Data Analytics
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Dive into the world of Data Analytics โ€“ uncover insights, explore trends, and master data-driven decision making.

Admin: @HusseinSheikho || @Hussein_Sheikho
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These Google Colab-notebooks help to implement all machine learning algorithms from scratch ๐Ÿคฏ

Repo: https://udlbook.github.io/udlbook/


๐Ÿ‘‰ @codeprogrammer
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Ant AI Automated Sales Robot is an intelligent robot focused on automating lead generation and sales conversion. Its core function simulates human conversation, achieving end-to-end business conversion and easily generating revenue without requiring significant time investment.

I. Core Functions: Fully Automated "Lead Generation - Interaction - Conversion"

Precise Lead Generation and Human-like Communication: Ant AI is trained on over 20 million real social chat records, enabling it to autonomously identify target customers and build trust through natural conversation, requiring no human intervention.

High Conversion Rate Across Multiple Scenarios: Ant AI intelligently recommends high-conversion-rate products based on chat content, guiding customers to complete purchases through platforms such as iFood, Shopee, and Amazon. It also supports other transaction scenarios such as movie ticket purchases and utility bill payments.

24/7 Operation: Ant AI continuously searches for customers and recommends products. You only need to monitor progress via your mobile phone, requiring no additional management time.

II. Your Profit Guarantee: Low Risk, High Transparency, Zero Inventory Pressure, Stable Commission Sharing

We have established partnerships with platforms such as Shopee and Amazon, which directly provide abundant product sourcing. You don't need to worry about inventory or logistics. After each successful order, the company will charge the merchant a commission and share all profits with you. Earnings are predictable and withdrawals are convenient. Member data shows that each bot can generate $30 to $100 in profit per day. Commission income can be withdrawn to your account at any time, and the settlement process is transparent and open.

Low Initial Investment Risk. Bot development and testing incur significant costs. While rental fees are required, in the early stages of the project, the company prioritizes market expansion and brand awareness over short-term profits.

If you are interested, please join my Telegram group for more information and leave a message: https://t.me/+lVKtdaI5vcQ1ZDA1
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Do you see yourself as a programmer, researcher, or engineer?
Anonymous Poll
44%
Programmer
23%
Researcher
33%
Engineer
๐Ÿ’› Top 10 Best Websites to Learn Machine Learning โญ๏ธ
by [@codeprogrammer]

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๐Ÿง  Googleโ€™s ML Course
๐Ÿ”— https://developers.google.com/machine-learning/crash-course

๐Ÿ“ˆ Kaggle Courses
๐Ÿ”— https://kaggle.com/learn

๐Ÿง‘โ€๐ŸŽ“ Coursera โ€“ Andrew Ngโ€™s ML Course
๐Ÿ”— https://coursera.org/learn/machine-learning

โšก๏ธ Fast.ai
๐Ÿ”— https://fast.ai

๐Ÿ”ง Scikit-Learn Documentation
๐Ÿ”— https://scikit-learn.org

๐Ÿ“น TensorFlow Tutorials
๐Ÿ”— https://tensorflow.org/tutorials

๐Ÿ”ฅ PyTorch Tutorials
๐Ÿ”— https://docs.pytorch.org/tutorials/

๐Ÿ›๏ธ MIT OpenCourseWare โ€“ Machine Learning
๐Ÿ”— https://ocw.mit.edu/courses/6-867-machine-learning-fall-2006/

โœ๏ธ Towards Data Science (Blog)
๐Ÿ”— https://towardsdatascience.com

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๐Ÿ’ก Which one are you starting with? Drop a comment below! ๐Ÿ‘‡
#MachineLearning #LearnML #DataScience #AI

https://t.me/CodeProgrammer ๐ŸŒŸ
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Basic Machine Learning Algorithms

1. Linear Regression (linear regression)
Predicts a number based on a linear relationship (example: apartment price).

2. Logistic Regression (logistic regression)
Classification, usually 0/1 (spam/not spam), outputs a probability.

3. Decision Tree (decision tree)
"If-then" rules, easy to explain but prone to overfitting.

4. SVM (support vector machine)
Seeks the boundary between classes with the maximum margin; works well on medium-sized data.

5. KNN (k-nearest neighbors)
Looks at the nearest points and votes; simple but slows down on large datasets.

6. Dimensionality Reduction (dimensionality reduction, often PCA/UMAP/t-SNE)
Compresses features to simplify data/visualization/remove noise.

7. Random Forest (random forest)
Many trees + averaging/voting; often a strong out-of-the-box solution.

8. K-means
Unsupervised clustering: divides points into k groups.

9. Naive Bayes (naive Bayes)
A fast probabilistic classifier, often good for text.

๐Ÿ‘‰ @DataAnalyticsX
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https://t.me/+HDFF3Mo_t68zNWQy
Channel photo updated
๐—ฆ๐—ค๐—Ÿ ๐—™๐—ฟ๐—ผ๐—บ ๐—•๐—ฎ๐˜€๐—ถ๐—ฐ๐˜€ ๐˜๐—ผ ๐—”๐—ฑ๐˜ƒ๐—ฎ๐—ป๐—ฐ๐—ฒ๐—ฑ: This PDF-file contains SQL from beginner to advanced level.

