across diverse domains. However, to truly understand and derive insights from data, it must be effectively represented and analyzed. In this blog post, we'll explore two powerful methods of representing data: graphical and analytical techniques.

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We work with companies that either have no Analytics Department or their existing models were created in spreadsheets, require constant monitoring and updating to keep their models intact, burdening the company with large maintenance costs. As we partner with companies, we’re able to provide modeling and compute power via Machine Learning that outperforms these legacy models substantially at a fraction of the cost. Most legacy models / spreadsheets are built as a reactive tool, meaning they only identify targets or segments after the action has occurred. This is very hard to derive any meaningful use from the model for the business. Another downside is that these models require static analysis. An analyst needs to pull data and send it to another team who starts reviewing and by the time this happens, a week has gone by leading to missed opportunities and stale data in today’s world. Machine Learning Models, when properly implemented, removes all of those complexities and touch points and allow decision makers and executives to interact with the data instead of working up through the layers of the organization. Gamify Data builds our models with the focus on creating Predictive Power, Dynamic Data Adaptation, Personalization and Feature Engineering, Scalability and Automation. We’re not just Data Scientists and Machine Learning Engineers. We’re problem solvers who have built really good tools to help people solve their problems quickly, easily, and efficiently. We build our solutions and automate them so our users can reliably get answers from their data they’re looking for without having to wait or rely on others to pull data for them. Come check out our Portfolio and chat with us about how we can build the best model available in the market for you! www.gamifydata.com #MachineLearning #DataScience #ProductizedML #Automations

Something I’ve been thinking about a lot lately: What experiences drive trust in data and confidence that the data is valid and true? 🤔 What do we collectively know as a business? 🙋♀️ How did we come to KNOW that thing? 🤓 Do we have quantitative data or robust qualitative data to back anecdotal stories? 🫤 Does this data mean what I think it means? Who can tell me what this column represents? 👩💼How can I know if someone(thing) is authoritative? The source of truth? 🤷♀️ Are these metrics written down somewhere? What’s the calculation and who decided it was “correct”? and my very favorite… ⁉️ Why don’t these two numbers in two different reports made by two different departments match? Who is right? Who gets to decide? What does right even mean…. How can u trust EITHER if these reports now?🙃 You can see how perhaps this could spiral…into never ending inquiry and existential crisis. Or perhaps instead it could motivate us to put in place processes, metadata, documentation, decision making frameworks, and improved skills in precision of data generation, definition, operationalization, calculation, and visualization? That was a long sentence, but meant every word and couldn’t none. Can you imagine working somewhere that people value bringing data to decision making, yes, but beyond that the culture of the business strives for excellence in the entire framework of how knowledge is developed and managed across the organization? Thats the world I want to live in. Where collectively producers and consumers of data manage the knowledge of the company across time. And we can run smarter more efficient businesses, and discover more and better things for our customers. AI is super exciting and in fact machine learning overall has been exciting for a while now. But the number one pain point blocking so much innovation? - Difficulty in finding enough high data quality data that is well documented and known to be highly valid. And that is not a data scientist problem alone, that’s a huge business problem for us all. We solve that collectively as a business by constantly improving the culture of data excellence and knowledge management across the entire knowledge lifecycle.