Coursera_IBM_ML_with_Python

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python.

In this course, i reviewed two main components: First, i learned the purpose of Machine Learning and where it applies to the real world. Second, i got a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.

I practiced with real-life examples of Machine learning and see how it affects society in many ways.

After few weeks of training, this is what i got.

1. New skills such as regression, classification, clustering, sci-kit learn and SciPy.
2. New projects including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more.
3. And a certificate in machine learning to prove my competency.

Skills covered and its notebooks:

1. Simple Linear Regression.
2. Multiple Linear Regression.
3. Polynomial Regression.
4. Non-linear Regression.
5. K-Nearest Neighbors.
6. Decision Trees.
7. Logistic Regression.
8. Suport Vector Machine (SVM) - Cancer detection.
9. K-Means - Customer Segmentation.
10. Hierarchical Clustering - Cars clustering.
11. DBSCAN - Weather Station Clustering.
12. Collaborative Filtering - Creation of a recommendation system.
13. Content Based Filtering - Creation of a recommendation system.
14. Final Assignement - Loan Full pipeline + Application of classifications : KNN / Decision tree / SVM / Logistic Regression

Author :

Jennyfer WAN