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

In this course, i did reviewed two main components: First, i learned about 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.

In this course, it was possible to practice with real-life examples of Machine learning and see how it affects society in ways i may not have guessed!

By just putting in a few hours a week, this is what i got.

1. Review some 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.

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 - Cancer detection.
9. K-Means - Customer Segmentation.
10. Hierarchical Clustering - Cars clustering.
11. DBSCAN - Weather Station Clustering.
12. Colaborative Filtering - Creation of a recommendation system.
13. Content Based Filtering - Creation of a recommendation system.
14. Final project with full pipeline and aplication of classification algorithms: KNN, Decision Treens, SVM and Logistic Regression .