https://github.com/paritoshtripathi935/machine_learning_projects
Various machine Learning projects
https://github.com/paritoshtripathi935/machine_learning_projects
data-science deep-learning machine machine-learning machine-learning-algorithms python scikit-learn statistics
Last synced: 4 months ago
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Various machine Learning projects
- Host: GitHub
- URL: https://github.com/paritoshtripathi935/machine_learning_projects
- Owner: paritoshtripathi935
- Created: 2021-08-04T15:01:18.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2022-04-18T15:31:21.000Z (almost 4 years ago)
- Last Synced: 2025-03-14T10:33:50.436Z (11 months ago)
- Topics: data-science, deep-learning, machine, machine-learning, machine-learning-algorithms, python, scikit-learn, statistics
- Language: Jupyter Notebook
- Homepage:
- Size: 8.26 MB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
You are welcome to contribute to this repository, you can contribute your repository of machine learning models, learning, and projects in the AI and ML fields. 😎
DataScience and Machine-Learning Roadmap 😎
```Show some ❤️ by starring this repo! ⭐ ```
Define Goal : PRODUCTS or ALGORITHMS
## Maths
- Linear Algebra (Matrix, Vector)
- Statistics
- Probability
## Learn Python & its Libraries
- Numpy
- Pandas
## Learn ML Algorithms
- Supervised vs Unsupervised vs Reinforcement
- Linear RegressionDefine Goal : PRODUCTS or ALGORITHMS
## Maths
- Linear Algebra (Matrix, Vector)
- Statistics
- Probability
## Learn Python & its Libraries
- Numpy
- Pandas
## Learn ML Algorithms
- Supervised vs Unsupervised vs Reinforcement
- Linear Regression, Logistic Regression, Clustering
- KNN (K Nearest Neighbours)
- SVM (Support Vector Machine)
- Decision Trees
- Random Forests
- Overfitting, Underfitting
- Regularization, Gradient Descent, Slope
- Confusion Matrix
## Data Preprocessing (for higher accuracy)
- Handling Null Values
- Standardization
- Handling Categorical Values
- One-Hot Encoding
- Feature Scaling
## Learn ML libraries
- Scikit learn
- Matplotlib
- Tensorflow for DL
1) Practice and participate in various competetions of kaggle
2) Explore projects on Github
## Resources :
[Mathematics-1](http://www.maths.qmul.ac.uk/~pjc/notes/linalg.pdf)
[Mathematics-2](https://www.mathsbox.org.uk/twi/astats.pdf)
[Mathematics-2](https://www.youtube.com/playlist?list=PLLy_2iUCG87D1CXFxE-SxCFZUiJzQ3IvE)
[Machine Learning Course by Google](https://developers.google.com/machine-learning/crash-course )
[Python Basics](https://www.datacamp.com/courses/intro-to-python-for-data-science )
[Stanford Course by Andrew ng](https://www.coursera.org/learn/machine-learning)
[Made With ML](https://madewithml.com/)
[Data Preprocessing](https://www.javatpoint.com/data-preprocessing-machine-learning )
[Scikit Learn](https://scikit-learn.org/stable/)
[Tensorflow](https://www.tensorflow.org/)
[Kaggle](https://www.kaggle.com/)
