🤖 Python Machine Learning Study Guide

A comprehensive, hands-on curriculum for mastering Machine Learning with Python

From mathematical foundations to production deployment

Getting Started • Learning Path • Architecture • Technologies

📖 Table of Contents

Why This Project?
Project Goals
System Architecture
Learning Path
Technology Stack
Project Timeline
Getting Started
Project Structure
Current Progress

🎯 Why This Project?

The Problem

Learning Machine Learning is challenging due to:

Challenge	Impact	Our Solution
Fragmented Resources	Learners jump between tutorials without cohesion	Unified, progressive curriculum
Theory-Practice Gap	Math concepts don't connect to code	Every concept has implementation
No Production Focus	Tutorials don't cover real-world deployment	End-to-end projects with deployment
Outdated Content	Many resources use deprecated libraries	Modern stack (PyTorch 2.0+, Python 3.12)
Missing Testing	No emphasis on code quality	TDD approach with pytest

The Solution

This study guide provides a structured, progressive path from Python basics to deploying production ML systems:

flowchart LR
    subgraph Foundation["🔧 Foundation"]
        A[NumPy]
        B[Pandas]
        C[Visualization]
    end

    subgraph Classical["📊 Classical ML"]
        D[Supervised]
        E[Unsupervised]
    end

    subgraph Deep["🧠 Deep Learning"]
        F[Neural Nets]
        G[CNNs]
        H[RNNs]
    end

    subgraph Applied["🚀 Applied"]
        I[NLP]
        J[Computer Vision]
        K[Projects]
    end

    Foundation --> Classical
    Classical --> Deep
    Deep --> Applied

    style Foundation fill:#1a1a2e,stroke:#00d4ff,color:#fff
    style Classical fill:#1a1a2e,stroke:#00ff88,color:#fff
    style Deep fill:#1a1a2e,stroke:#ff6b6b,color:#fff
    style Applied fill:#1a1a2e,stroke:#ffd93d,color:#fff

🎓 Project Goals

Learning Objectives

mindmap
  root((ML Mastery))
    Foundations
      NumPy Arrays
      Pandas DataFrames
      Data Visualization
      Statistics
    Classical ML
      Regression
      Classification
      Clustering
      Dimensionality Reduction
    Deep Learning
      Neural Networks
      CNNs
      RNNs/LSTMs
      Transformers
    Applications
      NLP
      Computer Vision
      Time Series
      Deployment

Success Metrics

Metric	Target	Measurement
Notebooks Completed	50+	Interactive Jupyter notebooks
Unit Tests	90%+ coverage	pytest with coverage reports
Projects Built	5+ end-to-end	From data to deployment
Code Quality	100% type-hinted	mypy + pylint passing

🏗️ System Architecture

High-Level Overview

flowchart TB
    subgraph Input["📥 Input Layer"]
        direction TB
        NB["📓 Jupyter Notebooks"]
        DATA["📁 Datasets"]
        CFG["⚙️ Configs"]
    end

    subgraph Core["⚙️ Core Processing"]
        direction TB
        SRC["🐍 Source Code"]
        UTILS["🔧 Utilities"]
        MODELS["🤖 Models"]
        VIZ["📊 Visualization"]
    end

    subgraph Quality["✅ Quality Assurance"]
        direction TB
        TESTS["🧪 Tests"]
        LINT["📝 Linting"]
        DOCS["📚 Documentation"]
    end

    subgraph Deploy["�� Deployment"]
        direction TB
        DOCKER["🐳 Docker"]
        API["🌐 API"]
    end

    Input --> Core
    Core --> Quality
    Quality --> Deploy

    style Input fill:#2d3436,stroke:#00cec9,color:#fff
    style Core fill:#2d3436,stroke:#6c5ce7,color:#fff
    style Quality fill:#2d3436,stroke:#00b894,color:#fff
    style Deploy fill:#2d3436,stroke:#e17055,color:#fff

