Master Machine Learning with Python provides a comprehensive guide to become an expert at both the concepts and tools of building a machine learning workflow using Python.
Many other courses use poor practices to teach the machine learning library scikit-learn. With Master Machine Learning with Python, you will be given the absolute best practices to use the library to help you rapidly transform into an expert.
Instructor Ted Petrou has taught more than 1,000 hours of live classes using this course. Each time he uses his experience to improve explanations and clarify results. Ted Petrou has become one of the foremost authorities on how to best use the Python data science libraries.
Reading texts or listening to lectures give the false impression of learning. To demonstrate learning, you must be able to complete tasks on your own. Master Machine Learning with Python comes with many exercises .
The Python data science libraries are in a state of flux with new additions added and other parts deprecated. Ted is very quick to update the course to reflect the newest changes to the libraries. This course will be continuously updated through at least 2020. You will have lifetime access to all the updates.
The course is divided into the following parts:
Models are anything that help us represent the real world. You will learn how machine learning models are a subset of mathematical models that learn from data.
You will learn how to model data with linear regression, a simple and practical model that is a useful starting point before diving into more advanced models. You will learn how the parameters of the model are fit and understand what it means to minimize squared error.
The details of several more supervised learning models are covered such as K-Nearest Neighbors, Decision Trees, and Random Forests. You'll learn how to build both K-Nearest Neighbors and Decision Trees from scratch directly using Python.
The goal of machine learning is to build a model that provides us intelligence on data that it has yet to encounter. You will learn how to determine how well a model is likely to perform on unseen data by properly evaluating it with techniques such as cross validation.
You will learn how to choose 'better' models by selecting combinations of hyper-parameter values that yield good cross-validated results.
We take a step back to learn how to transform our data before machine learning so that we can build a better model. You will learn about different transformations such as filling in missing values and standardizing features.
Additionally, within part 6, you will learn how to build machine learning pipelines that can handle both data transformations and machine learning all with a single estimator.
Before getting started analyzing data, you will learn how to setup a robust environment on your system to do data science. You will install the Miniconda distribution along with all the data science libraries used throughout the course.
You will also learn how to best use Jupyter Notebooks, our main tool for exploring data.
This course is taught by Ted Petrou, an expert at Python, data exploration and machine learning. Ted is the author of the highly rated text Pandas Cookbook. Ted has taught hundreds of students Python and data science during in-person classroom settings. He sees first hand exactly where students struggle and continually upgrades his material to minimize these struggles by providing simple and direct paths forward.
Ted is one of the foremost authorities on using the pandas library to do data analysis. His blog posts have totaled well over 1 million views. He is also a prolific contributor on Stack Overflow having answered over 400 questions.
Ted holds a master's degree in statistics from Rice University and is the author of Exercise Python, Master Data Analysis with Python and Master Machine Learning with Python.
You are purchasing a digital download which includes the following:
"in my opinion what distinguishes you from everyone is your deep understanding of Python and Pandas. I follow lot of people on twitter, linkedin, Medium who share tips/tricks/codes on Python, Pandas, scikit-learn but no one comes close to you when writing efficient code and explaining the finer nuances"
Master Machine Learning with Python assumes you already have a solid understanding of the fundamentals of Python. If you do not, you should master these fundamentals first. Exercise Python provides the necessary prerequisite knowledge.