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K-means clustering helps group similar songs together, even without explicit labels. https://www.pospiech.space/blog/cds-module5-unsupervised-learning monthly 0.5 2024-11-11 https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/2bdbc542-db3d-4db7-9cd3-9e71f5882535/cover.jpg Blog - Citizen Data Scientist, Module V: Unsupervised Learning: Discovering hidden patterns - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/9e963fb8-c2b5-414f-987e-9287a5f1041b/Penguins.jpg Blog - Citizen Data Scientist, Module V: Unsupervised Learning: Discovering hidden patterns - Clustering Penguins If you have data about penguins (e.g., bill length, flipper length, body mass), K-means clustering can group them by species without knowing the labels in advance. This is helpful for discovering natural groupings in datasets https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/3cc1f815-bbfe-42d4-8c4f-0ecdb54c9829/credit.jpg Blog - Citizen Data Scientist, Module V: Unsupervised Learning: Discovering hidden patterns - Detecting Credit Card Fraud Banks use anomaly detection to flag transactions that don’t align with a customer's usual spending behavior. For instance, if a card is suddenly used for a large purchase in another country, anomaly detection can identify this as a potential case of fraud https://www.pospiech.space/blog/fsi-1-way-coupling monthly 0.5 2025-12-31 https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/f1a2e0cc-b759-4045-addb-fb96c45881e6/Cover.jpg Blog - Fluid-Structure Interaction Chpater 2: One-way physics coupling https://www.pospiech.space/blog/cds-module4-applying-ds-in-practice monthly 0.5 2024-11-03 https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/7cb5ef80-e9fd-4f88-b4b6-18fccac1cfc7/Cover.jpg Blog - Citizen Data Scientist, Module IV: Applying Data Science in Practice: Feature Engineering, Scaling, and Selection https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/1374cbd6-ef08-49f8-b0fe-69582810f34b/cancer.jpg Blog - Citizen Data Scientist, Module IV: Applying Data Science in Practice: Feature Engineering, Scaling, and Selection - Breast Cancer dataset In the assignment, we used the breast cancer dataset to predict whether a tumor is benign or malignant. Some features, like the mean radius and mean perimeter, have very different scales. After scaling, we saw that the model’s performance improved significantly because it no longer prioritized features with larger raw values https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/dec20359-d97f-4863-9425-2c837ccc14bf/forwards_backwards.jpg Blog - Citizen Data Scientist, Module IV: Applying Data Science in Practice: Feature Engineering, Scaling, and Selection - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/3170cf89-9424-470e-9734-8fd817a0fba5/biasVVariance.png Blog - Citizen Data Scientist, Module IV: Applying Data Science in Practice: Feature Engineering, Scaling, and Selection - Predicting Income from Age If we use a simple linear regression to predict income from age, our model might have high bias - it won’t capture the complexity of real-world income trends. But if we use a very complex model, we might capture random fluctuations in the data, leading to high variance. Finding the right balance ensures the model generalizes well to new data /Photo source/ https://www.pospiech.space/blog/cds-module3-measuring-metrics monthly 0.5 2024-10-20 https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/1728674040642-7L7T1P6QSLGEUOR262M3/Cover.jpg Blog - Citizen Data Scientist, Module III: Measuring Model Performance: Metrics That Matter https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/d39c90d1-2975-47be-94af-acd79a14c820/eqn_1.png Blog - Citizen Data Scientist, Module III: Measuring Model Performance: Metrics That Matter - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/5df7532b-7a9b-4803-ab51-7f8cc9eacb0b/eqn_2.png Blog - Citizen Data Scientist, Module III: Measuring Model Performance: Metrics That Matter - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/49e0abf4-e252-4977-868f-845aabb182f6/eqn_3.png Blog - Citizen Data Scientist, Module III: Measuring Model Performance: Metrics That Matter - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/f6626c83-7d08-4556-80dd-033561f691dd/Recall.jpg Blog - Citizen Data Scientist, Module III: Measuring Model Performance: Metrics That Matter - When Recall Matters More Than Precision Imagine you're developing a model to detect a rare but dangerous machine failure in an industrial setting. If the failure occurs, it could cause millions of dollars in damage. In this case, recall is crucial because missing even one failure (false negative) could be catastrophic. The model should flag every possible failure case, even if that means some false positives (incorrect alarms). False alarms can be handled, but missing a critical failure is much worse. Conversely, in tasks where false positives are costly or problematic (e.g., spam filters where marking legitimate emails as spam is highly undesirable), precision would take priority . https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/5a74fb40-d0f4-4bd7-a90e-a20937513a20/Overfitting.png Blog - Citizen Data