Directory Architecture

flowchart TD
    subgraph Root["📁 python-ML-learn"]
        direction TB

        subgraph Learning["📚 Learning Modules"]
            F01["01-fundamentals/"]
            F02["02-supervised-learning/"]
            F03["03-unsupervised-learning/"]
            F04["04-deep-learning/"]
            F05["05-nlp/"]
            F06["06-computer-vision/"]
            F07["07-projects/"]
        end

        subgraph Source["💻 Source Code"]
            SRC_UTILS["src/utils/"]
            SRC_MODELS["src/models/"]
            SRC_DATA["src/data_processing/"]
            SRC_VIZ["src/visualization/"]
        end

        subgraph Support["🔧 Support"]
            TESTS["tests/"]
            DOCS["docs/"]
            DOCKER["docker/"]
            MEMORY["memory-bank/"]
        end
    end

    style Root fill:#1e272e,stroke:#fff,color:#fff
    style Learning fill:#2d3436,stroke:#74b9ff,color:#fff
    style Source fill:#2d3436,stroke:#a29bfe,color:#fff
    style Support fill:#2d3436,stroke:#55efc4,color:#fff

Data Flow Architecture

flowchart LR
    subgraph Data["📊 Data Pipeline"]
        RAW["Raw Data"]
        CLEAN["Cleaned Data"]
        FEAT["Features"]
    end

    subgraph Model["🤖 Model Pipeline"]
        TRAIN["Training"]
        VAL["Validation"]
        TEST["Testing"]
    end

    subgraph Output["📈 Output"]
        PRED["Predictions"]
        METRICS["Metrics"]
        VIZ["Visualizations"]
    end

    RAW --> CLEAN
    CLEAN --> FEAT
    FEAT --> TRAIN
    TRAIN --> VAL
    VAL --> TEST
    TEST --> PRED
    TEST --> METRICS
    METRICS --> VIZ

    style Data fill:#2d3436,stroke:#00cec9,color:#fff
    style Model fill:#2d3436,stroke:#6c5ce7,color:#fff
    style Output fill:#2d3436,stroke:#fdcb6e,color:#fff

📚 Learning Path

Phase Overview

flowchart TB
    subgraph P1["Phase 1: Foundation"]
        direction LR
        P1A["Week 1-2"]
        P1B["Infrastructure<br/>& Setup"]
        P1A --> P1B
    end

    subgraph P2["Phase 2: Fundamentals"]
        direction LR
        P2A["Week 3-5"]
        P2B["NumPy, Pandas<br/>Statistics, Viz"]
        P2A --> P2B
    end

    subgraph P3["Phase 3: Supervised"]
        direction LR
        P3A["Week 6-8"]
        P3B["Regression<br/>Classification"]
        P3A --> P3B
    end

    subgraph P4["Phase 4: Unsupervised"]
        direction LR
        P4A["Week 9-10"]
        P4B["Clustering<br/>PCA, t-SNE"]
        P4A --> P4B
    end

    subgraph P5["Phase 5: Deep Learning"]
        direction LR
        P5A["Week 11-13"]
        P5B["Neural Nets<br/>CNN, RNN"]
        P5A --> P5B
    end

    subgraph P6["Phase 6-9: Advanced"]
        direction LR
        P6A["Week 14-26"]
        P6B["NLP, CV<br/>Projects, MLOps"]
        P6A --> P6B
    end

    P1 --> P2 --> P3 --> P4 --> P5 --> P6

    style P1 fill:#1e3a5f,stroke:#3498db,color:#fff
    style P2 fill:#1e3a5f,stroke:#2ecc71,color:#fff
    style P3 fill:#1e3a5f,stroke:#9b59b6,color:#fff
    style P4 fill:#1e3a5f,stroke:#e74c3c,color:#fff
    style P5 fill:#1e3a5f,stroke:#f39c12,color:#fff
    style P6 fill:#1e3a5f,stroke:#1abc9c,color:#fff

Curriculum Details

📘 Phase 1: Foundation (Weeks 1-2)

Topic	Description	Deliverable
Project Structure	Modular src layout	Folder hierarchy
Development Environment	VS Code + extensions	`.vscode/settings.json`
Docker Setup	Reproducible environment	`Dockerfile`, `docker-compose.yml`
Testing Framework	pytest configuration	`conftest.py`, `pytest.ini`

Status: ✅ Complete

📗 Phase 2: Core ML Fundamentals (Weeks 3-5)

Topic	Key Concepts	Notebook
NumPy	Arrays, broadcasting, linear algebra	`01_numpy_fundamentals.ipynb`
Pandas	DataFrames, cleaning, aggregation	`02_pandas_data_manipulation.ipynb`
Visualization	matplotlib, seaborn, plotly	`03_data_visualization.ipynb`
Statistics	Distributions, hypothesis testing	`04_statistics_for_ml.ipynb`
Scikit-learn Intro	Pipelines, preprocessing, models	`05_sklearn_introduction.ipynb`

