Machine learning with Scikit-learn

About this course

This course will explain how to use scikit-learn to do advanced machine learning. If you are aiming to work as a professional data scientist, you need to master scikit-learn! It is expected that you have some familiarity with statistics, and python programming. It's not necessary to be an expert, but you should be able to understand what is a Gaussian distribution, code loops and functions in Python, and know the basics of a maximum likelihood estimator. The course will be entirely focused on the python implementation, and the math behind it will be omitted as much as possible. The objective of this course is to provide you with a good understanding of scikit-learn (being able to identify which technique you can use for a particular problem). If you follow this course, you should be able to handle quite well a machine learning interview. Even though in that case you will need to study the math with more detail. We'll start by explaining what is the machine learning problem, methodology and terminology. We'll explain what are the differences between AI, machine learning (ML), statistics, and data mining. Scikit-learn (being a Python library) benefits from Python's spectacular simplicity and power. We'll start by explaining how to install scikit-learn and its dependencies. And then show how can we can use Pandas data in scikit-learn, and also benefit from SciPy and Numpy. We'll then show how to create synthetic data-sets using scikit-learn. We will be able to create data-sets specifically tailored for regression, classification and clustering. In essence, machine learning can be divided into two big groups: supervised and unsupervised learning. In supervised learning we will have an objective variable (which can be continuous or categorical) and we want to use certain features to predict it. Scikit-learn will provide estimators for both classification and regression problems. We will start by discussing the simpl

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