Scientist, Module III: Measuring Model Performance: Metrics That Matter - Overfitting in Predictive Maintenance Imagine building a predictive maintenance model that uses thousands of sensor data points from machines. If the model is overfitting, it might perfectly predict when certain machines need maintenance in the training set but fail to generalize to new machines with slightly different behaviors. As a result, the model would have high variance and may perform poorly in real-world applications /Photo source/ https://www.pospiech.space/blog/cds-module2-supervised-learning monthly 0.5 2024-10-11 https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/9a42579b-0108-4d5a-ba04-26e5dff75b2c/Cover.jpg Blog - Citizen Data Scientist, Module II: Supervised learning: Predicting the Future with Labeled Data https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/358fcf85-d60a-4277-8483-17943f21ccd0/eq_1.png Blog - Citizen Data Scientist, Module II: Supervised learning: Predicting the Future with Labeled Data - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/6708af17-358d-4909-88fa-7dc906edcd42/eq_2.png Blog - Citizen Data Scientist, Module II: Supervised learning: Predicting the Future with Labeled Data - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/9dd7c8f7-c812-44ca-8b52-38e11b1796d8/eq_3.png Blog - Citizen Data Scientist, Module II: Supervised learning: Predicting the Future with Labeled Data - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/2708157d-bef6-4ba3-9b7c-b67af78cfea1/Predictions.jpg Blog - Citizen Data Scientist, Module II: Supervised learning: Predicting the Future with Labeled Data - Predictions True Positive (TP): The model correctly predicts a positive class (e.g., predicting "spam" when the email is actually spam) False Positive (FP): The model incorrectly predicts a positive class (e.g., predicting "spam" when the email is not spam) False Negative (FN): The model incorrectly predicts a negative class (e.g., predicting "not spam" when the email is spam) https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/0a1b528f-9c7c-496f-ae59-57b72afee850/eq_4.png Blog - Citizen Data Scientist, Module II: Supervised learning: Predicting the Future with Labeled Data - Gradient Descent Imagine the cost function as a landscape with hills and valleys. The goal is to find the lowest point (the minimum error). At each step, gradient descent calculates the slope and moves the parameters in the direction of the steepest descent until it reaches a minimum https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/b890d573-ed9e-44f4-bec0-c2025cd1b720/DecisionTrees.jpg Blog - Citizen Data Scientist, Module II: Supervised learning: Predicting the Future with Labeled Data - Decision Trees Decision trees are another popular method for both regression and classification tasks. They work by recursively splitting the dataset based on the feature that maximizes information gain. Each decision node asks a yes/no question, leading to further branches until a final prediction is made. https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/b44adf49-d4a1-4923-9b6b-4ae201f1c5b5/eq_5.png Blog - Citizen Data Scientist, Module II: Supervised learning: Predicting the Future with Labeled Data - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/6dff4620-93a2-4a72-ba39-bbacd5ce4fe3/PredictingLoan.jpg Blog - Citizen Data Scientist, Module II: Supervised learning: Predicting the Future with Labeled Data - Predicting Loan Repayment Imagine we’re building a decision tree to predict whether a customer will repay a loan. We might start with a question like "Is the customer employed?" If yes, we proceed with further splits like "Does the customer have savings over $10,000?" Each branch of the tree represents a decision path that leads to a final prediction https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/26c78458-86e9-4309-b27f-d5dd18c9492b/Gradient.jpg Blog - Citizen Data Scientist, Module II: Supervised learning: Predicting the Future with Labeled Data - Gradient Descent Imagine the cost function as a landscape with hills and valleys. The goal is to find the lowest point (the minimum error). At each step, gradient descent calculates the slope and moves the parameters in the direction of the steepest descent until it reaches a minimum https://www.pospiech.space/blog/fsi-overview monthly 0.5 2025-12-31 https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/a0a8768f-d794-4660-b661-46920d27c3ed/Cover.jpg Blog - Fluid-Structure Interaction Chapter 1: Introduction https://www.pospiech.space/blog/cds-module1-fundamentals monthly 0.5 2024-10-11 https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/6d465337-a796-40d6-970b-d2b2ad7daec2/Cover.jpg Blog - Citizen Data Scientist, Module I: Introduction to Data Science: Laying the Foundation https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/f39d42b1-8053-4164-9d56-457969b0ea2c/AI.jpg Blog - Citizen Data Scientist, Module I: Introduction to Data Science: Laying the Foundation - The most common terminologies: Data Science: The core discipline that leverages scientific methods, algorithms, and statistical models to extract knowledge from structured and unstructured data. It can be applied to predict future