Status: ✅ Complete (5 notebooks, 114 tests)

📙 Phase 3: Supervised Learning (Weeks 6-8)

Algorithm	Mathematical Foundation	Implementation
Linear Regression	$\hat{y} = X\beta$, MSE loss	From scratch + sklearn
Logistic Regression	Sigmoid, cross-entropy	Binary & multiclass
Decision Trees	Gini impurity, entropy	Visualization included
Random Forests	Bagging, feature importance	Hyperparameter tuning
SVM	Kernel trick, margin maximization	Multiple kernels
Gradient Boosting	Sequential ensembles	XGBoost, LightGBM

Status: ✅ Complete (5 notebooks, 15 tests)

📕 Phase 4: Unsupervised Learning (Weeks 9-10)

Algorithm	Purpose	Implementation
K-Means	Centroid-based clustering	From scratch + sklearn
Hierarchical	Agglomerative clustering	Dendrograms
DBSCAN	Density-based clustering	Parameter tuning
PCA	Dimensionality reduction	From scratch + sklearn
t-SNE	Visualization	Perplexity tuning
Anomaly Detection	Outlier detection	Isolation Forest, LOF, One-Class SVM

Status: ✅ Complete (3 notebooks, 37 tests)

📕 Phase 5-9: Advanced Topics (Weeks 11-26)

Phase	Topics	Hours
5. Deep Learning	Neural nets, CNN, RNN, PyTorch	90
6. NLP	Embeddings, BERT, Transformers	70
7. Computer Vision	Object detection, segmentation	70
8. Projects	End-to-end ML systems	100+
9. MLOps	Deployment, monitoring, CI/CD	40

🛠️ Technology Stack

Core Technologies Explained

flowchart TB
    subgraph Languages["🐍 Languages & Runtime"]
        PY["Python 3.12+"]
        JUP["Jupyter"]
    end

    subgraph DataScience["📊 Data Science"]
        NP["NumPy"]
        PD["Pandas"]
        SP["SciPy"]
    end

    subgraph Visualization["📈 Visualization"]
        MPL["Matplotlib"]
        SNS["Seaborn"]
        PLT["Plotly"]
    end

    subgraph ML["🤖 Machine Learning"]
        SK["scikit-learn"]
        XG["XGBoost"]
        LG["LightGBM"]
    end

    subgraph DL["🧠 Deep Learning"]
        PT["PyTorch"]
        TF["TensorFlow"]
        HF["Transformers"]
    end

    subgraph DevOps["🔧 DevOps"]
        DOC["Docker"]
        GIT["Git"]
        TEST["pytest"]
    end

    Languages --> DataScience
    Languages --> Visualization
    DataScience --> ML
    ML --> DL
    DL --> DevOps

    style Languages fill:#2c3e50,stroke:#3498db,color:#fff
    style DataScience fill:#2c3e50,stroke:#2ecc71,color:#fff
    style Visualization fill:#2c3e50,stroke:#9b59b6,color:#fff
    style ML fill:#2c3e50,stroke:#e74c3c,color:#fff
    style DL fill:#2c3e50,stroke:#f39c12,color:#fff
    style DevOps fill:#2c3e50,stroke:#1abc9c,color:#fff

Technology Reference

Technology	Version	Purpose	Why Chosen
Python	3.12+	Core language	Industry standard, rich ecosystem
NumPy	2.4+	Numerical computing	10-100x faster than pure Python, vectorization
Pandas	2.0+	Data manipulation	Intuitive DataFrame API, SQL-like operations
scikit-learn	1.3+	Classical ML	Consistent API, comprehensive algorithms
PyTorch	2.0+	Deep learning	Dynamic graphs, Pythonic, research-friendly
TensorFlow	2.13+	Deep learning	Production-ready, TensorBoard, Keras API
Matplotlib	3.7+	Plotting	Highly customizable, publication quality
Seaborn	0.12+	Statistical viz	Beautiful defaults, statistical plots
Docker	Latest	Containerization	Reproducible environments
pytest	7.4+	Testing	Simple syntax, powerful fixtures

NumPy: The Foundation

flowchart LR
    subgraph NumPy["NumPy Ecosystem"]
        ARR["ndarray<br/>N-dimensional arrays"]
        UFUNC["ufuncs<br/>Element-wise ops"]
        LINALG["linalg<br/>Matrix operations"]
        RAND["random<br/>Statistical sampling"]
    end

    subgraph Benefits["Why NumPy?"]
        SPEED["⚡ 10-100x Faster"]
        MEM["💾 Memory Efficient"]
        BROAD["📡 Broadcasting"]
        INTER["🔗 Interoperability"]
    end

    NumPy --> Benefits

    style NumPy fill:#2c3e50,stroke:#013243,color:#fff
    style Benefits fill:#2c3e50,stroke:#4dabf7,color:#fff

Definition: NumPy is the fundamental package for scientific computing in Python.

