Machine Learning Concepts: From Beginner to Expert

Welcome to this repository! This collection of notebooks and resources is designed to guide you through the exciting world of machine learning, starting from the fundamentals and progressing to advanced concepts.

Overview

This repository provides a structured learning path for anyone looking to understand and apply machine learning techniques. It covers a wide range of topics, enabling you to build a solid foundation and explore more advanced areas.

Structure

The repository is organized into the following folders:

Beginner:
- Linear Regression
- Logistic Regression
- Basic Classification
- And other fundamental concepts.
Intermediate:
Advanced:
- Neural Networks
- Deep Learning
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- And other advanced topics.
Preprocessing:
- Data Cleaning
- Feature Engineering
Evaluation:
- Model Evaluation Metrics
- Cross-Validation

(Note: Please replace path/to/... with the actual relative paths to your folders.)

Getting Started

Clone the repository:

git clone [https://github.com/codehax41/Machine-Learning-Concepts-From-Beginner-to-Expert.git](https://www.google.com/search?q=https://github.com/codehax41/Machine-Learning-Concepts-From-Beginner-to-Expert.git)

Navigate to the desired folder:

cd Machine-Learning-Concepts-From-Beginner-to-Expert/Beginner/Linear_Regression

Open the Jupyter Notebooks or Python scripts:
- Ensure you have Python and necessary libraries (e.g., NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch) installed.
- Use Jupyter Notebook or your preferred IDE to run the code.
Follow the instructions within the notebooks/scripts.

Prerequisites

Basic understanding of Python programming.
Familiarity with fundamental mathematical concepts (e.g., linear algebra, calculus, statistics).
Python 3.6+
Jupyter Notebooks (Recommended)
Required python libraries:
- numpy
- pandas
- scikit-learn
- matplotlib
- seaborn
- tensorflow or pytorch (for deep learning portions)

Contributing

Contributions are welcome! If you find any errors or have suggestions for improvements, please feel free to:

Submit a pull request.
Open an issue.

Author

codehax41

Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
Car Price Prediction Assignment		Car Price Prediction Assignment
Telecom Churn		Telecom Churn
1. DS Visualization.ipynb		1. DS Visualization.ipynb
10. VIF.ipynb		10. VIF.ipynb
11. Feature Importance - Wrapper Methods.ipynb		11. Feature Importance - Wrapper Methods.ipynb
12. Gradient Decent - Implementation all Types.ipynb		12. Gradient Decent - Implementation all Types.ipynb
13. Statistical Tests.ipynb		13. Statistical Tests.ipynb
2. DS Visualization 2.ipynb		2. DS Visualization 2.ipynb
3. DS Visualization 3.ipynb		3. DS Visualization 3.ipynb
4. Cost Function & Gradient Decent.ipynb		4. Cost Function & Gradient Decent.ipynb
5. Linear Regression.ipynb		5. Linear Regression.ipynb
6. Linear Regression - Advertisement Dataset.ipynb		6. Linear Regression - Advertisement Dataset.ipynb
7. Multiple Linear Regression - Advertisement Project.ipynb		7. Multiple Linear Regression - Advertisement Project.ipynb
8. Correlation Coefficient .ipynb		8. Correlation Coefficient .ipynb
9. Linear Regression - Assumptions.ipynb		9. Linear Regression - Assumptions.ipynb
AUC ROC Score.ipynb		AUC ROC Score.ipynb
Agglomerative & Divisive Clustering.ipynb		Agglomerative & Divisive Clustering.ipynb
Book Recommendation System using KNN.ipynb		Book Recommendation System using KNN.ipynb
Car Price Assignment - 2.ipynb		Car Price Assignment - 2.ipynb
CatBoost.ipynb		CatBoost.ipynb
Classification Metrics.ipynb		Classification Metrics.ipynb
Collaborative Filtering System.ipynb		Collaborative Filtering System.ipynb
Content Based Recommendation System.ipynb		Content Based Recommendation System.ipynb
Correlation & Covariance.ipynb		Correlation & Covariance.ipynb
Curse of Dimensionality, Code Explaination .ipynb		Curse of Dimensionality, Code Explaination .ipynb
DBSCAN Clustering.ipynb		DBSCAN Clustering.ipynb
Decision Tree.ipynb		Decision Tree.ipynb
Encoding.ipynb		Encoding.ipynb
Find Best K in KMeans Algorithm.ipynb		Find Best K in KMeans Algorithm.ipynb
Find best K in KNN.ipynb		Find best K in KNN.ipynb
GOSS & EFB.ipynb		GOSS & EFB.ipynb
Gaussian Mixture Model.ipynb		Gaussian Mixture Model.ipynb
Handling Imbalanced Dataset.ipynb		Handling Imbalanced Dataset.ipynb
Hybrid Recommendation System.ipynb		Hybrid Recommendation System.ipynb
Hyperparam Tuning.ipynb		Hyperparam Tuning.ipynb
Interpretability & Explainability.ipynb		Interpretability & Explainability.ipynb
K Fold Cross Validation.ipynb		K Fold Cross Validation.ipynb
KNN.ipynb		KNN.ipynb
Kmean Clustering Algorithm.ipynb		Kmean Clustering Algorithm.ipynb
Kmeans ++ Algorithm.ipynb		Kmeans ++ Algorithm.ipynb
LDA & SVD.ipynb		LDA & SVD.ipynb
LGBM.ipynb		LGBM.ipynb
Lasso, Rdge, Elastic Net Implementation.ipynb		Lasso, Rdge, Elastic Net Implementation.ipynb
Logistic Regression - Multi Class Problem.ipynb		Logistic Regression - Multi Class Problem.ipynb
Logistic Regression.ipynb		Logistic Regression.ipynb
Movie Recommendation using KNN.ipynb		Movie Recommendation using KNN.ipynb
PCA.ipynb		PCA.ipynb
Polynomial Regression.ipynb		Polynomial Regression.ipynb
README.md		README.md
Random Forest.ipynb		Random Forest.ipynb
Ratings_Distributions.png		Ratings_Distributions.png
Recommendation System using Weighted Avg Method and tfIdf.ipynb		Recommendation System using Weighted Avg Method and tfIdf.ipynb
Sampling Techniques.ipynb		Sampling Techniques.ipynb
Support Vector Machines.ipynb		Support Vector Machines.ipynb
TSNE.ipynb		TSNE.ipynb
Telecom Churn Project.ipynb		Telecom Churn Project.ipynb
Underfitting & Overfitting.ipynb		Underfitting & Overfitting.ipynb
Why Fit_Transform trainset and why just transform Test set.ipynb		Why Fit_Transform trainset and why just transform Test set.ipynb
XGB.ipynb		XGB.ipynb
advertising.csv		advertising.csv
cust_dimen.csv		cust_dimen.csv
market_fact.csv		market_fact.csv
melbourne.csv		melbourne.csv
orders_dimen.csv		orders_dimen.csv
prod_dimen.csv		prod_dimen.csv
shipping_dimen.csv		shipping_dimen.csv

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Machine Learning Concepts: From Beginner to Expert

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