My Machine Learning Learning Journey

Welcome to my Machine Learning Learning Journey repository! This repository serves as a record of my progress as I learn various machine learning concepts and techniques. I will be completing a comprehensive machine learning course and documenting my learnings, code implementations, and project progress in this repository.

Course Structure

The course I am following covers a wide range of topics in machine learning. Here's an overview of the different parts and their contents:

1. Part 1 - Data Preprocessing: Techniques for handling missing data, encoding categorical variables, and scaling numerical features.

2. Part 2 - Regression: Simple linear regression, multiple linear regression, polynomial regression, support vector regression (SVR), decision tree regression, and random forest regression.

3. Part 3 - Classification: Logistic regression, k-nearest neighbors (K-NN), support vector machines (SVM), kernel SVM, naive Bayes, decision tree classification, and random forest classification.

4. Part 4 - Clustering: K-means clustering and hierarchical clustering.

5. Part 5 - Association Rule Learning: Apriori and Eclat algorithms for association rule learning.

6. Part 6 - Reinforcement Learning: Upper Confidence Bound (UCB) and Thompson Sampling algorithms for reinforcement learning.

7. Part 7 - Natural Language Processing: Bag-of-words model and algorithms for natural language processing (NLP).

8. Part 8 - Deep Learning: Artificial neural networks (ANN) and convolutional neural networks (CNN) for deep learning.

9. Part 9 - Dimensionality Reduction: Principal component analysis (PCA), linear discriminant analysis (LDA), and kernel PCA for dimensionality reduction.

10. Part 10 - Model Selection & Boosting: k-fold cross-validation, parameter tuning, grid search, and XGBoost for model selection and boosting.

Repository Structure

In this repository, you will find the following structure:

• code: This folder contains the code files corresponding to each part of the course. Each part has its own subfolder, where you can find the code implementations for that specific topic.

• data: If applicable, this folder stores the datasets used in the code files. It includes any necessary documentation or README files to explain the data sources or provide additional information.

• notebooks: This folder holds Jupyter Notebook files (.ipynb) that document my code, experiments, and analysis. Each notebook corresponds to a specific part or topic of the course.

• resources: This folder contains additional resources or reference materials related to the course. It may include PDFs, slides, links to external articles, or tutorials that supplement my learning.

Progress Tracking

Throughout my learning journey, I will be committing and pushing my code regularly to this repository. By doing so, I aim to track my progress, showcase my understanding of different machine learning concepts, and demonstrate my ability to implement them in practical projects.

Feel free to explore the code, datasets, and resources in this repository. If you have any questions, suggestions, or feedback, please feel free to reach out. I'm always open to learning, improving, and engaging in discussions with fellow learners.

Happy exploring and learning!

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