Machine Learning with Python

A new interactive sentiment visualization project has been developed, featuring a dynamic smiley face that reflects sentiment analysis results in real time. Using a natural language processing model, the system evaluates input text and adjusts the smiley face expression accordingly: 🙂 Positive sentiment ☹️ Negative sentiment The visualization offers an intuitive and engaging way to observe sentiment dynamics as they happen. 🔗 GitHub: https://lnkd.in/e_gk3hfe 📰 Article: https://lnkd.in/e_baNJd2 #AI #SentimentAnalysis #DataVisualization #InteractiveDesign #NLP #MachineLearning #Python #GitHubProjects #TowardsDataScience 🔗 Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk 📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

9 machine learning concepts for ML engineers! (explained as visually as possible) Here's a recap of several visual summaries posted in the Daily Dose of Data Science newsletter. 1️⃣ 4 strategies for Multi-GPU Training. - Training at scale? Learn these strategies to maximize efficiency and minimize model training time. - Read here: https://lnkd.in/gmXF_PgZ 2️⃣ 4 ways to test models in production - While testing a model in production might sound risky, ML teams do it all the time, and it isn’t that complicated. - Implemented here: https://lnkd.in/g33mASMM 3️⃣ Training & inference time complexity of 10 ML algorithms Understanding the run time of ML algorithms is important because it helps you: - Build a core understanding of an algorithm. - Understand the data-specific conditions to use the algorithm - Read here: https://lnkd.in/gKJwJ__m 4️⃣ Regression & Classification Loss Functions. - Get a quick overview of the most important loss functions and when to use them. - Read here: https://lnkd.in/gzFPBh-H 5️⃣ Transfer Learning, Fine-tuning, Multitask Learning, and Federated Learning. - The holy grail of advanced learning paradigms, explained visually. - Learn about them here: https://lnkd.in/g2hm8TMT 6️⃣ 15 Pandas to Polars to SQL to PySpark Translations. - The visual will help you build familiarity with four popular frameworks for data analysis and processing. - Read here: https://lnkd.in/gP-cqjND 7️⃣ 11 most important plots in data science - A must-have visual guide to interpret and communicate your data effectively. - Explained here: https://lnkd.in/geMt98tF 8️⃣ 11 types of variables in a dataset Understand and categorize dataset variables for better feature engineering. - Explained here: https://lnkd.in/gQxMhb_p 9️⃣ NumPy cheat sheet for data scientists - The ultimate cheat sheet for fast, efficient numerical computing in Python. - Read here: https://lnkd.in/gbF7cJJE

#MachineLearning #DataScience #MLEngineering #DeepLearning #AI #MLOps #BigData #Python #NumPy #Pandas #Visualization

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📀 55+ AI and Data Science Projects 💻 Often you read all these articles, watch online courses, but until you do a practical project, start coding, and implement the concepts in practice, you don't learn anything. 🔸 Here is a list of 55 projects in different categories:👇 1⃣ Large language models 🔸 Link 🔢 Fine-tuning LLMs 🔸 Link 🔢 Time series data analysis 🔸 Link 🔢 Computer Vision 🔸 Link 🔢 Data Science 🔸 Link ➖➖➖➖➖ ⏪ You can also access all of the above projects through the following GitHub repo: 👇 ┌ 📂 AI Data Guided Projects └🐱 GitHub-Repos Join to our WhatsApp 💬channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗮𝗹 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 (𝗣𝗖𝗔) 𝗧𝗵𝗲 𝗔𝗿𝘁 𝗼𝗳 𝗥𝗲𝗱𝘂𝗰𝗶𝗻𝗴 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝘀 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗟𝗼𝘀𝗶𝗻𝗴 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗪𝗵𝗮𝘁 𝗘𝘅𝗮𝗰𝘁𝗹𝘆 𝗜𝘀 𝗣𝗖𝗔? ⤷ 𝗣𝗖𝗔 is a 𝗺𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝗮𝗹 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲 used to transform a 𝗵𝗶𝗴𝗵-𝗱𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝗮𝗹 dataset into fewer dimensions, while retaining as much 𝘃𝗮𝗿𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 (𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻) as possible. ⤷ Think of it as “𝗰𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗻𝗴” data, similar to how we reduce the size of an image without losing too much detail. 𝗪𝗵𝘆 𝗨𝘀𝗲 𝗣𝗖𝗔 𝗶𝗻 𝗬𝗼𝘂𝗿 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀? ⤷ 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝘆 your data for 𝗲𝗮𝘀𝗶𝗲𝗿 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 and 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 ⤷ 𝗘𝗻𝗵𝗮𝗻𝗰𝗲 machine learning models by reducing 𝗰𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝗼𝘀𝘁 ⤷ 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲 multi-dimensional data in 2𝗗 or 3𝗗 for insights ⤷ 𝗙𝗶𝗹𝘁𝗲𝗿 𝗼𝘂𝘁 𝗻𝗼𝗶𝘀𝗲 and uncover hidden patterns in your data 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗮𝗹 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 ⤷ The 𝗳𝗶𝗿𝘀𝘁 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗮𝗹 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 is the direction in which the data varies the most. ⤷ Each subsequent component represents the 𝗻𝗲𝘅𝘁 𝗵𝗶𝗴𝗵𝗲𝘀𝘁 𝗿𝗮𝘁𝗲 of variance, but is 𝗼𝗿𝘁𝗵𝗼𝗴𝗼𝗻𝗮𝗹 (𝘂𝗻𝗰𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗲𝗱) to the previous one. ⤷ The challenge is selecting how many components to keep based on the 𝘃𝗮𝗿𝗶𝗮𝗻𝗰𝗲 they explain. 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 1: 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗦𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Imagine you’re working on a project to 𝘀𝗲𝗴𝗺𝗲𝗻𝘁 customers for a marketing campaign, with data on spending habits, age, income, and location. ⤷ Using 𝗣𝗖𝗔, you can reduce these four variables into just 𝘁𝘄𝗼 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗮𝗹 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 that retain 90% of the variance. ⤷ These two new components can then be used for 𝗸-𝗺𝗲𝗮𝗻𝘀 clustering to identify distinct customer groups without dealing with the complexity of all the original variables. 𝗧𝗵𝗲 𝗣𝗖𝗔 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 — 𝗦𝘁𝗲𝗽-𝗕𝘆-𝗦𝘁𝗲𝗽 ⤷ 𝗦𝘁𝗲𝗽 𝟭: 𝗗𝗮𝘁𝗮 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Ensure your data is on the same scale (e.g., mean = 0, variance = 1). ⤷ 𝗦𝘁𝗲𝗽 𝟮: 𝗖𝗼𝘃𝗮𝗿𝗶𝗮𝗻𝗰𝗲 𝗠𝗮𝘁𝗿𝗶𝘅 Calculate how features are correlated. ⤷ 𝗦𝘁𝗲𝗽 𝟯: 𝗘𝗶𝗴𝗲𝗻 𝗗𝗲𝗰𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 Compute the eigenvectors and eigenvalues to determine the principal components. ⤷ 𝗦𝘁𝗲𝗽 𝟰: 𝗦𝗲𝗹𝗲𝗰𝘁 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 Choose the top-k components based on the explained variance ratio. ⤷ 𝗦𝘁𝗲𝗽 𝟱: 𝗗𝗮𝘁𝗮 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 Transform your data onto the new 𝗣𝗖𝗔 space with fewer dimensions. 𝗪𝗵𝗲𝗻 𝗡𝗼𝘁 𝘁𝗼 𝗨𝘀𝗲 𝗣𝗖𝗔 ⤷ 𝗣𝗖𝗔 is not suitable when the dataset contains 𝗻𝗼𝗻-𝗹𝗶𝗻𝗲𝗮𝗿 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀 or 𝗵𝗶𝗴𝗵𝗹𝘆 𝘀𝗸𝗲𝘄𝗲𝗱 𝗱𝗮𝘁𝗮. ⤷ For non-linear data, consider 𝗧-𝗦𝗡𝗘 or 𝗮𝘂𝘁𝗼𝗲𝗻𝗰𝗼𝗱𝗲𝗿𝘀 instead. https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A 📱

