Welcome to my repository, a collection of my exploratory projects in the diverse fields of AI, Data Science and Machine Learning.
For a detailed understanding of these projects, you can refer to the comprehensive documentation available here.
In addition to these projects, I regularly share my insights and learnings on the Medium platform. You can access my articles here.
Tip
The projects listed below are organized alphabetically for your convenience.
- Credit Card Fraud Detection: Unveil fraudulent transactions using a neural network-based approach.
- Automated GitHub Commits: Simplify your workflow with an automated solution for committing and pushing changes to GitHub.
- Ants vs Bees Image Classification: Harness the power of deep learning models to classify images.
- TOML vs. YAML: Choosing the right configuration format for your projects.
- Top 5 Formats: Top 5 structured data formats for data science.
- TOON: Token-efficient, human-readable serialization format optimized for LLM contexts.
- Sorting Algorithms: A comprehensive guide to understanding and implementing popular sorting algorithms in Python.
- Understanding Hashing: Dive into the world of hashing, its applications, and Python implementation.
- Bloom Filter: Learn about the Bloom filter data structure and its applications.
- ggplot2: Create visually appealing plots with the R's ggplot2 library.
- lets-plot: Create stunning plots with lets-plot, a Python port of the R's ggplot2 library.
- Pitfalls: Avoid common pitfalls in data visualization.
- QR Code: Generate QR codes with ease.
- Data Balancing: Learn techniques to balance imbalanced datasets.
- Handling Missing Data: Discover various methods for handling missing data in datasets.
- Pivot Tables with pandas: Create pivot tables using the pandas library.
- Polars: Leverage the Polars library for efficient data manipulation and analysis.
- ETL Pipeline with Airflow and Docker: A project showcasing the automation of data extraction, transformation, and loading into a database.
- KerasTuner: Optimize your models with hyperparameter tuning using the KerasTuner library.
- Optuna: Enhance your models with hyperparameter tuning using the Optuna library.
- Tokenization: Explore the tokenization of text data.
- Best Threshold for Logistic Regression: Explore different methods to find the optimal threshold for logistic regression.
- Anonymization: Learn about data anonymization and its applications.
- Encryption: A guide to understanding and implementing Python encryption.
- Argument Parsing: Master argument parsing using the
argparsemodule. - Built-ins — Advanced 1, 2, 3: Master advanced built-in functions to write cleaner, more efficient code.
- Calendars: Explore the
calendarmodule in Python. - Generators: A hands-on guide to understanding and using generators.
- Lambda: Get introduced to lambda functions.
- Pattern Matching: Learn pattern matching with the
match-casestatement. - Serialization: Understand serialization and deserialization in Python.
- Design Patterns: Learn about design patterns and their applications.
- A/B Testing: Test the effectiveness of a new feature in a web application using A/B testing.
- Hypothesis Testing: p-values Around 0.05: Understand when to reject the null hypothesis if the p-value is around 0.05.
- Introduction: Learn to generate synthetic data using Python and understand the considerations for using synthetic data.
- Forecasting with sktime: Forecast time-series data using the sktime library.
- Prevent Overfitting: Learn techniques to prevent overfitting in time series forecasting.
- jobinventory: Scrape job listings from jobinventory.com using Python.
- Introduction: Understand the importance of explainable AI and its applications.