๐ฏ ๐๐ฒ๐ด๐ถ๐ป๐ป๐ฒ๐ฟ-๐๐ฟ๐ถ๐ฒ๐ป๐ฑ๐น๐ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐๐ ๐๐ผ ๐๐๐ถ๐น๐ฑ ๐ฌ๐ผ๐๐ฟ ๐ฃ๐ผ๐ฟ๐๐ณ๐ผ๐น๐ถ๐ผ ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ๐
๐ฉโ๐ป Want to Break into Data Science but Donโt Know Where to Start?๐
The best way to begin your data science journey is with hands-on projects using real-world datasets.๐จโ๐ป๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/44LoViW
Enjoy Learning โ ๏ธ
๐ฉโ๐ป Want to Break into Data Science but Donโt Know Where to Start?๐
The best way to begin your data science journey is with hands-on projects using real-world datasets.๐จโ๐ป๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/44LoViW
Enjoy Learning โ ๏ธ
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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
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Forwarded from Artificial Intelligence
๐๐ผ๐ผ๐ด๐น๐ฒ ๐ง๐ผ๐ฝ ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐๐
If youโre job hunting, switching careers, or just want to upgrade your skill set โ 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 analytics๐
๐๐ข๐ง๐ค๐:-
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Enroll For FREE & Get Certified ๐๏ธ
If youโre job hunting, switching careers, or just want to upgrade your skill set โ 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 analytics๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4dwlDT2
Enroll For FREE & Get Certified ๐๏ธ
โค1๐1
Machine learning .pdf
5.3 MB
Core machine learning concepts explained through memes and simple charts created by Mihail Eric.
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๐ Machine Learning Cheat Sheet ๐
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 ๐๐
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 ๐๐
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