𝐊-𝐌𝐞𝐚𝐧𝐬 𝐂𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝 - 𝐟𝐨𝐫 𝐛𝐞𝐠𝐢𝐧𝐧𝐞𝐫𝐬 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐊-𝐌𝐞𝐚𝐧𝐬? It’s an unsupervised machine learning algorithm that automatically groups your data into K similar clusters without labels. It finds hidden patterns using distance-based similarity. 𝐈𝐧𝐭𝐮𝐢𝐭𝐢𝐯𝐞 𝐞𝐱𝐚𝐦𝐩𝐥𝐞: You run a mall. Your data has: › Age › Annual Income › Spending Score K-Means can divide customers into: ⤷ Budget Shoppers ⤷ Mid-Range Customers ⤷ High-End Spenders 𝐇𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬: ① Choose the number of clusters K ② Randomly initialize K centroids ③ Assign each point to its nearest centroid ④ Move centroids to the mean of their assigned points ⑤ Repeat until centroids don’t move (convergence) 𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞: Minimize the total squared distance between data points and their cluster centroids 𝐉 = Σ‖𝐱ᵢ - μⱼ‖² Where 𝐱ᵢ = data point, μⱼ = cluster center 𝐇𝐨𝐰 𝐭𝐨 𝐩𝐢𝐜𝐤 𝐊: Use the Elbow Method ⤷ Plot K vs. total within-cluster variance ⤷ The “elbow” in the curve = ideal number of clusters 𝐂𝐨𝐝𝐞 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 (𝐒𝐜𝐢𝐤𝐢𝐭-𝐋𝐞𝐚𝐫𝐧):

from sklearn.cluster import KMeans
X = [[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]]
model = KMeans(n_clusters=2, random_state=0)
model.fit(X)
print(model.labels_)
print(model.cluster_centers_)

𝐁𝐞𝐬𝐭 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬: ⤷ Customer segmentation ⤷ Image compression ⤷ Market analysis ⤷ Social network analysis 𝐋𝐢𝐦𝐢𝐭𝐚𝐭𝐢𝐨𝐧𝐬: › Sensitive to outliers › Requires you to predefine K › Works best with spherical clusters https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A 📱

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Introduction to Machine Learning” by Alex Smola and S.V.N. Vishwanathan is a foundational textbook that offers a comprehensive and mathematically rigorous introduction to core concepts in machine learning. The book covers key topics including supervised and unsupervised learning, kernels, graphical models, optimization techniques, and large-scale learning. It balances theory and practical application, making it ideal for graduate students, researchers, and professionals aiming to deepen their understanding of machine learning fundamentals and algorithmic principles. PDF: https://alex.smola.org/drafts/thebook.pdf

#MachineLearning #AI #DataScience #MLAlgorithms #DeepLearning #MathForML #MLTheory #MLResearch #AlexSmola #SVNVishwanathan

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