Motivation: Python lists are slow for numerical operations. NumPy provides:

Contiguous memory allocation
Vectorized operations (no Python loops)
C-level execution speed

Mechanism:

# Python list (slow)
result = [x ** 2 for x in range(1000000)]  # ~200ms

# NumPy array (fast)
arr = np.arange(1000000)
result = arr ** 2  # ~2ms (100x faster!)

Impact: Enables processing of large datasets that would be impractical with pure Python.

📅 Project Timeline

Gantt Chart

gantt
    title ML Study Guide - 26 Week Timeline
    dateFormat  YYYY-MM-DD

    section Phase 1
    Infrastructure Setup    :done,    p1, 2025-12-16, 2w

    section Phase 2
    NumPy Fundamentals      :done,    p2a, after p1, 3d
    Pandas & Data           :active,  p2b, after p2a, 1w
    Visualization           :         p2c, after p2b, 5d
    Statistics              :         p2d, after p2c, 4d
    Feature Engineering     :         p2e, after p2d, 5d

    section Phase 3
    Linear Regression       :         p3a, after p2e, 5d
    Logistic Regression     :         p3b, after p3a, 5d
    Decision Trees          :         p3c, after p3b, 6d
    SVM & Boosting          :         p3d, after p3c, 1w

    section Phase 4
    Clustering              :         p4a, after p3d, 1w
    Dimensionality Reduction:         p4b, after p4a, 1w

    section Phase 5
    Neural Networks         :         p5a, after p4b, 2w
    CNN & RNN               :         p5b, after p5a, 2w

    section Phase 6-9
    NLP                     :         p6, after p5b, 3w
    Computer Vision         :         p7, after p6, 3w
    Projects                :         p8, after p7, 4w
    MLOps                   :         p9, after p8, 2w

Milestone Tracker

Milestone	Target	Status	Progress
M1: Infrastructure	Week 2	✅ Complete	████████████ 100%
M2: Fundamentals	Week 5	✅ Complete	████████████ 100%
M3: Supervised	Week 8	✅ Complete	████████████ 100%
M4: Unsupervised	Week 10	✅ Complete	████████████ 100%
M5: Deep Learning	Week 13	✅ Complete	████████████ 100%
M6: NLP	Week 16	✅ Complete	████████████ 100%
M7: Computer Vision	Week 19	✅ Complete	████████████ 100%
M8: Projects	Week 24	✅ Complete	████████████ 100%
M9: MLOps	Week 26	⭕ Not Started	░░░░░░░░░░░░ 0%

🚀 Getting Started

Prerequisites

Requirement	Version	Check Command
Python	3.8+	`python --version`
pip	Latest	`pip --version`
Git	Latest	`git --version`
Docker (optional)	Latest	`docker --version`

Quick Start

# 1. Clone the repository
git clone https://github.com/yourusername/python-ML-learn.git
cd python-ML-learn

# 2. Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Start Jupyter Lab
jupyter lab

Docker Setup (Recommended)

