This repository contains several folders with data science learning tasks. Each folder represents a different topic and contains files and code for practicing and improving data science skills.
These folders contain Jupyter notebooks that demonstrate how to perform exploratory data analysis to better understand data before modeling. The notebooks include visualization techniques, statistical analysis, and data cleaning methods.
Folders with tasks related to exploratory data analysis (EDA):
EDA - Group project on analyzing Netflix dataset
These folders covers different techniques used to extract, transform and select features from raw data. They include Jupyter notebooks that explain how to engineer features and why it's important for building accurate models.
Folders with tasks related to feature engineering:
PCA_sound_compression - PCA reducing dimensionality
These folder focus on different machine learning algorithms and techniques. Include Jupyter notebooks that cover supervised learning, unsupervised learning, and reinforcement learning. Each notebook includes code examples, explanations of the algorithms, and how to evaluate their performance.
Folders with tasks related to machine learning algorithms and models:
shine-on-you - linear regression and clusterization
models - for simple tests of different kinds of models
gradient-boositng - combined application of EDA, different ML models and feature selections\engineering
Neural_networks - introduction to a magnificent world of neural networks and deep learning
These folders covers different optimization algorithms used in machine learning. Includes Jupyter notebooks that explain how optimization algorithms work and their role in training machine learning models. The notebooks cover different optimization algorithms such as gradient descent and RMSprop. TODO: Stochastic gradient descent (SGD), Adam and Adagrad.
Folders with tasks related to optimization algorithms:
gradient-descent - different types of gradient descent
To run the code in this repository, you'll need to have the following software installed on your machine:
Python 3
Jupyter Notebook
Required Python packages listed in requirements.txt
Clone or download the repository to your local machine.
Install the required packages by running pip install -r requirements.txt.
Navigate to the folder containing the topic you're interested in.
Open the Jupyter Notebook file(s) in that folder to get started.
Contributing
If you'd like to contribute to this repository, please follow these steps:
Create a new branch for your changes.
Make your changes and commit them to your branch.
Push your changes to your fork.
Submit a pull request to the main repository.
This repository is licensed under the MIT license. See the 'LICENSE' file for more details.
If you have any questions or comments about this repository, you most like to know me, so just text me in Telegram.