Machine Learning for Software Developers

12/12/2021 - Morning

MLSD - Version:1

Description

Machine learning expands the boundaries of what's possible by letting software accomplish tasks that can't be accomplished algorithmically. In this hands-on workshop, learn the fundamentals of machine learning from a software developer's perspective and learn how to use it to build and operationalize sophisticated predictive models. You’ll learn about clustering, regression, classification, Support Vector Machines, and more, and you’ll do it with Scikit-learn, the world’s most popular machine-learning library. You'll also see examples of machine learning in action, train models of your own to perform tasks such as facial recognition, and go away with lots of sample code to kick-start your next project.

Intended audience

Software Developers

Topics
Prerequisites
Objectives

▼Expand All

Introduction to Machine Learning
- Learn what machine learning is, what types of machine-learning models there are, and how to use Scikit-learn to build simple unsupervised- and supervised-learning models using algorithms such as k-means clustering and k-nearest neighbors. Also learn the basics of data cleaning and preparation.
Regression
- Learn how to build supervised-learning models that use regression algorithms to predict numeric values such as how long a flight will be delayed or how much a house might sell for. Also learn how to score regression models for accuracy, how to handle categorical values in datasets, and about popular regression algorithms such as linear regression, random forests, and gradient-boosting machines (GBMs).
Binary Classification
- Learn how to build classification models that predict binary outcomes such as whether a flight will or will not arrive on time. Also learn about precision, recall, confusion matrices, and other metrics used to score binary-classification models, and how to build machine-learning models around textual data – for example, models that predict whether an e-mail is “spam” or “not spam” and models that analyze text for sentiment.
Multiclass Classification
- Learn how to build classification models that predict non-binary outcomes such as what character a hand-written digit represents or what category a document belongs to. Also take a deep dive into Support Vector Machines (SVMs), hyperparameter tuning, and data normalization, and put your skills to work building a facial-recognition model.