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learn-codesignal-ml

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Repo for learning ML via CodeSignal challenges, including algorithms and projects

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# Learn CodeSignal ML

A comprehensive collection of machine learning, AI, and web development resources from CodeSignal's learning paths, implemented in Jupyter notebooks.

## Project Overview

This repository contains Jupyter notebooks covering various topics from CodeSignal's course paths (https://learn.codesignal.com/course-paths). The project spans machine learning fundamentals through advanced agent systems, cloud engineering, and AI-powered applications — **41 courses** total.

## Complete Course Materials

### 1. Machine Learning & AI Fundamentals

1. **Journey into Machine Learning with Sklearn and Tensorflow**
   - Data Cleaning and Preprocessing in Machine Learning
   - Foundational Machine Learning Models with Sklearn
   - Feature Engineering for Machine Learning
   - Intro to Model Optimization in Machine Learning
   - Introduction to Neural Networks with TensorFlow

2. **AI Theory and Coding**
   - Regression and Gradient Descent
   - Classification Algorithms and Metrics
   - Gradient Descent: Building Optimization Algorithms from Scratch
   - Ensemble Methods from Scratch
   - Unsupervised Learning and Clustering
   - Neural Networks Basics from Scratch

3. **Introduction to Machine Learning with SciKit Learn**
   - Training Your First Machine Learning Model from Scratch
   - Data Preprocessing For Machine Learning
   - Diving Deep into Regression
   - Cracking Classification
   - Deep Dive into Regression and Classification Metrics
   - Ensembles in Machine Learning
   - Hypertuning and Cross-Validation

### 2. Advanced Data Science & Analytics

4. **Mastering Dimensionality Reduction with Python**
   - Navigating Data Simplification with PCA
   - Linear Landscapes of Dimensionality Reduction
   - Non-linear Dimensionality Reduction Techniques
   - Enigmatic Autoencoders for Dimensionality Reduction
   - Dimensionality Reduction with Feature Selection

5. **Deep Dive into Numpy and Pandas**
   - NumPy Basics
   - Pandas Basics and DataFrame Manipulation
   - Introduction to Data Cleaning and Transformation
   - Advanced Data Analysis with Pandas
   - Data Transformation Techniques in Pandas
   - Comprehensive Data Wrangling and Analysis with Pandas and Numpy

6. **Mastering Clustering in Machine Learning**
   - K-means Clustering Decoded
   - Hierarchical Clustering Deep Dive
   - Density-Based Clustering Simplified
   - Cluster Performance Unveiled

### 3. Deep Learning & Neural Networks

7. **Comprehensive Introduction to Tensorflow**
   - Introduction to TensorFlow Basics
   - Building a Neural Network in TensorFlow
   - Modeling the Iris Dataset with TensorFlow
   - TensorFlow Techniques for Model Optimization

8. **Mathematical Foundations for Deep Learning**
   - Introduction to Linear Algebra for Machine Learning
   - Introduction to Calculus for Machine Learning
   - Advanced Calculus for Machine Learning
   - Foundations of Optimization Algorithms
   - Introduction to Probability and Statistics for Machine Learning

9. **Building a Sketch Recognition System with CNN**
   - Introduction to Drawing Recognition and CNN Fundamentals
   - Data Preparation for Drawing Recognition
   - Drawing Recognition with CNNs for Sketches
   - Building the UI for Drawing Recognition

### 4. Natural Language Processing

10. **Introduction to Natural Language Processing**
    - Introduction to Text Data Exploration in Python
    - Text Data Preprocessing in Python
    - Introduction to TF-IDF Vectorization in Python
    - Building and Evaluating Text Classifiers in Python

11. **Text Classification with Natural Language Processing**
    - Collecting and Preparing Textual Data for Classification
    - Feature Engineering for Text Classification
    - Introduction to Modeling Techniques for Text Classification
    - Advanced Modeling for Text Classification

12. **Token Classification in NLP using spaCy**
    - Building an NLP Pipeline with spaCy for Token Classification
    - Linguistics for Token Classification in spaCy
    - Practical Applications of spaCy for Real-Life Tasks

13. **Data Processing for LLMs**
    - Foundations of NLP Data Processing
    - Modern Tokenization Techniques for AI & LLMs
    - Optimized Data Preparation for Large-Scale LLMs
    - Chunking and Storing Text for Efficient LLM Processing

### 5. Generative AI & LLMs

14. **Prompt Engineering for Everyone**
    - Understanding LLMs and Basic Prompting Techniques
    - Engineering Output Size with LLMs
    - Journey Into Format Control in Prompt Engineering
    - Prompt Engineering for Precise Text Modification
    - Advanced Techniques in Prompt Engineering

15. **Generative AI for Everyone in 2025**
    - Generative AI in 2025 - Overview and Practice
    - Mastering Communication with AI Language Models
    - Applying Generative AI in Everyday Professional Tasks
    - Making Things Shine - Practice and Learn Image Generation with AI
    - Generative AI - The Next Frontier: Voice, Video, and More

