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 06:
Overview/Topics: Final Project
- Doing the final assignment in Watson studio