# Build and run with Docker Compose
cd docker
docker-compose up -d

# Access Jupyter Lab at http://localhost:8888

Verify Installation

# Run tests to verify setup
python -m pytest tests/ -v

# Expected output: All tests passing

📁 Project Structure

python-ML-learn/
├── 📓 01-fundamentals/          # NumPy, Pandas, Visualization (5 notebooks)
│   ├── 01_numpy_fundamentals.ipynb
│   ├── 02_pandas_data_manipulation.ipynb
│   ├── 03_data_visualization.ipynb
│   ├── 04_statistics_for_ml.ipynb
│   └── 05_sklearn_introduction.ipynb
├── 📓 02-supervised-learning/   # Regression, Classification (5 notebooks)
│   ├── 01_linear_regression.ipynb
│   ├── 02_logistic_regression.ipynb
│   ├── 03_decision_trees_random_forests.ipynb
│   ├── 04_svm.ipynb
│   └── 05_gradient_boosting.ipynb
├── 📓 03-unsupervised-learning/ # Clustering, PCA (3 notebooks)
│   ├── 01_clustering.ipynb
│   ├── 02_dimensionality_reduction.ipynb
│   └── 03_anomaly_detection.ipynb
├── 📓 04-deep-learning/         # Neural Networks, CNN, RNN (5 notebooks)
│   ├── 01_neural_network_fundamentals.ipynb
│   ├── 02_pytorch_introduction.ipynb
│   ├── 03_convolutional_neural_networks.ipynb
│   ├── 04_recurrent_neural_networks.ipynb
│   └── 05_training_techniques.ipynb
├── 📓 05-nlp/                   # Text Processing, Transformers (5 notebooks)
│   ├── 01_text_preprocessing.ipynb
│   ├── 02_text_vectorization.ipynb
│   ├── 03_word_embeddings.ipynb
│   ├── 04_text_classification.ipynb
│   └── 05_transformers_introduction.ipynb
├── 📓 06-computer-vision/       # Object Detection, Segmentation (5 notebooks)
│   ├── 01_image_fundamentals.ipynb
│   ├── 02_cnn_architectures.ipynb
│   ├── 03_transfer_learning.ipynb
│   ├── 04_object_detection.ipynb
│   └── 05_image_segmentation.ipynb
├── 📓 07-projects/              # End-to-End Projects (5 notebooks)
│   ├── 01_house_price_prediction.ipynb
│   ├── 02_customer_churn_prediction.ipynb
│   ├── 03_image_classification_app.ipynb
│   ├── 04_sentiment_analysis_pipeline.ipynb
│   └── 05_recommendation_system.ipynb
├── 📓 08-mlops/                 # MLOps & Production (5 notebooks)
│   ├── 01_model_serving_fastapi.ipynb
│   ├── 02_docker_containerization.ipynb
│   ├── 03_experiment_tracking.ipynb
│   ├── 04_cicd_pipelines.ipynb
│   └── 05_model_monitoring.ipynb
│
├── 💻 src/                      # Source Code
│   ├── utils/                   # Utility functions
│   │   ├── timer.py            # Performance timing
│   │   ├── numpy_helpers.py    # NumPy utilities
│   │   ├── pandas_helpers.py   # Pandas utilities
│   │   ├── stats_helpers.py    # Statistical functions
│   │   ├── sklearn_helpers.py  # Scikit-learn utilities
│   │   └── visualization_helpers.py # Plotting utilities
│   ├── ml_core/                 # ML helper modules
│   │   ├── supervised.py       # Supervised learning helpers
│   │   ├── unsupervised.py     # Unsupervised learning helpers
│   │   ├── deep_learning.py    # Deep learning helpers
│   │   ├── nlp.py              # NLP helpers
│   │   └── computer_vision.py  # Computer vision helpers
│   ├── models/                  # ML model implementations
│   ├── data_processing/         # Data pipelines
│   └── visualization/           # Plotting utilities
│
├── 🧪 tests/                    # Test Suite
│   ├── unit/                    # Unit tests
│   └── integration/             # Integration tests
│
├── 📚 docs/                     # Documentation
│   └── project-plan.md         # Detailed project plan
│
├── 🗃️ memory-bank/              # Project Memory
│   ├── change-log.md           # Version history
│   └── architecture-decisions/ # ADRs
│
├── 🐳 docker/                   # Docker Configuration
│   ├── Dockerfile
│   └── docker-compose.yml
│
├── 📊 data/                     # Datasets
│   ├── raw/                    # Original data
│   └── processed/              # Cleaned data
│
├── ⚙️ configs/                   # Configuration files
├── 📜 requirements.txt          # Python dependencies
└── 📖 README.md                 # This file

📊 Current Progress

🎉 Project Complete!

All 9 phases of the Machine Learning curriculum have been completed!

