{"id":32880739,"url":"https://github.com/ansinitro/programming-for-ai","last_synced_at":"2026-06-22T12:31:26.236Z","repository":{"id":321229486,"uuid":"1081610955","full_name":"ansinitro/programming-for-ai","owner":"ansinitro","description":"AITU Master Degree course Programming for AI | Instructuor: Arailym Tleubayeva","archived":false,"fork":false,"pushed_at":"2025-11-16T02:56:22.000Z","size":8065,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-11-16T04:29:48.613Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Programming for Artificial Intelligence\n\n## 🎯 Course Overview\n**Programming for AI** provides a foundation in programming principles, tools, and frameworks essential for developing Artificial Intelligence (AI) applications.  \nStudents will learn to design, implement, and test AI-related programs using **Python**, **NumPy**, **Pandas**, **Scikit-learn**, **PyTorch**, and **TensorFlow**.  \n\nThis course bridges theory and practice — helping students gain both algorithmic understanding and hands-on experience in working with real-world datasets, building machine learning models, and managing AI development workflows.\n\n---\n\n## 🧠 Learning Objectives\nBy completing this course, you will learn to:\n- Use key tools for AI development — Python, Git/GitHub, Jupyter/Colab, and cloud environments.  \n- Create and manipulate data structures, apply OOP principles, and write modular, reusable code.  \n- Read and write datasets in formats like CSV, JSON, and TXT, including working with APIs and large data files.  \n- Apply **NumPy** and **tensor operations** for mathematical tasks such as linear algebra and random sampling.  \n- Load, clean, and preprocess datasets for machine learning experiments.  \n- Apply **text preprocessing** and **NLP techniques** (tokenization, TF-IDF, embeddings).  \n- Visualize data and models using **Matplotlib**, **Seaborn**, and **TensorBoard**.  \n- Implement machine learning models (classification, regression, clustering) using **Scikit-learn**.  \n- Design and train **neural networks** (MLP, CNN) using **PyTorch** or **TensorFlow**.\n\n---\n\n## 📚 Topics Covered\n### Week 1–5: Python Foundations and Data Handling\n- Python syntax, data types, OOP concepts  \n- File handling (CSV, JSON, large data)  \n- NumPy and tensor operations  \n- Dataset management and preprocessing  \n\n### Week 6–7: NLP and Visualization\n- Basics of natural language processing (tokenization, sentiment analysis)  \n- Visualization of data and model performance  \n\n### Week 8–9: Machine Learning \u0026 Neural Networks\n- Supervised and unsupervised ML models  \n- Neural network fundamentals, activation functions, and training  \n\n### Week 10: Mini Project\n- Design and implementation of an AI mini project using learned concepts  \n\n---\n\n## ⚙️ Tools \u0026 Technologies\n- **Languages:** Python  \n- **Libraries:** NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow  \n- **Visualization:** Matplotlib, Seaborn, TensorBoard  \n- **Development:** Jupyter Notebook, Google Colab, Git/GitHub  \n- **Cloud Platforms:** Kaggle, Google Cloud, AWS (optional)\n\n---\n\n## 🧩 Practical Components\nStudents will complete:\n- **4 programming assignments** (covering Python, NumPy, ML, and Neural Networks)\n- **1 midterm and 1 endterm exam**\n- **Final mini project** — an applied AI system built on a real dataset\n\n---\n\n## 📈 Evaluation Scheme\n| Component | Weight | Description |\n|------------|---------|-------------|\n| Attestation I | 30% | Assignments + Midterm |\n| Attestation II | 30% | Assignments + Endterm |\n| Final Project | 40% | Project + Presentation |\n\nGrading follows a percentage-based scale:\n- **A (95–100):** Excellent  \n- **B (80–89):** Good  \n- **C (65–79):** Satisfactory  \n- **D (50–64):** Minimal pass  \n- **F (\u003c50):** Fail  \n\n---\n\n## 🧠 Skills You’ll Gain\n- Python development for AI applications  \n- Data preprocessing and feature engineering  \n- Tensor manipulation and matrix operations  \n- Model design and evaluation  \n- Data visualization and performance tracking  \n- Neural network training and debugging  \n- Version control and collaboration with GitHub  \n\n---\n\n## 📘 Recommended Learning Resources\n- *Head First Python* — Paul Barry (O’Reilly, 2023)  \n- *Python Crash Course* — Eric Matthes (No Starch Press, 2019)  \n- *Python for Everybody* — Dr. Charles Severance  \n- *Statistical Learning with Sparsity* — Trevor Hastie et al.  \n- *Data Science for Business* — Foster Provost \u0026 Tom Fawcett  \n\n---\n\n## 🧾 Example Mini Project Ideas\n- Sentiment analysis of product reviews  \n- Image classifier using CNN  \n- Chatbot using NLP and text embeddings  \n- Predictive model for customer behavior  \n- AI-powered data visualization dashboard  \n\n---\n\n## 🧩 Course Keywords\n`Python` · `AI Programming` · `Machine Learning` · `Data Science` · `Deep Learning` · `NumPy` · `PyTorch` · `TensorFlow` · `OOP` · `Data Visualization`\n\n---\n\n## 📂 Structure Example\n```\n\nProgramming-for-AI/\n├── assignments/\n│   ├── assignment_1_python_basics.ipynb\n│   ├── assignment_2_numpy_tensors.ipynb\n│   ├── assignment_3_ml_models.ipynb\n│   └── assignment_4_neural_networks.ipynb\n├── data/\n│   ├── sample_dataset.csv\n├── mini_project/\n│   ├── report.md\n│   └── presentation.pptx\n└── README.md\n\n```\n\n---\n\n## 🚀 Outcome\nAfter completing this course, you will be able to independently develop, test, and optimize AI models — from data preprocessing to deployment — using professional programming and machine learning tools.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fansinitro%2Fprogramming-for-ai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fansinitro%2Fprogramming-for-ai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fansinitro%2Fprogramming-for-ai/lists"}