Machine Learning Specialization - Notebooks 📒

This repository contains my Jupyter notebooks with exercise solutions from the Machine Learning Specialization by Andrew Ng, offered by Stanford Online and DeepLearning.AI. The specialization provided a deep dive into foundational machine learning concepts, leveraging libraries like NumPy and scikit-learn, and covering a wide range of algorithms and techniques.

What I've Learned 📝

• Building and training machine learning models in Python.
• Applying both supervised and unsupervised learning techniques.
• Practical uses of algorithms like linear and logistic regression, neural networks, decision trees, and more.
• Techniques for preventing overfitting, such as regularization.
• Working with TensorFlow and frameworks for complex models like XGBoost.
• Insights into model development, anomaly detection, collaborative filtering, and reinforcement learning.

Course Structure ✍🏻

1. Supervised Machine Learning: Regression and Classification
   • Focusing on prediction and binary classification tasks.
2. Advanced Learning Algorithms
   • Diving into complex algorithms and their applications.
3. Unsupervised Learning and Recommender Systems
   • Exploring clustering, dimensionality reduction, and recommender systems.

How to Use This Repository

Feel free to explore the notebooks, run the code, and use the exercises to deepen your own understanding of machine learning concepts.

git clone https://github.com/Biotechnologyguy/machine-learning.git
cd machine-learning

Run the notebooks and have fun!! Please note that this is to document my learning only!!!

Acknowledgements

Special thanks to Andrew Ng and the instructors from Stanford Online and DeepLearning.AI for creating this amazing specialization that has enabled me to grow as a machine learning practitioner.

Happy learning!