Supervised Learning - Ensemble Models 2023

Ensemble Techniques in Data Science

What you'll learn

The theoretical foundations of ensemble learning, including the concepts of bias, variance, and ensemble diversity.

Different types of ensemble methods, such as bagging, boosting, and stacking, and how they can be applied to improve model performance.

Techniques for combining individual models, including averaging, weighted averaging, and meta-learning.

Practical implementation of ensemble methods using popular machine learning libraries and frameworks, along with hands-on experience in building ensemble models

Requirements

A basic understanding of individual machine learning algorithms, such as decision trees, random forests, and gradient boosting.

Familiarity with the concept of model bias and variance trade-off.

Knowledge of evaluation metrics used to assess model performance, such as accuracy, precision, recall, and F1 score.

Awareness of ensemble methods, including bagging, boosting, and stacking, and their respective advantages and limitations.

Description

Ensemble techniques play a pivotal role in improving the accuracy and robustness of predictive models in data science. This intermediate-to-advanced level course is designed to provide a comprehensive understanding of ensemble methods and equip participants with the knowledge and skills needed to effectively apply ensemble techniques in real-world scenarios. Through a combination of theoretical concepts, practical implementation, and hands-on projects, participants will explore various ensemble methods and gain insights into their applications, strengths, and limitations.Course Objectives:1. Understand the Fundamentals of Ensemble Techniques:- Gain an in-depth understanding of ensemble methods and their importance in data science.- Learn about the intuition behind ensemble techniques and their advantages over individual models.2. Study Bagging and Random Forest:- Examine bagging as an ensemble technique, including its underlying principles and algorithmic implementation.- Dive into Random Forest, a popular bagging-based ensemble method, and learn how it improves model performance.3. Explore Boosting Algorithms:- Learn about boosting algorithms, such as AdaBoost, Gradient Boosting, and XGBoost, and their iterative nature.- Understand the boosting process, including weak learner selection, weight adjustments, and error correction.4. Master Stacking Techniques:- Study the concept of stacking, also known as stacked generalization, and its role in combining multiple models.- Explore various stacking architectures, including blending and meta-model approaches.5. Model Aggregation and Voting:- Discover different methods of aggregating ensemble predictions, such as majority voting and weighted voting.- Explore advanced ensemble techniques like stacking with meta-features and stacking with model pruning.6. Practical Implementation and Case Studies:- Apply ensemble techniques to real-world datasets and problems.- Work on hands-on projects to gain practical experience in implementing ensemble methods using Python/R and relevant libraries.7. Advanced Topics and Recent Developments:- Gain insights into advanced ensemble techniques, including gradient boosting variants like LightGBM and CATBoost.- Explore recent research and developments in ensemble methods, such as deep learning ensembles.8. Ethical Considerations and Best Practices:- Discuss ethical considerations surrounding ensemble techniques, including biases, fairness, and interpretability.- Learn best practices for applying ensemble techniques responsibly and effectively.This course combines lectures, hands-on exercises, and practical projects to provide a comprehensive learning experience. Participants will have access to a dedicated online learning platform where they can access course materials, video lectures, and supplementary resources. Live sessions and discussion forums will foster interaction, collaboration, and the opportunity to seek clarification and guidance from instructors and peers. Participants will have the opportunity to work on real-world case studies and projects, applying ensemble techniques to solve data-driven problems and gain practical insights.Assessment and Certification:Participants will be assessed based on their performance in assignments, quizzes, and project submissions throughout the course. Successful completion of the course, including meeting the assessment criteria, will earn participants a certificate of completion. This certificate can be used to showcase their proficiency in ensemble techniques and their ability to apply them in practical settings.

Overview

Section 1: Introduction

Lecture 1 Introduction about Tutor

Section 2: Introduction about Basic Terminology

Lecture 2 Agenda and stages of Analytics

Lecture 3 What is Diagnoistic Analytics?

Lecture 4 What is Predictive Analytics ?

Lecture 5 What is Prescriptive Analytics?

Lecture 6 What is CRISP-ML(Q)

Section 3: Business Understanding Phase

Lecture 7 Business Understanding - Define Scope Of Application

Lecture 8 Business Understanding - Define Success Criteria

Lecture 9 Business Understanding - Use Cases

Section 4: Data Understanding Phase - Data Types

Lecture 10 Agenda Data Understanding

Lecture 11 Introduction to Data Understanding ?

