Data science/ML/AI

Hands On Python Data Science - Data Science Bootcamp Master Python for Data Science with Real-World Applications: Dive Deep into Data Analysis, Machine Learning Rating ⭐️: 4.3 out 5 Students 👨‍🎓 : 4865 Duration ⏰ : 5.5 hours on-demand video Created by 👨‍🏫: Sayman Creative Institute 🔗 COURSE LINK ⚠️ Its free for first 1000 enrollments only! #datascience #python ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @datascience_bds for more👈

macos OSX (macOS) inside a Docker container. Creator: Dockur Stars ⭐️: 5.2k Forked By: 185 https://github.com/dockur/macos #datascience ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool repositories. *This channel belongs to @bigdataspecialist group

Forecasting vs. Predictive Analytics: The Obama Example Analytics can influence elections, not just predict them. This article explores how the Obama campaign used predictive analytics to outmaneuver traditional forecasting. Forecasting vs. Predictive Analytics Nate Silver’s forecasting predicted state outcomes, while Obama’s team used predictive analytics to score individual voters, targeting those most likely to be persuaded. Impact of Predictive Analytics The Obama campaign optimized interactions, avoiding “do-not-disturb” voters and improving ad spending effectiveness by 18%. Conclusion Predictive analytics enables organizations to shape outcomes through personalized insights, distinguishing it from forecasting’s broad predictions.

Essential Machine Learning Algorithms for Data Scientists Master essential machine learning algorithms and elevate your data science skills Rating ⭐️: 4.6 out 5 Students 👨‍🎓 : 791 Duration ⏰ : 43min of on-demand video Created by 👨‍🏫: Arunkumar Krishnan 🔗 Course Link #ml #algorithm ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @datascience_bds for more👈

streamlit Streamlit — A faster way to build and share data apps. Creator: Streamlit Stars ⭐️: 35.4k Forked By: 3.1k https://github.com/streamlit/streamlit #datascience ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool repositories. *This channel belongs to @bigdataspecialist group

12 Fundamental Math Theories Needed to Understand AI 1. Curse of Dimensionality This phenomenon occurs when analyzing data in high-dimensional spaces. As dimensions increase, the volume of the space grows exponentially, making it challenging for algorithms to identify meaningful patterns due to the sparse nature of the data. 2. Law of Large Numbers A cornerstone of statistics, this theorem states that as a sample size grows, its mean will converge to the expected value. This principle assures that larger datasets yield more reliable estimates, making it vital for statistical learning methods. 3. Central Limit Theorem This theorem posits that the distribution of sample means will approach a normal distribution as the sample size increases, regardless of the original distribution. Understanding this concept is crucial for making inferences in machine learning. 4. Bayes’ Theorem A fundamental concept in probability theory, Bayes’ Theorem explains how to update the probability of your belief based on new evidence. It is the backbone of Bayesian inference methods used in AI. 5. Overfitting and Underfitting Overfitting occurs when a model learns the noise in training data, while underfitting happens when a model is too simplistic to capture the underlying patterns. Striking the right balance is essential for effective modeling and performance. 6. Gradient Descent This optimization algorithm is used to minimize the loss function in machine learning models. A solid understanding of gradient descent is key to fine-tuning neural networks and AI models. 7. Information Theory Concepts like entropy and mutual information are vital for understanding data compression and feature selection in machine learning, helping to improve model efficiency. 8. Markov Decision Processes (MDP) MDPs are used in reinforcement learning to model decision-making scenarios where outcomes are partly random and partly under the control of a decision-maker. This framework is crucial for developing effective AI agents. 9. Game Theory Old school AI is based off game theory. This theory provides insights into multi-agent systems and strategic interactions among agents, particularly relevant in reinforcement learning and competitive environments. 10. Statistical Learning Theory This theory is the foundation of regression, regularization and classification. It addresses the relationship between data and learning algorithms, focusing on the theoretical aspects that govern how models learn from data and make predictions. 11. Hebbian Theory This theory is the basis of neural networks, “Neurons that fire together, wire together”. Its a biology theory on how learning is done on a cellular level, and as you would have it — Neural Networks are based off this theory. 12. Convolution (Kernel) Not really a theory and you don’t need to fully understand it, but this is the mathematical process on how masks work in image processing. Convolution matrix is used to combine two matrixes and describes the overlap. Special thanks to Jiji Veronica Kim for this list. ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool repositories. *This channel belongs to @bigdataspecialist group

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Data Science Portfolios, Speeding Up Python, KANs, and Other May Must-Reads Python One Billion Row Challenge — From 10 Minutes to 4 Seconds With a longstanding reputation for slowness, you’d think that Python wouldn’t stand a chance at doing well in the popular “one billion row” challenge. Dario Radečić’s viral post aims to show that with some flexibility and outside-the-box thinking, you can still squeeze impressive time savings out of your code. N-BEATS — The First Interpretable Deep Learning Model That Worked for Time Series Forecasting Anyone who enjoys a thorough look into a model’s inner workings should bookmark Jonte Dancker’s excellent explainer on N-BEATS, the “first pure deep learning approach that outperformed well-established statistical approaches” for time-series forecasting tasks. Build a Data Science Portfolio Website with ChatGPT: Complete Tutorial In a competitive job market, data scientists can’t afford to be coy about their achievements and expertise. A portfolio website can be a powerful way to showcase both, and Natassha Selvaraj’s patient guide demonstrates how you can build one from scratch with the help of generative-AI tools. A Complete Guide to BERT with Code Why not take a step back from the latest buzzy model to learn about those precursors that made today’s innovations possible? Bradney Smith invites us to go all the way back to 2018 (or several decades ago, in AI time) to gain a deep understanding of the groundbreaking BERT (Bidirectional Encoder Representations from Transformers) model. Why LLMs Are Not Good for Coding — Part II Back in the present day, we keep hearing about the imminent obsolescence of programmers as LLMs continue to improve. Andrea Valenzuela’s latest article serves as a helpful “not so fast!” interjection, as she focuses on their inherent limitations when it comes to staying up-to-date with the latest libraries and code functionalities. PCA & K-Means for Traffic Data in Python What better way to round out our monthly selection than with a hands-on tutorial on a core data science workflow? In her debut TDS post, Beth Ou Yang walks us through a real-world example—traffic data from Taiwan, in this case—of using principle component analysis (PCA) and K-means clustering.