Artificial intelligence and machine learning are defining features of the 21st century and are quickly becoming a key factor in gaining and maintaining competitive advantage in each industry which incorporates them.
This 5-day class provides the skills needed by scientists, engineers, data scientists, data analysts, and business intelligence experts to use Python and machine learning for their data mining, classification, and predictive modeling tasks. This highly interactive training will empower your team with the skills they need to build reliable, repeatable analyses and prediction workflows. After this class, they will be able to significantly increase the amount of data they can process, thanks to automation, and speed up the classification, interpretation, and analysis of data.
In this course we combine conceptual knowledge of machine learning with extensive experience applying it to real-world data. Your team will develop skills in applying Python’s machine learning tools, such as the scikit-learn package, to make predictions about complicated phenomena by leveraging the information contained in numerical data, natural language, images, and discrete categories.
The emphasis is on learning techniques to maximize the predictive performance of machine learning workflows. After building a solid foundation in the Python scientific stack, we focus on the different types of feature sources for machine learning. For each, we progress through a short introductory lecture followed by exercises of progressive difficulty. Intermingled with the machine learning material are short discussions of helpful and diagnostic data visualizations.
Fill out the form below to have Enthought’s full set of 3 Machine Learning with Scikit-Learn cheat sheets sent directly to you (free!).
Enthought instructors have doctorates in scientific fields such as physics, engineering, computer science, and mathematics, and all have extensive experience through research and consulting in applying Python to solve complex problems across a range of industries, allowing them to bring their real world experience to the classroom every day. Enthought instructors possess professional, first-hand experience with the tools and technologies covered in our courses.
Experience with Python is helpful (but not required). However, programming experience in some language (such as R, MATLAB, SAS, Mathematica, Java, C, C++, VB, or FORTRAN) is expected. In particular, participants need to be comfortable with general programming concepts like variables, loops, and functions.
We kick off the class by exploring the functionality of the IPython Shell, an enhanced interactive science-centric console. Next we review the Jupyter Notebook, a web-based application that mixes code, plots, and rich media, making it ideal for sharing and publishing analyses with peers. You’ll leave with a mastery of tools that will accelerate your productivity and facilitate collaboration.
Building a Solid Infrastructure to Go From Exploratory Analysis to Reproducible Workflows
Next we move into an introduction to Python’s core language features that form part of your universal toolkit for tasks ranging from initial data exploration to extensible application development. We’ll introduce Python’s built-in data structures, including how and where each might be used and what trade-offs are present, and we’ll cover Python’s looping and control flow constructs. Along the way we’ll provide insight into Python’s design choices that will help you understand why Python works the way it does.
NumPy is a tool for rapidly manipulating and processing large data sets. Whether you have a team of scientists writing scripts to analyze and plot analytical results or analysts writing large-scale quantitative finance applications for Wall Street, NumPy is a critical tool.
Then, we use Matplotlib, a versatile 2D plotting library, to generate publication-quality with just a few lines of code.
We do a deep dive into the Python Data Analysis Library (Pandas), a powerful package for working with tabular data. Pandas’ powerful data aggregation and reorganization capabilities, including support for labeling data along each dimension, missing values, and time series manipulations, have made Python an indispensable tool for data exploration and analysis.
We start with a short conceptual introduction to machine learning. We demystify what it’s all about, explain how it works, and what kinds of problems it’s best suited to solve. Next, we cover the frameworks and tools provided by scikit-learn, a widely used library for machine learning. Then, we focus on the best practices for extracting useful information from various features sources.
Onsite corporate classes are also available. Discounts are available for 3+ attendees and academics currently at a degree-granting institution. Contact us with the form to the right to learn more.
|Where||When||Price (per person)||Register|
|Contact us with the form to the right to request a private onsite class, or an open class in your area.|
Note: The 3 day Machine Learning Mastery Workshop is an alternative course for those who already have both (1) current working knowledge of programming in the Python standard language (data structures, control flow, assignment, functions, and package access) and (2) familiarity with array programming in NumPy.
*each box represents ~1 day of content
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