Lecture 12 Data Types - Continuous Vs Discrete

Lecture 13 Categorical Data Vs Count Data

Lecture 14 Pratical Data Understanding using Realtime Example

Lecture 15 Scale of Measurement

Lecture 16 Quantitave Vs Qualitative

Lecture 17 Structure Vs Unstructured Data

Section 5: Data Understanding Phase - Data Collection

Lecture 18 What is Data Collection?

Lecture 19 Understanding Primary Data Sources

Lecture 20 Understanding Secondary Data Sources

Lecture 21 Understanding Data Collection Using Survey

Lecture 22 Understanding Data Collection Using DoE

Lecture 23 Understanding possible errors in Data Collection Stage

Lecture 24 Understanding Bias and Fairness

Section 6: Understanding Basic Statistics

Lecture 25 Introduction to CRISP-ML(Q) Data preparation & Agenda

Lecture 26 What is Probability?

Lecture 27 What is Random Variable?

Lecture 28 Understanding Probability and its Application,Probabiity Discussion

Section 7: Data Preparation Phase - Exploratory Data Analysis (EDA)

Lecture 29 Understanding Normal Distribution

Lecture 30 What is Inferential Statistics?

Lecture 31 Understanding Standard Normal Distribution & what is Z Scores?

Lecture 32 Understanding Measures of central tendency ( First moment business decision)

Lecture 33 Understanding Measures of Dispersion ( Second moment business decision)

Lecture 34 Understanding Box Plot(Diff B-w Percentile and Quantile and Quartile)

Lecture 35 Understanding Graphical Techniques-Q-Q-Plot

Lecture 36 Understanding about Bivariate Scatter Plot

Section 8: Python Installation and Setup

Lecture 37 Python Installation

Lecture 38 Anakonda Installation

Lecture 39 Understand about Anakonda Navigator, Spyder & Python Libraries

Lecture 40 Understanding about Jupyter and Google Colab

Section 9: Data Preparation Phase | Data Cleansing- Type Casting

Lecture 41 Recap Of Concepts

Lecture 42 Understanding Data Cleansing Typecasting

Lecture 43 Understanding Data Cleansing Typecasting Using Python

Section 10: Data Preparation Phase | Data Cleansing- Handling Duplicates

Lecture 44 Understanding Handling Duplicates

Lecture 45 Understanding Handling Duplicates using Python

Section 11: Data Preparation Phase | Data Cleansing-Outlier Analysis Treatment

Lecture 46 Understanding Outlier Analysis Treatment

Lecture 47 Understanding Outlier Analysis Treatment using Python

Section 12: Data Mining - Clustering / Segmentation using Python

Lecture 48 Overview Of Clustering / Segmentation

Lecture 49 Distance Between Clusters

Lecture 50 Hierarchical Clustering Process

Lecture 51 Learning Clustering Using Python

Section 13: Dimension Reduction Techniques

Lecture 52 About Dimension Reduction & its Applications

Section 14: Network Analysis

Lecture 53 Elements of a Network

Lecture 54 About Google PageRank Algorithm

Lecture 55 Network Based Similarity Metrics

Lecture 56 Network related Properties

Section 15: Traditional ML Models - Naive Bayes

Lecture 57 Introduction to Naive Baye

Lecture 58 Use Cases of Naive Bayes

Section 16: K Nearest Neighbors (KNN) Models

Lecture 59 Introduction to K Nearest Neighbors and it's Use Case

Section 17: Decision Tree

Lecture 60 About Decision Tree and it's Use Case.

Section 18: Introduction about Ensemble Model

Lecture 61 Overview about the Ensemble Model

Section 19: About Stacking

Lecture 62 What is Stacking?

Lecture 63 Let's know more in detail about Stacking and learn where it can be used.

Section 20: About Bagging

Lecture 64 What is Bagging and how it can be used.

Section 21: About Boosting

Lecture 65 Introduction about Boosting

Lecture 66 Let's know more in detail about Boosting

This ensemble techniques course is designed for data scientists, machine learning engineers, and researchers who want to enhance their understanding and skills in ensemble learning methods for improving model performance.,This course can benefit professionals working in various domains such as finance, healthcare, e-commerce, and marketing, where accurate predictions and reliable models are crucial.,It is also suitable for individuals with a background in statistics or mathematics who want to delve into the field of machine learning and explore advanced techniques for building robust predictive models.