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Ensemble Learning with Python

  • Course Code: Artificial Intelligence - Ensemble Learning with Python
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 3 Days Audience: This course is geared for those who wants to Combine popular machine learning techniques to create ensemble models using Python.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Ensemble Learning with Python skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Combine popular machine learning techniques to create ensemble models using Python. 
  • 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, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This course will demonstrate how you can use a variety of weak algorithms to make a strong predictive model. With its hands-on approach, you’ll not only get up to speed with the basic theory but also the application of different ensemble learning techniques. Using examples and real-world datasets, you’ll be able to produce better machine learning models to solve supervised learning problems such as classification and regression. In addition to this, you’ll go on to leverage ensemble learning techniques such as clustering to produce unsupervised machine learning models. As you progress, the lessons will cover different machine learning algorithms that are widely used in the practical world to make predictions and classifications. You’ll even get to grips with the use of Python libraries such as scikit-learn and Keras for implementing different ensemble models. By the end of this course, you will be well-versed in ensemble learning, and have the skills you need to understand which ensemble method is required for which problem, and successfully implement them in real-world scenarios. 

Working in a hands-on learning environment, led by our Python expert instructor, students will learn about and explore: 

  • Implement ensemble models using algorithms such as random forests and AdaBoost 
  • Apply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model 
  • Explore real-world data sets and practical examples coded in scikit-learn and Keras 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Implement ensemble methods to generate models with high accuracy 
  • Overcome challenges such as bias and variance 
  • Explore machine learning algorithms to evaluate model performance 
  • Understand how to construct, evaluate, and apply ensemble models 
  • Analyze tweets in real time using Twitter’s streaming API 
  • Use Keras to build an ensemble of neural networks for the MovieLens dataset 

Audience & Pre-Requisites 

This course is geared for attendees wants to Combine popular machine learning techniques to create ensemble models using Python 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills and Python programming knowledge 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. A Machine Learning Refresher 
  • A Machine Learning Refresher 
  • Technical requirements 
  • Learning from data 
  • Supervised and unsupervised learning 
  • Performance measures 
  • Machine learning algorithms 
  1. Getting Started with Ensemble Learning 
  • Getting Started with Ensemble Learning 
  • Technical requirements 
  • Bias, variance, and the trade-off 
  • Ensemble learning 
  • Difficulties in ensemble learning 
  1. Voting 
  • Voting 
  • Technical requirements 
  • Hard and soft voting 
  • Python implementation 
  • Using scikit-learn 
  1. Stacking 
  • Stacking 
  • Technical requirements 
  • Meta-learning 
  • Deciding on an ensemble’s composition 
  • Python implementation 
  1. Bagging 
  • Bagging 
  • Technical requirements 
  • Bootstrapping 
  • Bagging 
  • Python implementation 
  • Using scikit-learn  
  1. Boosting 
  • Boosting 
  • Technical requirements 
  • AdaBoost 
  • Gradient boosting 
  • Using scikit-learn 
  • XGBoost 
  1. Random Forests 
  • Random Forests 
  • Technical requirements 
  • Understanding random forest trees 
  • Creating forests 
  • Using scikit-learn 
  1. Clustering 
  • Clustering 
  • Technical requirements 
  • Consensus clustering 
  • Using OpenEnsembles 
  1. Classifying Fraudulent Transactions 
  • Classifying Fraudulent Transactions 
  • Technical requirements 
  • Getting familiar with the dataset 
  • Exploratory analysis 
  • Voting 
  • Stacking 
  • Bagging 
  • Boosting 
  • Using random forests 
  • Comparative analysis of ensembles 
  1. Predicting Bitcoin Prices 
  • Predicting Bitcoin Prices 
  • Technical requirements 
  • Time series data 
  • Voting 
  • Stacking 
  • Bagging 
  • Boosting 
  • Random forests 
  1. Evaluating Sentiment on Twitter 
  • Evaluating Sentiment on Twitter 
  • Technical requirements 
  • Sentiment analysis tools  
  • Getting Twitter data 
  • Creating a model 
  • Classifying tweets in real time 
  1. Recommending Movies with Keras 
  • Recommending Movies with Keras 
  • Technical requirements 
  • Demystifying recommendation systems 
  • Neural recommendation systems 
  • Using Keras for movie recommendations 
  1. Clustering World Happiness 
  • Clustering World Happiness 
  • Technical requirements 
  • Understanding the World Happiness Report 
  • Creating the ensemble 
  • Gaining insights 
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