Name: JPMC Machine Learning Essentials 2
Rating: 4 (1 reviews)

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Updated

July 6, 2022

Are you aware that more data has been created in the past two years than in the rest of human history...combined? That’s why machine learning models that find patterns in data and make decisions are so important. Learn how to build machine learning models with Python.

Ernesto Lee

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The Deck is Stacked Against Developers

Machine learning is often taught by academics, for academics.

That’s why most material is so dry and math-heavy.

Developers need to know what works and how to use it.

We need less math and more practical hands-on examples with working code.

Machine Learning Essentials is for Developers ….with a little Background in Machine Learning …and LOTS of Interest in Making Accurate Predictions and Delivering Results!

We have carefully designed this camp for developers that already know a little background in python and machine learning (Bootcamp 1) and who are interested in discovering how to make accurate predictions and deliver results with machine learning in the Python ecosystem.

Introducing your guide to applied machine learning with Python.

You will discover the step-by-step process that you can use to get started and become good at machine learning for predictive modeling with the Python ecosystem including:

Python 3.6+
SciPy
NumPy
Matplotlib
Pandas
Scikit-Learn

This camp will lead you from being a developer who is interested in machine learning with Python to a developer who has the resources and capability to work through a new dataset end-to-end using Python and develop accurate predictive models.

After completing this camp, you will know…

How to deliver a model that can make accurate predictions on new unseen data.
How to complete all subtasks of a predictive modeling problem with Python.
How to learn new and different techniques in Python and SciPy.
How to work through a small to medium sized dataset end-to-end.
How to get help with Python machine learning.
You will know which Python modules, classes and functions to use for common machine learning tasks.

From here you can start to dive into the specifics of the functions, techniques and algorithms used with the goal of learning how to use them better in order to deliver more accurate predictive models, more reliably in less time.