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Ensemble Machine Learning Cookbook

  • Course Code: Data Science - Ensemble Machine Learning Cookbook
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 3 Days Audience: This course is geared for those who wants to Implement machine learning algorithms to build ensemble models using Keras, H2O, Scikit-Learn, Pandas and more.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Ensemble Machine Learning Cookbook skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Implement machine learning algorithms to build ensemble models using Keras, H2O, Scikit-Learn, Pandas and more. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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 Cookbook 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 

Topics Covered: 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 

Audience & Pre-Requisites 

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. 

Course Agenda / Topics 

  1. Get Closer to Your Data 
  • Get Closer to Your Data 
  • Introduction 
  • Data manipulation with Python 
  • Analyzing, visualizing, and treating missing values 
  • Exploratory data analysis 
  1. Getting Started with Ensemble Machine Learning 
  • Getting Started with Ensemble Machine Learning 
  • Introduction to ensemble machine learning 
  • Max-voting 
  • Averaging 
  • Weighted averaging 
  1. Resampling Methods 
  • Resampling Methods 
  • Introduction to sampling 
  • k-fold and leave-one-out cross-validation 
  • Bootstrapping 
  1. Statistical and Machine Learning Algorithms 
  • Statistical and Machine Learning Algorithms 
  • Technical requirements 
  • Multiple linear regression 
  • Logistic regression 
  • Naive Bayes 
  • Decision trees 
  • Support vector machines 
  1. Bag the Models with Bagging 
  • Bag the Models with Bagging 
  • Introduction 
  • Bootstrap aggregation 
  • Ensemble meta-estimators 
  • Bagging regressors 
  1. When in Doubt, Use Random Forests 
  • When in Doubt, Use Random Forests 
  • Introduction to random forests 
  • Implementing a random forest for predicting credit card defaults using scikit-learn 
  • Implementing random forest for predicting credit card defaults using H2O 
  1. Boosting Model Performance with Boosting 
  • Boosting Model Performance with Boosting 
  • Introduction to boosting 
  • Implementing AdaBoost for disease risk prediction using scikit-learn 
  • Implementing a gradient boosting machine for disease risk prediction using scikit-learn 
  • Implementing the extreme gradient boosting method for glass identification using XGBoost with scikit-learn  
  1. Blend It with Stacking 
  • Blend It with Stacking 
  • Technical requirements 
  • Understanding stacked generalization 
  • Implementing stacked generalization by combining predictions 
  • Implementing stacked generalization for campaign outcome prediction using H2O 
  1. Homogeneous Ensembles Using Keras 
  • Homogeneous Ensembles Using Keras 
  • Introduction 
  • An ensemble of homogeneous models for energy prediction 
  • An ensemble of homogeneous models for handwritten digit classification 
  1. Heterogeneous Ensemble Classifiers Using H2O 
  • Heterogeneous Ensemble Classifiers Using H2O 
  • Introduction  
  • Predicting credit card defaulters using heterogeneous ensemble classifiers 
  1. Heterogeneous Ensemble for Text Classification Using NLP 
  • Heterogeneous Ensemble for Text Classification Using NLP 
  • Introduction 
  • Spam filtering using an ensemble of heterogeneous algorithms 
  • Sentiment analysis of movie reviews using an ensemble model 
  1. Homogenous Ensemble for Multiclass Classification Using Keras 
  • Homogenous Ensemble for Multiclass Classification Using Keras 
  • Introduction 
  • An ensemble of homogeneous models to classify fashion products 
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