Welcome to my Machine Learning Journey repository! This is a comprehensive collection of everything I have learned and experienced in the field of machine learning over the past five years. As a self-learner, I have taken various courses, completed projects, and explored numerous resources to deepen my understanding and skills in machine learning.
This repository is a personal knowledge dump, a roadmap for aspiring machine learning enthusiasts, and a showcase of my learning process.
This repository contains:
- Roadmap: A step-by-step guide to learning machine learning, tailored from my experiences.
- Course Notes: Summarized notes and key takeaways from courses I have completed.
- Code Implementations: Practical implementations of algorithms and projects.
- Projects: Detailed descriptions and code for projects I have worked on.
- Resources: Links to books, articles, tutorials, and other useful resources.
- Reflections: Insights and experiences from my journey as a self-learner.
This section outlines a structured approach to mastering machine learning, including foundational concepts, essential programming skills, and advanced topics like deep learning, AI and data science.
Here, you will find notes from the machine learning course I completed, along with additional insights from other resources. Topics include:
- Data Preprocessing
- Regression Techniques
- Classification Algorithms
- Clustering Methods
- Dimensionality Reduction
- Model Evaluation and Improvement
- Deep Learning
This section includes code examples and Jupyter notebooks for:
- Machine Learning Algorithms
- Data Preprocessing Techniques
- Visualization Tools
- Custom Implementations
Detailed explanations and code for my machine learning projects, including:
- Predictive Modeling
- Natural Language Processing
- Image Classification
- Recommendation Systems
- Exploratory Data Analysis (EDA)
A curated list of:
- Books
- Articles
- Online Courses
- GitHub Repositories
- Kaggle Datasets
Personal reflections on my learning journey, challenges faced, and how I overcame them.
- Clone this repository:
git clone https://github.com/ShishirRmc/machine-learning-journey.git
- Navigate to the desired section for learning or reference.
- Use the Jupyter notebooks or Python scripts for hands-on practice.
While this is a personal repository, I welcome suggestions and ideas! Feel free to open an issue or reach out if you have recommendations.
This repository is licensed under the MIT License. Feel free to use and share the resources, but give proper credit where it is due.
Happy Learning! 🚀