16. **Foundations of Retrieval Augmented Generation (RAG) Systems**
    - Introduction to RAG
    - Text Representation Techniques for RAG Systems
    - Scaling up RAG with Vector Databases
    - Beyond Basic RAG: Improving our Pipeline

17. **Talk to Your Documents with LangChain and Python**
    - LangChain Chat Essentials in Python
    - Document Processing and Retrieval with LangChain in Python
    - Building a RAG-Powered Chatbot with LangChain and Python

18. **LLM Evaluation Techniques in Practice**
    - Benchmarking LLMs with QA
    - Benchmarking LLMs on Text Generation
    - Scoring LLM Outputs with Logprobs and Perplexity
    - Behavioral Benchmarking of LLMs

19. **Optimizing Prompts with DSPy**
    - DSPy Programming
    - Evaluation in DSPy
    - How to Optimize with DSPy

### 6. AI Applications & Services

20. **Implementing Video Transcriber with OpenAI Whisper in Python**
    - Getting Started with OpenAI Whisper API in Python
    - Transcribing Large Files in Python using FFmpeg
    - Scraping and Transcribing Remote Videos

21. **Building a Chatbot with FastAPI and OpenAI**
    - Creating a Chatbot with OpenAI in Python
    - Building a Chatbot Service With FastAPI
    - Developing a Chatbot Web Application With FastAPI

22. **Building a Personal Tutor with DeepSeek and FastAPI**
    - Creating a Personal Tutor with DeepSeek in Python
    - Building a Personal Tutor Service With FastAPI
    - Developing a Personal Tutor Web Application With FastAPI

23. **MCP Servers Made Easy with Python and OpenAI Agents**
    - Introduction to OpenAI Agents SDK in Python
    - Developing and Integrating an MCP Server in Python
    - Advanced MCP Server and Agent Integration in Python

24. **Building a Code Review Assistant with OpenAI**
    - Database Setup and Code Ingestion
    - AI Integration and Analysis
    - Web Application and API

25. **Building a Deep Researcher using Python and Streamlit**
    - Building Reusable LLM Components in Python
    - Automating Web Content Retrieval and Parsing in Python
    - Creating a Researcher in Python with OpenAI
    - Making a Frontend for our Researcher with Streamlit

26. **Building a Chatbot with Flask and OpenAI**
    - Creating a Chatbot with OpenAI in Python
    - Building a Chatbot Service With Flask
    - Developing a Chatbot Web Application With Flask

### 7. Web Development & APIs

27. **Introduction to Django for Back-End Development**
    - First Steps Into Back-End Engineering with Django
    - Managing Data with SQLite and Django ORM
    - Retrieving and Manipulating Data with Django ORM
    - Building a Full-Featured To-Do List Application

28. **APIs Made Easy with Python and Flask**
    - Introduction to Flask Basics
    - Mastering Flask HTTP Methods
    - Flask Data Modeling with Marshmallow
    - Securing Flask Apps with JWT Authentication

29. **Mastering Web Scraping with Python and Beautiful Soup**
    - Basic Python and Web Requests
    - Introduction to BeautifulSoup for Web Scraping
    - Advanced Web Scraping Techniques
    - Implementing Scalable Web Scraping with Python

### 8. Cloud & Production Systems

30. **Mastering Cloud Engineering with AWS and Python**
    - Introduction to AWS SDK for Python
    - Mastering Amazon S3 with AWS SDK for Python
    - Introduction to DynamoDB with AWS SDK for Python
    - Mastering Messaging with AWS SDK for Python
    - AWS Secrets Management with AWS SDK for Python

31. **Deploying ML Models in Production**
    - Building Reusable Pipeline Functions
    - Model Serving with FastAPI
    - Automating Retraining with Apache Airflow

32. **Learning and Mastering Redis with Python**
    - Introduction to Redis with Python: The Basics
    - Mastering Redis Data Structures with Python: Beyond Basics
    - Mastering Redis Transactions and Efficiency with Python
    - Mastering Redis for High-Performance Applications with Python
    - Implementing a Redis-based Backend System with Python

33. **An Overview of GCP Development**
    - Developing with Core GCP Services
    - Building Serverless GCP Applications
    - Developer Security and Observability

34. **Mastering Cloud Engineering with GCP and Python**
    - Introduction to Google Cloud SDK for Python
    - Mastering Google Cloud Storage with Python
    - Introduction to Google Cloud Firestore with Python
    - Mastering Messaging with Google Cloud Pub/Sub and Python
    - GCP Secrets and Key Management with Python

### 9. Specialized Topics

35. **Intro to Machine Learning in Trading with $TSLA**
    - Basic $TSLA Financial Data Handling in Pandas
    - Technical Indicators in Financial Analysis with Pandas
    - Preparing Financial Data for Machine Learning
    - Introduction to Machine Learning with Gradient Boosting Models

