A comprehensive learning and project notebook for Python, Computer Science, and AI & Data Science.
Documenting my journey from Python fundamentals to advanced AI/ML projects. Continuously updated as I learn and progress.
This repository organizes a structured learning path from beginner to advanced AI & Data Science topics. It includes Python programming, algorithms, data structures, data processing, machine learning, and AI fundamentals, along with mini projects and end-to-end workflows.
- Python fundamentals: syntax, data types, variables, operators
- Functions, loops, conditionals
- Beginner-level snippets and exercises
- Mini projects: small scripts, math challenges, mini games
- Core and advanced algorithms
- Binary Search, Sorting, Searching
- Recursion, Two Pointers, Sliding Window techniques
- Exercises and mini projects
- Arrays, Linked Lists, Stacks, Queues
- Trees, Heaps, Hash Tables
- Pseudocode, examples, Python implementations
- Dynamic Programming
- Graph Algorithms: BFS, DFS, Dijkstra
- Greedy Approaches
- Competitive programming tricks
- Numerical computations, broadcasting, indexing
- Linear algebra operations and performance optimization
- Mini exercises: Linear regression, matrix operations
- Data manipulation and analysis
- Exploratory Data Analysis (EDA) and feature engineering
- Mini projects using real datasets
- Basic ML algorithms: Linear & Logistic Regression, kNN, Decision Trees
- Model evaluation and metrics
- End-to-end small ML pipelines
- Supervised & Unsupervised Learning
- Model pipelines, cross-validation, feature engineering
- Mini AI projects (chatbots, recommendation systems)
- Data cleaning and building pipelines
- Basic SQL for data manipulation
- Small ETL projects
- Beginner: Python scripts, mini games, small algorithm projects
- Intermediate: Titanic EDA, House Prices regression/classification, Iris dataset
- Advanced: MNIST digit classifier, Cats vs Dogs CNN, Sentiment Analysis on Tweets
- All projects are in Jupyter Notebook format, runnable, and documented with explanations and visualizations
- Short-term: Learn Python, algorithms, and data structures + mini projects
- Medium-term: Master NumPy, Pandas, and core AI & Data Science concepts + build a GitHub portfolio
- Long-term: Gain advanced expertise in AI & Data Science + be fully prepared for future
- Clone the repository:
git clone https://github.com/BusraCevik/cs-to-ai-engineering.git