Ensemble Machine Learning with Real Use Cases

This is a high-level list of topics covered in this course. Please see the detailed Agenda below • Understand how to use machine learning algorithms for regression and classification problems • Implement ensemble techniques such as averaging, weighted averaging, and max-voting • Get to grips with advanced ensemble methods, such as bootstrapping, bagging, and stacking • Use Random Forest for tasks such as classification and regression • Implement an ensemble of homogeneous and heterogeneous machine-learning algorithms • Learn and implement various boosting techniques, such as AdaBoost, Gradient Boosting Machine, and XGBoost

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Hours :

Hands on Labs

Requirements

  • This course is designed for developers wants to Implement machine learning algorithms to build ensemble models using Keras, H2O, Scikit-Learn, Pandas and more Pre-Requisites: Students should have familiar with • Basics of Python and ML • Knowledge of Python is assumed.

Description

Ensemble modeling is an approach used to improve the performance of machine learning models. It combines two or more similar or dissimilar machine learning algorithms to deliver superior intellectual powers. This course will help you to implement popular machine learning algorithms to cover different paradigms of ensemble machine learning such as boosting, bagging, and stacking. The Ensemble Machine Learning Cookbook will start by getting you acquainted with the basics of ensemble techniques and exploratory data analysis. You'll then learn to implement tasks related to statistical and machine learning algorithms to understand the ensemble of multiple heterogeneous algorithms. It will also ensure that you don't miss out on key topics, such as like resampling methods. As you progress, you’ll get a better understanding of bagging, boosting, stacking, and working with the Random Forest algorithm using real-world examples. The course will highlight how these ensemble methods use multiple models to improve machine learning results, as compared to a single model. In the concluding lessons, you'll delve into advanced ensemble models using neural networks, natural language processing, and more. You’ll also be able to implement models such as fraud detection, text categorization, and sentiment analysis. By the end of this course, you'll be able to harness ensemble techniques and the working mechanisms of machine learning algorithms to build intelligent models using individual recipes. Working in a hands-on learning environment, led by our Ensemble Machine Learning with Real Use Cases expert instructor, students will learn about and explore: • Apply popular machine learning algorithms using a recipe-based approach • Implement boosting, bagging, and stacking ensemble methods to improve machine learning models • Discover real-world ensemble applications and encounter complex challenges in Kaggle competitions

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About the Instructor

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About the Instructor

Ernesto Lee is an impassioned blockchain entrepreneur and technologist from the Miami/Fort Lauderdale Area. For over 25 years Ernesto has been asking hard questions and pursuing tough answers. Ernesto was an original founding member, co-owner and the CTO of Blockchain Training Alliance and former Chief Solutions Architect at TechBlue.com. Presently, Ernesto is the CEO at Ernesto.Net and Engineer at Kaiser Permanente.  Ernesto’s career illustrates a lifelong commitment to pushing the envelope on innovation and growing opportunities for all around him.

As a graduate of Old Dominion University (BS, Physics), Virginia Tech (MS, Software Engineering), and Harvard Extension School (Graduate Certificate, Business Communication), Ernesto has always had a passion for technology and teaching. It has been a cornerstone of Ernesto’s career.