36. **Mastering Algorithms and Data Structures in Python**
    - Hashing, Dictionaries, and Sets in Python
    - Sorting and Searching Algorithms in Python
    - Linked Lists, Stacks, and Queues in Python
    - Understanding and Using Trees in Python
    - Mastering Graphs in Python

37. **AI Interviews - Software Design, Architecture, and More**
    - AI Interviews: Software Development and Methodologies
    - AI Interviews: System Architecture and Design
    - AI Interviews: Network and Data Management
    - AI Interviews: System Performance and Security

38. **Getting Started with SQL with Leo Messi**
    - Getting Started with SQL with Leo Messi
    - Learning SQL Joins with Leo Messi
    - Mastering SQL Functions and Clauses with Leo Messi
    - Advanced Query Techniques and Conditional Logic in SQL

39. **Go Programming for Beginners**
    - Getting Started with Go Programming Language
    - Exploring Simple Data Structures in Go
    - Iterations and Loops in Go
    - Mastering Debugging with Go
    - Go String Manipulation for Beginners
    - Creating Functions in Go
    - Understanding Structs and Interfaces in Go

### 10. AI Agent Development

40. **Building a 12-Factor Agents App in Python**
    - Understanding the 12-Factor Agents Methodology
    - Foundations of Agentic Tool Use in Python
    - Developing a Stateless Agent in Python
    - Exposing Agents with Simple APIs in Python

41. **Learning Claude Code Basics**
    - Getting Started with Claude Code
    - Customizing Claude Code for Your Projects
    - Advanced Claude Code Features
    - Extending Claude's Capabilities with Agent Skills
    - Claude Code: Applying What You've Learned

## Why This Project?

- **Practical Learning**: Hands-on implementation of theoretical concepts
- **Diverse Topics**: Covers ML, AI, web development, cloud engineering, and agent systems
- **Industry Relevance**: Focuses on in-demand skills and technologies
- **Open Source**: Encourages collaboration and knowledge sharing

## How to Use

1. Clone the repository
2. Open the Jupyter notebooks
3. Explore the markdown explanations and code examples
4. Experiment with the code and adapt it to your projects

## Development Tools

### `create_folder.py` - Folder Structure Generator

**Purpose**: Automatically creates folder structures based on course text files.

**How it works**:
- Reads a text file containing course outline
- Detects lines ending with "practices" as course section indicators
- Creates numbered folders for each section found after a "practices" line

**Usage**:
```bash
python create_folder.py <text_file>
```

**Input format example**:
```
Unit 1
4 practices
16 min
Introduction to Tokenization (Rule-Based Tokenization)
Preview
Tokenize Text with NLTK
```

**Output**: Creates folder `1. Introduction to Tokenization (Rule-Based Tokenization)`

---

### `generate-ipynb.sh` - Jupyter Notebook Generator

**Purpose**: Converts structured text files into Jupyter notebooks with proper formatting.

**Features**:
- **Smart Filtering**: Automatically skips lines containing `N practices`, `N min`, or `Preview`
- **Intelligent Naming**: Creates notebook filenames using Unit number + first valid content line
- **Content Structure**: Converts text content to markdown cells with proper heading hierarchy

**Usage**:
```bash
./generate-ipynb.sh <input_text_file>
```

**Output example**:
- `1.Introduction-to-Tokenization-(Rule-Based-Tokenization).ipynb`
- `2.Byte-Pair-Encoding-(BPE)-Subword-Tokenization.ipynb`

---

### `rename_windows_safe.py` - Windows-Safe File Renamer

**Purpose**: Renames files and folders to be safe on Windows file systems.

**What it fixes**:
- Replaces Windows-forbidden characters: `: < > | " * ? \`
- Collapses double spaces and removes trailing dots/spaces
- Guards against Windows reserved names (CON, NUL, COM1–9, LPT1–9)

**Usage**:
```bash
# Dry-run (shows what would change, nothing renamed)
python rename_windows_safe.py <dir> [<dir> ...]

# Actually apply renames
python rename_windows_safe.py --execute <dir> [<dir> ...]
```

**Example**:
```bash
python rename_windows_safe.py --execute doc 40.Building-a-12-Factor-Agents-App-in-Python
```

---

### Workflow Example

1. **Organize Structure**: Use `create_folder.py` to create folder hierarchy
2. **Generate Content**: Use `generate-ipynb.sh` to create notebook files
3. **Manual Enhancement**: Add code cells, explanations, and examples to the notebooks
4. **Cross-platform Safety**: Run `rename_windows_safe.py` to ensure names work on Windows

## Contributions

Contributions are welcome! Feel free to submit pull requests with improvements, additional exercises, or bug fixes.

## License

This project is open-source and available under the MIT License.

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