Welcome to my GitHub repository where I document my journey to becoming a data scientist! This repository is a collection of resources, notes, and projects that I follow while learning data science from the CampusX Data Science Mentor Program.
Feel free to explore, learn, and contribute!
I am following the Data Science Mentor Program by CampusX, which covers everything from the basics of data science to advanced topics like machine learning. You can find the complete playlist here:
The repository is structured in a way that helps me track my progress, projects, and resources. Below is a breakdown of what you'll find in each folder:
- Notebooks: Jupyter notebooks where I practice concepts and work on various data science problems.
- Projects: End-to-end projects that I complete as part of my learning journey.
- Resources: Helpful links, articles, and books related to data science, machine learning, and AI.
- Notes: My personal notes and summaries from the videos and other resources.
- Scripts: Python scripts for smaller exercises, algorithms, or data handling tasks.
- Python for Data Science
- Data Wrangling and Preprocessing
- Exploratory Data Analysis (EDA)
- Data Visualization (Matplotlib, Seaborn)
- Statistics for Data Science
- Machine Learning Algorithms
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forests
- K-Means Clustering
- Support Vector Machines (SVM)
- Deep Learning (Introduction)
- Model Evaluation and Tuning
- Deployment of Models
- Python
- Jupyter Notebooks
- Pandas
- NumPy
- Scikit-learn
- Matplotlib
- Seaborn
- TensorFlow/Keras (Introduction to Deep Learning)
Here are some of the key projects I've worked on during the program:
- Project Name 1: Short description of the project.
- Project Name 2: Short description of the project.
- Project Name 3: Short description of the project.
Stay tuned for more projects as I keep updating this repo!
I regularly take notes to better understand the concepts taught in the course. You can find my notes in the Notes folder. Some of the key topics covered include:
- Data preprocessing techniques
- Understanding bias and variance
- Cross-validation methods
- Feature engineering
- Clone this repository:
git clone https://github.com/mrravipandee/Machine-Learning-Enthusiast.git
- Explore the folders to find resources, notebooks, and projects.
- Feel free to fork and contribute by adding your own projects or resources!
If you have any questions, suggestions, or feedback, feel free to reach out!
- GitHub: mrravipandee
- LinkedIn: Ravi Pandey