Machine Learning with Python

This course is offered by IBM in coursera and this repo conitains final week's assignment(week-06) with lecture notes.

• Course: https://www.coursera.org/learn/machine-learning-with-python
• Offered by: https://www.ibm.com/
• Instructor: https://www.coursera.org/instructor/saeed
• My certificate: https://www.coursera.org/account/accomplishments/records/DAS5ACHKZE3J

Week 01:

Overview/Topics: Introduction to Machine Learning

• Python for machine learning
• Supervised vs Unsupervised learning

Programming Assignments:

• [Week 01: N/A]

Week 02:

Overview/Topics: Regression

• Simple linear regression
• Multiple linear regression
• Polynomial regression
• Non-linear regression

Programming Assignments:

• Week 02: ML0101EN-Reg-Simple-Linear-Regression-Co2-py-v1
• Week 02: ML0101EN-Reg-Mulitple-Linear-Regression-Co2-py-v1
• Week 02: ML0101EN-Reg-Polynomial-Regression-Co2-py-v1
• Week 02: ML0101EN-Reg-NoneLinearRegression-py-v1

Week 03:

Overview/Topics: Classification

• K-Nearest Neighbours
• Decision Trees
• Logistic regression
• Support Vector Machine

Programming Assignments:

• Week 03: ML0101EN-Clas-K-Nearest-neighbors-CustCat-py-v1
• Week 03: ML0101EN-Clas-Decision-Trees-drug-py-v1
• Week 03: ML0101EN-Clas-Logistic-Reg-churn-py-v1
• Week 03: ML0101EN-Clas-SVM-cancer-py-v1

Week 04:

Overview/Topics: Clustering

• K-Means Clustering
• Hierarchical clustering
• Density based clustering: DBSCAN

Programming Assignments:

• Week 04: ML0101EN-Clus-K-Means-Customer-Seg-py-v1
• Week 04: ML0101EN-Clus-Hierarchical-Cars-py-v1
• Week 04: ML0101EN-Clus-DBSCN-weather-py-v1

Week 05:

Overview/Topics: Recommender Systems

• Content based recommender systems
• Collaborative filtering on movies

Programming Assignments:

• Week 05: Content based recommender systems
• Week 05: Collaborative filtering on moives

Week 06:

Overview/Topics: Final Project

• Doing the final assignment in Watson studio

Programming Assignments:

• Week 06: Final Assignment