You will need this 101-page PDF file to prepare and review SQL before any data-related interview.

https://drive.google.com/file/d/1N2uPi4hkdCLYPgBa5UfjFT4koqMbGUHz/view

๐Ÿ‘‰ @DataAnalyticsX
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This channels is for Programmers, Coders, Software Engineers.

0๏ธโƒฃ Python
1๏ธโƒฃ Data Science
2๏ธโƒฃ Machine Learning
3๏ธโƒฃ Data Visualization
4๏ธโƒฃ Artificial Intelligence
5๏ธโƒฃ Data Analysis
6๏ธโƒฃ Statistics
7๏ธโƒฃ Deep Learning
8๏ธโƒฃ programming Languages

โœ… https://t.me/addlist/8_rRW2scgfRhOTc0

โœ… https://t.me/Codeprogrammer
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Here: GitHub repository to learn AI Engineering.

It contains some of the best free courses, articles, tutorials, and videos on the following topics:

Mathematical foundation
Basics of AI and #ML
Deep Learning and specializations
Generative #AI
Large language models (#LLM)
Guides on #promptengineering
#RAG, #agents, and #MCP

See here: https://github.com/ashishps1/learn-ai-engineering

๐Ÿ‘‰ @CODEPROGRAMMER
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Numpy_Cheat_Sheet.pdf
4.8 MB
NumPy Cheat Sheet: Data Analysis in Python

This #Python cheat sheet is a quick reference for #NumPy beginners.

Learn more:
https://www.datacamp.com/cheat-sheet/numpy-cheat-sheet-data-analysis-in-python

https://t.me/DataAnalyticsX
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Forwarded from Learn Python Hub
This channels is for Programmers, Coders, Software Engineers.

0๏ธโƒฃ Python
1๏ธโƒฃ Data Science
2๏ธโƒฃ Machine Learning
3๏ธโƒฃ Data Visualization
4๏ธโƒฃ Artificial Intelligence
5๏ธโƒฃ Data Analysis
6๏ธโƒฃ Statistics
7๏ธโƒฃ Deep Learning
8๏ธโƒฃ programming Languages

โœ… https://t.me/addlist/8_rRW2scgfRhOTc0

โœ… https://t.me/Codeprogrammer
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๐Ÿค– Automating Research with NotebookLM

Notebooklm-py is an unofficial library for working with Google NotebookLM, allowing you to automate research processes, generate content, and integrate AI agents. It's suitable for prototypes and personal projects, using Python or the command line.

๐Ÿš€ Key features:
- Integration with AI agents and Claude Code
- Automating research with source importing
- Generating podcasts, videos, and educational materials
- Support for working via the Python API and CLI
- Use with unofficial Google APIs

๐Ÿ“Œ GitHub: https://github.com/teng-lin/notebooklm-py

https://t.me/DataAnalyticsX
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Forwarded from Machine Learning
๐Ÿš€ Machine Learning Workflow: Step-by-Step Breakdown
Understanding the ML pipeline is essential to build scalable, production-grade models.

๐Ÿ‘‰ Initial Dataset
Start with raw data. Apply cleaning, curation, and drop irrelevant or redundant features.
Example: Drop constant features or remove columns with 90% missing values.

๐Ÿ‘‰ Exploratory Data Analysis (EDA)
Use mean, median, standard deviation, correlation, and missing value checks.
Techniques like PCA and LDA help with dimensionality reduction.
Example: Use PCA to reduce 50 features down to 10 while retaining 95% variance.

๐Ÿ‘‰ Input Variables
Structured table with features like ID, Age, Income, Loan Status, etc.
Ensure numeric encoding and feature engineering are complete before training.

๐Ÿ‘‰ Processed Dataset
Split the data into training (70%) and testing (30%) sets.
Example: Stratified sampling ensures target distribution consistency.

๐Ÿ‘‰ Learning Algorithms
Apply algorithms like SVM, Logistic Regression, KNN, Decision Trees, or Ensemble models like Random Forest and Gradient Boosting.
Example: Use Random Forest to capture non-linear interactions in tabular data.

๐Ÿ‘‰ Hyperparameter Optimization
Tune parameters using Grid Search or Random Search for better performance.
Example: Optimize max_depth and n_estimators in Gradient Boosting.

๐Ÿ‘‰ Feature Selection
Use model-based importance ranking (e.g., from Random Forest) to remove noisy or irrelevant features.
Example: Drop features with zero importance to reduce overfitting.

๐Ÿ‘‰ Model Training and Validation
Use cross-validation to evaluate generalization. Train final model on full training set.
Example: 5-fold cross-validation for reliable performance metrics.

๐Ÿ‘‰ Model Evaluation
Use task-specific metrics:
- Classification โ€“ MCC, Sensitivity, Specificity, Accuracy
- Regression โ€“ RMSE, Rยฒ, MSE
Example: For imbalanced classes, prefer MCC over simple accuracy.

๐Ÿ’ก This workflow ensures models are robust, interpretable, and ready for deployment in real-world applications.

https://t.me/DataScienceM
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Forwarded from Machine Learning
Effective Pandas 2: Opinionated Patterns for Data Manipulation

This book is now available at a discounted price through our Patreon grant:

Original Price: $53

Discounted Price: $12

Limited to 15 copies

Buy: https://www.patreon.com/posts/effective-pandas-150394542
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