Machine Learning Specialization

This repository contains reimplementations of the Machine Learning Specialization by DeepLearning.AI & Stanford University.

Courses Followed

1. Supervised Machine Learning: Regression and Classification

• Build machine learning models in Python using popular libraries such as NumPy and scikit-learn
• Build and train supervised machine learning models for prediction and binary classification tasks,
  including linear regression and logistic regression

2. Advanced Learning Algorithms

• Build and train a neural network with TensorFlow to perform multi-class classification
• Apply best practices for machine learning development to ensure generalization to real-world data and tasks
• Build and use decision trees and tree ensemble methods, including random forests and boosted trees

3. Unsupervised Learning, Recommenders, Reinforcement Learning

• Use unsupervised learning techniques, including clustering and anomaly detection
• Build recommender systems using a collaborative filtering approach and a content-based recommendation method
• Build a deep reinforcement learning model

Certificate