Machine Learning Specialization

This repo contains all my code for "Practice Lab" for this specialization https://www.coursera.org/specializations/machine-learning-introduction

Course 1: Supervised Machine Learning: Regression and Classification

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

Course 2: Advanced Learning Algorithms

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

Course 3: Unsupervised Learning, Recommenders, Reinforcement Learning

• Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.
• Build recommender systems with a collaborative filtering approach and a content-based deep learning method.
• Build a deep reinforcement learning model.