Phase Completion

pie title Project Completion by Phase
    "Phase 1 - Infrastructure" : 100
    "Phase 2 - Fundamentals" : 100
    "Phase 3 - Supervised" : 100
    "Phase 4 - Unsupervised" : 100
    "Phase 5 - Deep Learning" : 100
    "Phase 6 - NLP" : 100
    "Phase 7 - Computer Vision" : 100
    "Phase 8 - Projects" : 100
    "Phase 9 - MLOps" : 100

Test Coverage

Module	Tests	Coverage	Status
`utils/timer.py`	14	95%	✅
`utils/numpy_helpers.py`	24	100%	✅
`utils/pandas_helpers.py`	21	100%	✅
`utils/stats_helpers.py`	33	100%	✅
`utils/sklearn_helpers.py`	29	100%	✅
`utils/visualization_helpers.py`	31	100%	✅
`ml_core/supervised.py`	15	100%	✅
`ml_core/unsupervised.py`	37	100%	✅
`ml_core/deep_learning.py`	42	100%	✅
`ml_core/nlp.py`	53	100%	✅
`ml_core/computer_vision.py`	63	100%	✅

Total Tests: 362 passing ✅

Recent Updates

Date	Version	Changes
2025-12-22	v2.0.0	🎉 Phase 9: MLOps & Production (model serving, Docker, CI/CD, monitoring)
2025-12-22	v1.12.0	Phase 8: End-to-End Projects (5 comprehensive ML projects)
2025-07-09	v1.11.0	Phase 7: Computer Vision (image fundamentals, CNN, detection, segmentation)
2025-07-08	v1.10.0	Phase 6: NLP (text preprocessing, embeddings, transformers)
2025-07-08	v1.9.0	Phase 5: Deep learning (PyTorch, CNN, RNN, training techniques)
2025-07-08	v1.8.0	Phase 4: Unsupervised learning (clustering, PCA, anomaly detection)
2025-07-08	v1.7.0	Phase 3: Supervised learning (regression, classification, SVM, boosting)
2025-07-08	v1.6.0	Phase 2: Fundamentals complete (5 notebooks, helper modules)
2025-07-08	v1.0.0	Initial project structure, Docker setup

📈 Learning Tips

Best Practices

📐 Understand the Math: Don't skip mathematical intuition
💻 Code from Scratch: Implement algorithms before using libraries
📊 Visualize Everything: Use plots to understand behavior
�� Read Comments: Code is heavily documented
🔁 Practice Daily: Consistency is key
🧪 Write Tests: Verify your implementations

Study Schedule

flowchart LR
    subgraph Daily["Daily (2-3 hours)"]
        D1["📖 Theory<br/>30 min"]
        D2["💻 Coding<br/>90 min"]
        D3["📝 Review<br/>30 min"]
    end

    subgraph Weekly["Weekly"]
        W1["📓 1-2 Notebooks"]
        W2["🧪 Unit Tests"]
        W3["📊 Mini Project"]
    end

    Daily --> Weekly

    style Daily fill:#2c3e50,stroke:#3498db,color:#fff
    style Weekly fill:#2c3e50,stroke:#2ecc71,color:#fff

🔗 Resources

Documentation

Resource	Link	Description
NumPy	numpy.org	Array computing
Pandas	pandas.pydata.org	Data analysis
scikit-learn	scikit-learn.org	Machine learning
PyTorch	pytorch.org	Deep learning
TensorFlow	tensorflow.org	Deep learning

Learning Platforms

�� Kaggle Learn - Free micro-courses
📊 Papers With Code - Research implementations
🎥 3Blue1Brown - Visual math explanations

🤝 Contributing

Contributions are welcome! Please:

Fork the repository
Create a feature branch
Write tests for new code
Submit a pull request

📄 License

MIT License - Feel free to use for personal learning.

Made with ❤️ for Machine Learning Enthusiasts

⭐ Star this repo if you find it helpful!

Name		Name	Last commit message	Last commit date
Latest commit History 22 Commits
.github/instructions		.github/instructions
01-fundamentals		01-fundamentals
02-supervised-learning		02-supervised-learning
03-unsupervised-learning		03-unsupervised-learning
04-deep-learning		04-deep-learning
05-nlp		05-nlp
06-computer-vision		06-computer-vision
07-projects		07-projects
08-mlops		08-mlops
09-interview-prep		09-interview-prep
docker		docker
docs		docs
memory-bank		memory-bank
src		src
tests		tests
.gitignore		.gitignore
README.md		README.md
pytest.ini		pytest.ini
requirements.txt		requirements.txt

Folders and files

Latest commit

History

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🤖 Python Machine Learning Study Guide

📖 Table of Contents

🎯 Why This Project?

The Problem

The Solution

🎓 Project Goals

Learning Objectives

Success Metrics

🏗️ System Architecture

High-Level Overview

Directory Architecture

Data Flow Architecture

📚 Learning Path

Phase Overview

Curriculum Details

🛠️ Technology Stack

Core Technologies Explained

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NumPy: The Foundation

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