Machine Learning Specialization

Welcome to the repository containing my assignments for the Machine Learning Specialization on Coursera by DeepLearning.AI and Stanford University. This specialization provides a comprehensive introduction to machine learning, covering essential concepts, algorithms, and techniques. The courses in this specialization are designed to equip learners with the skills needed to build machine learning models and understand their applications.

Courses Overview

1. Supervised Machine Learning: Regression and Classification

• Focus:
  • Introduction to building and training supervised machine learning models.
  • Key techniques include linear regression for prediction and logistic regression for binary classification.
  • Implementation using Python libraries like NumPy and scikit-learn.

2. Advanced Learning Algorithms

• Focus:
  • Advanced machine learning models including neural networks, decision trees, and ensemble methods.
  • Practical applications of TensorFlow for multi-class classification.
  • Best practices in machine learning development to ensure models generalize well.

3. Unsupervised Learning, Recommenders, Reinforcement Learning

• Focus:
  • Unsupervised learning techniques like clustering and anomaly detection.
  • Building recommender systems using collaborative filtering and content-based deep learning methods.
  • Introduction to deep reinforcement learning models

Technologies Used

License

This repository is licensed under the MIT License.

Acknowledgments

I would like to thank the Machine Learning Specialization instructors and all the contributors for their hard work and dedication to create this comprehensive and engaging course. This course was definitely one of the most important courses I have completed and will be a guiding light for my future projects.