Data science/ML/AI

▎How to Enter Data Science 1. Master the Fundamentals Begin with the foundational skills by learning programming languages like Python and R, which are essential for data analysis and machine learning. Familiarize yourself with key libraries and tools such as Pandas, NumPy, scikit-learn, and TensorFlow for machine learning, as well as Tableau and Matplotlib for data visualization. Online courses, tutorials, and coding bootcamps can provide structured learning paths. 2. Identify Your Niche Data science spans various industries, including healthcare, finance, marketing, and technology. Explore these fields to determine where your interests lie. Understanding the specific challenges and data types in your chosen industry will help you tailor your learning and make you more effective in your future role. 3. Build a Strong Portfolio Start working on small projects that demonstrate your skills and knowledge. These could include data analysis tasks, machine learning models, or visualizations based on publicly available datasets. Use platforms like GitHub to showcase your work, and consider writing blog posts or creating presentations to explain your projects. A well-rounded portfolio not only highlights your technical capabilities but also reflects your problem-solving approach. 4. Engage with the Community Join data science communities online (like Kaggle, Stack Overflow, or LinkedIn groups) to connect with professionals in the field. Participating in discussions, attending webinars, and contributing to open-source projects can enhance your learning experience and expand your network. 5. Pursue Continuous Learning Data science is an ever-evolving field, so staying updated with the latest trends, techniques, and tools is crucial. Follow relevant blogs, podcasts, and research papers. Consider pursuing advanced certifications or degrees to deepen your expertise. 6. Gain Practical Experience Look for internships, volunteer opportunities, or part-time positions that allow you to apply your skills in real-world scenarios. Practical experience will not only reinforce your learning but also give you insights into the day-to-day responsibilities of a data scientist. By following these steps, you can build a solid foundation in data science and position yourself for success in this dynamic and rewarding field.

▎Common Machine Learning Terms 1. Algorithm: A set of rules or steps used to solve a problem or perform a task, particularly in the context of data processing and analysis. 2. Model: A mathematical representation of a real-world process, created by training an algorithm on data. 3. Training Data: The dataset used to train a machine learning model, consisting of input-output pairs. 4. Test Data: A separate dataset used to evaluate the performance of a trained model, ensuring it generalizes well to unseen data. 5. Overfitting: A modeling error that occurs when a model learns the training data too well, capturing noise along with the underlying pattern, leading to poor performance on new data. 6. Underfitting: A situation where a model is too simple to capture the underlying trend in the data, resulting in poor performance on both training and test datasets. 7. Feature: An individual measurable property or characteristic of the data used as input for a model. 8. Label: The output or target variable that a model aims to predict based on the input features. 9. Supervised Learning: A type of machine learning where the model is trained on labeled data, learning to map inputs to outputs. 10. Unsupervised Learning: A type of machine learning where the model is trained on unlabeled data, aiming to find patterns or groupings within the data. 11. Reinforcement Learning: A type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative reward. 12. Hyperparameters: Configuration settings used to control the training process of a model, which are set before training begins. 13. Loss Function: A mathematical function that quantifies how well a model's predictions match the actual outcomes; used to guide the optimization process. 14. Gradient Descent: An optimization algorithm used to minimize the loss function by iteratively adjusting model parameters in the direction of the steepest descent. 15. Cross-Validation: A technique for assessing how the results of a model will generalize by dividing the dataset into multiple subsets and training/testing across them. 16. Confusion Matrix: A table used to evaluate the performance of a classification model by comparing predicted labels against actual labels. 17. Precision and Recall: Metrics used to evaluate classification models; precision measures the accuracy of positive predictions, while recall measures the ability to find all relevant instances. 18. ROC Curve (Receiver Operating Characteristic Curve): A graphical representation of a model's diagnostic ability across various threshold settings, plotting true positive rates against false positive rates. 19. Regularization: Techniques used to prevent overfitting by adding a penalty for complexity to the loss function (e.g., L1 and L2 regularization). 20. Ensemble Learning: Combining multiple models to improve overall performance; common methods include bagging, boosting, and stacking.

