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Guest Lecturer Video Chapter 8

Prof: John Schuler chapter 8

Introduction

Introductory Video

6.1 What Is Machine Learning?

Lecture Video 1

Supervised vs. Unsupervised Learning

Training and Testing a Model

Overfitting and Underfitting

6.2 Classification Using Machine Learning

Lecture Video 2

Logistic Regression

k-Means Clustering

Density-Based Clustering (DBScan)

The Confusion Matrix

6.3 Machine Learning in Regression Analysis

Lecture Video 3

Linear Regression and Bootstrapping

Multiple Regression Techniques

6.4 Decision Trees

Lecture Video 4

Information and Entropy

Building a Decision Tree

Using a Decision Tree to Predict

6.5 Other Machine Learning Techniques

Lecture Video 5

Random Forests

Multinomial Naïve Bayes Classifiers

Gaussian Naïve Bayes for Continuous Probabilities

Working with Big Data

Knowledge Check

Welcome to 'Machine Learning for Data-Driven Decision Making,' a comprehensive course designed to bridge the gap between raw data and strategic business choices. In today's highly competitive landscape, organizations rely on data science to drive growth, optimize operations, and predict future trends.

Welcome to 'Machine Learning for Data-Driven Decision Making,' a comprehensive course designed to bridge the gap between raw data and strategic business choices. In today's highly competitive landscape, organizations rely on data science to drive growth, optimize operations, and predict future trends. This course demystifies machine learning, equipping you with the foundational knowledge and practical skills needed to leverage algorithms for actionable insights. Whether you are a business analyst, an aspiring data scientist, or a manager looking to harness the power of AI, this course provides a clear, structured pathway to mastering machine learning concepts. You will start with the basics, exploring the differences between supervised and unsupervised learning, and understanding crucial concepts like model training, testing, overfitting, and underfitting. From there, we dive deep into classification techniques such as Logistic Regression, k-Means, and DBScan clustering, alongside evaluating models using the Confusion Matrix. The curriculum also covers regression analysis, decision trees, random forests, and Naïve Bayes classifiers, ensuring you have a robust toolkit for various data challenges. Finally, you will learn how to scale these techniques for Big Data environments. By the end of this course, you will possess the confidence to implement machine learning models that transform complex datasets into clear, data-driven decisions.

Chapter 8: Machine Learning for Data-Driven Decision Making

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            <p>In today's rapidly evolving digital landscape, the ability to extract actionable insights from vast amounts of information is a critical asset. Machine Learning for Data-Driven Decision Making is designed to bridge the gap between raw data and strategic choices by equipping you with the foundational and advanced techniques of artificial intelligence. Rather than relying on intuition, modern problem-solving demands robust analytical frameworks. This course delves into the core mechanics of how algorithms learn from data, enabling you to build predictive models, uncover hidden patterns, and ultimately drive smarter, evidence-based decisions in any professional context.</p>

<p>Throughout this comprehensive journey, you will explore a wide spectrum of machine learning methodologies, starting with the essential distinctions between supervised and unsupervised learning, model training, and the critical balance of overfitting and underfitting. As you progress, the curriculum provides deep dives into practical applications, including classification techniques like logistic regression and DBScan clustering, as well as rigorous linear and multiple regression analysis. You will also master the construction and application of decision trees using information and entropy, evaluate model performance using confusion matrices, and explore powerful ensemble and probabilistic methods such as Random Forests and Naïve Bayes classifiers. Finally, the course addresses the unique challenges of applying these machine learning techniques to Big Data, ensuring you are fully prepared to tackle real-world, large-scale analytical challenges.</p>
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