This repository serves as a backup and version control system for my Jupyter notebooks, lab exercises, and graded assignments from the Machine Learning Specialization course on Coursera, taught by Andrew Ng and the DeepLearning.AI team.
- Backup: Safely store all course work and progress
- Version Control: Track changes and improvements over time
- Portfolio: Document learning journey and practical implementations
- Reference: Maintain organized collection of ML concepts and code
machine-learning-specialization-2025/
├── course-1-supervised-learning/
│ ├── week-1-linear-regression/
│ │ ├── labs/ # Lab exercises
│ │ └── graded_assignments/ # Graded assignments
│ ├── week-2-logistic-regression/
│ └── week-3-classification/
├── course-2-advanced-algorithms/
│ ├── week-1-neural-networks/
│ └── week-2-tensorflow/
└── course-3-unsupervised-learning/ # Future course
The Machine Learning Specialization consists of 3 courses:
- Week 1: Linear regression fundamentals
- Week 2: Logistic regression and classification
- Week 3: Advanced classification techniques
- Week 1: Neural networks and deep learning
- Week 2: TensorFlow implementation and ReLU activations
- Week 3: Advanced neural network concepts
- Clustering algorithms
- Dimensionality reduction
- Recommender systems
- Reinforcement learning fundamentals
- Python 3.8+
- pip (Python package installer)
- Git for version control
# Clone the repository
git clone <your-repo-url>
cd machine-learning-specialization-2025
# Create virtual environment
python3 -m venv ml_env
# Activate virtual environment
source ml_env/bin/activate # On Linux/Mac
# or
ml_env\Scripts\activate # On Windows
# Install required packages
pip install -r requirements.txt- Activate Environment:
source ml_env/bin/activate - Start Jupyter:
jupyter notebookorjupyter lab - Complete Labs: Work through course exercises
- Save Progress: Commit your work regularly
- Push Changes: Keep remote repository updated
# Start Jupyter Notebook (classic interface)
jupyter notebook
# Start Jupyter Lab (modern interface)
jupyter lab
# Deactivate environment when done
deactivate- Core ML: numpy, pandas, scikit-learn, matplotlib
- Jupyter: jupyter, notebook, ipykernel
- Visualization: seaborn, plotly
- Deep Learning: tensorflow, keras (for advanced topics)
✅ Version Control: Track your progress and commit your work
✅ Offline Work: Learn without internet dependency
✅ Custom Packages: Install any additional libraries you need
✅ Performance: Local execution is typically faster
✅ Git Streak: Perfect for maintaining daily commit habits
✅ Backup Security: Multiple copies of your work
✅ Portfolio Building: Document your ML learning journey
"externally-managed-environment" error: Always use the virtual environment, not system Python.
Package not found: Ensure your virtual environment is activated (you should see (ml_env) in your terminal).
Jupyter not starting: Verify you're in the virtual environment and all packages are installed.
- Check that your virtual environment is activated
- Verify packages:
pip list - Reinstall if needed:
pip install -r requirements.txt
This repository setup provides a robust foundation for your machine learning journey. Use it to:
- Safely backup all your course work
- Track your learning progress
- Build a portfolio of ML implementations
- Maintain consistent study habits through regular commits
Good luck with your machine learning specialization!