trends, as demonstrated in use cases like sales forecasting and image classification Artificial Intelligence (AI): The overarching field that encompasses intelligent systems capable of performing tasks typically requiring human intelligence. Machine Learning: A subset of AI, focused on creating systems that learn and improve over time as they are exposed to more data Deep Learning: A specific subset of ML, involving neural networks with many layers, suited for handling vast amounts of unstructured data, such as images or text. https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/f6cb1e41-9c42-4652-8fef-e15e0b663f81/CRISP_DM.png Blog - Citizen Data Scientist, Module I: Introduction to Data Science: Laying the Foundation - CRISP-DM: Business Understanding: Defining the problem and understanding the business goals Data Understanding: Identifying available data, its quality, and how it can address the business problem Data Preparation: Cleaning, transforming, and structuring the data for modeling Modeling: Selecting the appropriate models based on the problem and data characteristics Evaluation: Assessing the model’s performance and making adjustments as needed Deployment: Implementing the model into a real-world setting and monitoring its performance /Photo source/ https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/a860cc8f-b589-4b91-b26e-bcb877065e76/ML+Categories.png Blog - Citizen Data Scientist, Module I: Introduction to Data Science: Laying the Foundation - ML Classification: Supervised Learning: Models are trained on labeled data to predict outcomes. For instance, a model might classify whether a car is defective based on a set of images Unsupervised Learning: Involves finding patterns in data without labeled outcomes. A common example is clustering customers into different groups based on behavior Reinforcement Learning: Used in applications like game AI, where an agent learns by interacting with its environment and receiving feedback /Photo source/ https://www.pospiech.space/blog/getting-started-with-python monthly 0.5 2024-10-11 https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/039032ac-f4bf-4d5d-b0c7-bee42ac0901c/python-2195106_1280.jpg Blog - Getting Started with Python for Data Science: A Hands-On Introduction https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/631f9fc7-e5df-42b3-9658-0f261957db33/Python.jpg Blog - Getting Started with Python for Data Science: A Hands-On Introduction - Key Concepts Data Types & Variables: Understanding how Python handles different data types is crucial for manipulating and analyzing data efficiently. I explored integers, floats, strings, and more complex data structures like lists and dictionaries. Control Structures: Loops and conditionals are the backbone of complex logic. This section taught me how to make decisions and repeat actions in Python, essential for handling data processing tasks. Functions: Reusable blocks of code that make your scripts more modular and easier to maintain. This concept is vital for writing clean, organized code that can handle repetitive tasks with ease. https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/f6e53166-6787-4e38-9943-97a16b4cac1a/Pandas.jpg Blog - Getting Started with Python for Data Science: A Hands-On Introduction - Key Techniques DataFrame Operations: Creating and manipulating dataframes to filter, sort, and group data. These operations are key to organizing and preparing data for further analysis. Handling Missing Data: Techniques for dealing with missing values, which is a common issue in real-world datasets. Pandas offers several ways to fill in or drop missing data, ensuring the integrity of your dataset. Merging and Joining: Combining data from multiple sources, a skill that’s particularly useful in data integration tasks. Understanding how to merge and join dataframes is crucial for working with complex datasets that require information from various sources. https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/030aa301-2d3f-476a-aa13-edac7681a70e/Graphs.png Blog - Getting Started with Python for Data Science: A Hands-On Introduction - Key techniques Plotting with Matplotlib: Basics of creating line plots, bar charts, and scatter plots. These fundamental visualizations are crucial for presenting data in a digestible format. Advanced Visualizations with Seaborn: Creating complex visualizations like heatmaps and pair plots to uncover relationships in data. Seaborn’s integration with Pandas makes it a powerful tool for generating detailed and aesthetically pleasing graphics with minimal code. https://images.squarespace-cdn.com/content/v1/669cf57ce130f621b7e26af4/a11b0131-4a63-48ad-a5c5-bb66db6d1230/AI.jpg Blog - Getting Started with Python for Data Science: A Hands-On Introduction - Key techniques Numerical Computations with Numpy: Efficiently handling large arrays and performing operations like matrix multiplication. Numpy is the foundation for numerical computing in Python, offering the speed and efficiency needed for large-scale data processing. Building Models with Scikit-Learn: Implementing machine learning algorithms, from linear regression to more complex classifiers. 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