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, Learning about the purpose of Machine Learning and where it applies to the real world.
• Second, a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.

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

What I achieved:

1. New skills to add to my resume, 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

In module, I learned about applications of Machine Learning in different fields such as health care, banking, telecommunication, and so on. Got a general overview of Machine Learning topics such as supervised vs unsupervised learning, and the usage of each algorithm. Also, understood the advantage of using Python libraries for implementing Machine Learning models.

• To give examples of Machine Learning
• To demonstrate the Python libraries for Machine Learning
• To classify Supervised vs. Unsupervised algorithms

In this module, I got a brief intro to regression. I learned about Linear, Non-linear, Simple and Multiple regression, and their applications. Applied all these methods on two different datasets, in the lab part. Also, learned how to evaluate your regression model, and calculate its accuracy.

• To underestand the basics of regression
• To apply Simple and Multiple, Linear and Non-Linear Regression on a dataset for estimation.

In this module, I learned about classification technique. I practice with different classification algorithms, such as KNN, Decision Trees, Logistic Regression and SVM. Also, I learned about pros and cons of each method, and different classification accuracy metrics.

• To underestand different Classification methods.
• To apply Classification algorithms on various datasets to solve real world problems.
• To underestand evaluation methods in Classification.

In this module, I learned about different clustering approaches. I learned how to use clustering for customer segmentation, grouping same vehicles, and also clustering of weather stations. I understood 3 main types of clustering, including Partitioned-based Clustering, Hierarchical Clustering, and Density-based Clustering.

• To underestand different types of clustering algorithms.
• To apply clustering on different types of datasests.

In this module, I learned about recommender systems. First, I got introduced with main idea behind recommendation engines, then understood two main types of recommendation engines, namely, content-based and collaborative filtering.

• To understand the purpuse and mechanizem of recommendation systems.
• To understand different types of recommander systems.
• To implmenet recommender system on a real dataset.

In this module, I did a project based of what you have learned so far.

• The Machine Learning knowledge on a problem.
• Submit a link to your project notebook and a complete project report.