𝟯 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿-𝗙𝗿𝗶𝗲𝗻𝗱𝗹𝘆 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗬𝗼𝘂𝗿 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗶𝗻 𝟮𝟬𝟮𝟱😍
👩💻 Want to Break into Data Science but Don’t Know Where to Start?🚀
The best way to begin your data science journey is with hands-on projects using real-world datasets.👨💻📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/44LoViW
Enjoy Learning ✅️
👩💻 Want to Break into Data Science but Don’t Know Where to Start?🚀
The best way to begin your data science journey is with hands-on projects using real-world datasets.👨💻📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/44LoViW
Enjoy Learning ✅️
👍1
Python Science Projects.pdf_20231120_013618_0000.pdf
2.1 MB
Python Data Science Projects For Boosting Your Portfolio
Modern Time Series Forecasting with Python.pdf
25.5 MB
Modern Time Series Forecasting with Python
Manu Joseph, 2022
Manu Joseph, 2022
Rlecturenotes.pdf
4.3 MB
An Introduction to R
Petra Kuhnert, 2007
Petra Kuhnert, 2007
❤4
Forwarded from Artificial Intelligence
𝗚𝗼𝗼𝗴𝗹𝗲 𝗧𝗼𝗽 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍
If you’re job hunting, switching careers, or just want to upgrade your skill set — Google Skillshop is your go-to platform in 2025!
Google offers completely free certifications that are globally recognized and valued by employers in tech, digital marketing, business, and analytics📊
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4dwlDT2
Enroll For FREE & Get Certified 🎓️
If you’re job hunting, switching careers, or just want to upgrade your skill set — Google Skillshop is your go-to platform in 2025!
Google offers completely free certifications that are globally recognized and valued by employers in tech, digital marketing, business, and analytics📊
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4dwlDT2
Enroll For FREE & Get Certified 🎓️
❤1👍1
Machine learning .pdf
5.3 MB
Core machine learning concepts explained through memes and simple charts created by Mihail Eric.
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🔍 Machine Learning Cheat Sheet 🔍
1. Key Concepts:
- Supervised Learning: Learn from labeled data (e.g., classification, regression).
- Unsupervised Learning: Discover patterns in unlabeled data (e.g., clustering, dimensionality reduction).
- Reinforcement Learning: Learn by interacting with an environment to maximize reward.
2. Common Algorithms:
- Linear Regression: Predict continuous values.
- Logistic Regression: Binary classification.
- Decision Trees: Simple, interpretable model for classification and regression.
- Random Forests: Ensemble method for improved accuracy.
- Support Vector Machines: Effective for high-dimensional spaces.
- K-Nearest Neighbors: Instance-based learning for classification/regression.
- K-Means: Clustering algorithm.
- Principal Component Analysis(PCA)
3. Performance Metrics:
- Classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC.
- Regression: Mean Absolute Error (MAE), Mean Squared Error (MSE), R^2 Score.
4. Data Preprocessing:
- Normalization: Scale features to a standard range.
- Standardization: Transform features to have zero mean and unit variance.
- Imputation: Handle missing data.
- Encoding: Convert categorical data into numerical format.
5. Model Evaluation:
- Cross-Validation: Ensure model generalization.
- Train-Test Split: Divide data to evaluate model performance.
6. Libraries:
- Python: Scikit-Learn, TensorFlow, Keras, PyTorch, Pandas, Numpy, Matplotlib.
- R: caret, randomForest, e1071, ggplot2.
7. Tips for Success:
- Feature Engineering: Enhance data quality and relevance.
- Hyperparameter Tuning: Optimize model parameters (Grid Search, Random Search).
- Model Interpretability: Use tools like SHAP and LIME.
- Continuous Learning: Stay updated with the latest research and trends.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best 👍👍
1. Key Concepts:
- Supervised Learning: Learn from labeled data (e.g., classification, regression).
- Unsupervised Learning: Discover patterns in unlabeled data (e.g., clustering, dimensionality reduction).
- Reinforcement Learning: Learn by interacting with an environment to maximize reward.
2. Common Algorithms:
- Linear Regression: Predict continuous values.
- Logistic Regression: Binary classification.
- Decision Trees: Simple, interpretable model for classification and regression.
- Random Forests: Ensemble method for improved accuracy.
- Support Vector Machines: Effective for high-dimensional spaces.
- K-Nearest Neighbors: Instance-based learning for classification/regression.
- K-Means: Clustering algorithm.
- Principal Component Analysis(PCA)
3. Performance Metrics:
- Classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC.
- Regression: Mean Absolute Error (MAE), Mean Squared Error (MSE), R^2 Score.
4. Data Preprocessing:
- Normalization: Scale features to a standard range.
- Standardization: Transform features to have zero mean and unit variance.
- Imputation: Handle missing data.
- Encoding: Convert categorical data into numerical format.
5. Model Evaluation:
- Cross-Validation: Ensure model generalization.
- Train-Test Split: Divide data to evaluate model performance.
6. Libraries:
- Python: Scikit-Learn, TensorFlow, Keras, PyTorch, Pandas, Numpy, Matplotlib.
- R: caret, randomForest, e1071, ggplot2.
7. Tips for Success:
- Feature Engineering: Enhance data quality and relevance.
- Hyperparameter Tuning: Optimize model parameters (Grid Search, Random Search).
- Model Interpretability: Use tools like SHAP and LIME.
- Continuous Learning: Stay updated with the latest research and trends.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best 👍👍
❤1👍1
Forwarded from Artificial Intelligence
𝟳 𝗕𝗲𝘀𝘁 𝗪𝗲𝗯𝘀𝗶𝘁𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 𝗶𝗻 𝟮𝟬𝟮𝟱 (𝗡𝗼 𝗖𝗼𝘀𝘁, 𝗡𝗼 𝗖𝗮𝘁𝗰𝗵!)😍
Want to become a Data Scientist in 2025 without spending a single rupee? You’re in the right place📌
From Python and machine learning to hands-on projects and challenges🎯
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4dAuymr
Enjoy Learning ✅️
Want to become a Data Scientist in 2025 without spending a single rupee? You’re in the right place📌
From Python and machine learning to hands-on projects and challenges🎯
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4dAuymr
Enjoy Learning ✅️
👍1