Machine learning is changing the world and if you want to be a part of the ML revolution, this is a great place to start! In this track, you’ll learn the fundamental concepts in Machine Learning. At the end of day, the value of Data Scientists rests on their ability to describe the world and to make predictions. Machine Learning is the field of teaching machines and computers to learn from existing data to make predictions on new data - will a given tumor be benign or malignant? Which of your customers will take their business elsewhere? Is a particular email spam or not? In this course, you'll learn how to use Python to perform supervised learning, an essential component of Machine Learning. You'll learn how to build predictive models, how to tune their parameters and how to tell how well they will perform on unseen data, all the while using real world datasets. You'll do so using scikit-learn, one of the most popular and user-friendly machine learning libraries for Python.

1. Chapter1 - [Classification]
2. Chapter2 - [Regression]
3. Chapter3 - [Fine tune your model]
4. Chapter4 - [preprocessing pipeline]

1. Chapter1 - [Clustering for dataset exploration]
2. Chapter2 - [visualization with hierarchical clustering and t-sne]
3. Chapter3 - [Decorrelating your data and dimension reduction]
4. Chapter4 - [Discovering interpretable features]

1. Chapter1 - [Applying logistic regression and SVM]
2. Chapter2 - [Loss functions]
3. Chapter3 - [Logistic regression]
4. Chapter4 - [Support Vector Machines]

1. Chapter1 - [Basicsof deep learning and neural networks]
2. Chapter2 - [Optimizing a neural network with backward propagation]
3. Chapter3 - [Building deep learning models with keras]
4. Chapter4 - [Fine tuning keras models]