TradingAgents: Multi-Agents LLM Financial Trading Framework 📅 Publication Date: Dec 28, 2024 📑 Paper: https://arxiv.org/pdf/2412.20138 🔗 Code: https://github.com/tauricresearch/tradingagents 🚀 Spaces citing this paper: • https://huggingface.co/spaces/shanghengdu/LLM-Agent-Optimization-PaperList • https://huggingface.co/spaces/tahp0604/ai-stock-watchlist 📝 Description: The paper introduces TradingAgents, a multi-agent framework that utilizes large language models for stock trading, simulating the collaborative dynamics of real-world trading firms. The framework consists of various agents, including fundamental analysts, sentiment analysts, technical analysts, and traders with different risk profiles, all powered by large language models. These agents work together to assess market conditions, manage risk, and make informed trading decisions. The framework also includes researcher agents that evaluate market conditions and a risk management team that monitors exposure.

What is an Agenetic AI?

🧠 The Statistical Illusion: Simpson’s Paradox 🎭 Imagine you are choosing a hospital for a surgery. • Hospital A has a higher survival rate than Hospital B for "Easy" cases. • Hospital A also has a higher survival rate for "Hard" cases. Common sense says: Choose Hospital A. But when you look at the total combined data, Hospital B actually has a higher survival rate. 🤯 This is Simpson’s Paradox: A trend appears in several different groups but disappears or reverses when these groups are combined. 🔍 Why does this happen? It happens because of a Lurking Variable (a hidden factor). In this case, Hospital A is a world-class facility, so it takes on way more "Hard" cases than Hospital B. Even though they are better at both types, the high volume of risky surgeries drags their overall average down. 🐍 See the Paradox in Code Let's simulate this "impossible" scenario using Python:

    
        import pandas as pd

# Data: [Successes, Total Attempts]
data = {
    'Hospital': ['A', 'A', 'B', 'B'],
    'Case_Type': ['Easy', 'Hard', 'Easy', 'Hard'],
    'Survived': [95, 10, 90, 70], 
    'Total': [100, 100, 100, 1000] 
}
df = pd.DataFrame(data)

# 1. Check rates per group
df['Rate'] = df['Survived'] / df['Total']
print("--- Rates by Group ---")
print(df[['Hospital', 'Case_Type', 'Rate']])

# 2. Check overall rates
overall = df.groupby('Hospital').sum()
overall['Overall_Rate'] = overall['Survived'] / overall['Total']
print("\n--- Overall Rates (The Paradox!) ---")
print(overall['Overall_Rate'])
{}
    

The Result: • A is better at Easy (95% vs 90%). • A is better at Hard (10% vs 7%). • BUT... Overall, B wins (14% vs 52%) because B mostly did "Easy" cases. 🛠 How to avoid being fooled? 1. Don't trust the aggregate: When analyzing data, always try to "segment" or "drill down" into sub-groups. 2. Look for the Weight: Ask yourself: "Is one group disproportionately represented in the total?" 3. Identify the Lurking Variable: What context is missing? (e.g., Age, Severity, Time of Day). 🎯 The Takeaway In Data Science, the "Big Picture" can sometimes be a big lie. If your analysis produces a result that defies logic, you might be looking at a Simpson’s Paradox. Always slice your data before you trust it.

Hey everyone 👋 I know I promised to create a Data Science course. I was working on that late last year, but since early 2026 I’ve had some health issues, so they got postponed. I’ll get back to them as soon as I’m better 🙌 In the meantime, I launched this ☝️ today: https://learndevs.com/ I started building this back in the 2020s, together with many of you. It’s not perfect yet, but better to have it now than wait forever. Would love your feedback ❤️

We’re live 🚀 After 4 years of work, I finally launched: 👉 learndevs.com

Goal: one place for everything a developer needs (free courses, tech news, job offers, manually written blogs. best github repos etc)

A lot of you contributed by writing code or adding courses and knowledge along the way. This is as much yours as it is mine 🙌 And I’m already working on: • Personalized roadmaps • Live chat • Better job search & placement Try it and please tell me: What would you add next? Reminder that if you want early access to new features, Join our beta testers group. Looking for people who will explore, break things, and share honest feedback.