Predicting Bitcoin's Price with ML and Twitter Inputs

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Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Machine Learning Basics

Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.

World of Data: Machine Learning and Value Chain

Delves into machine learning, data insights, and commercial value in the digital world.

Machine learning: Physics and Data

Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.

Predicting New Product Life Cycles: Machine Learning Approach

Explores machine learning for predicting new product life cycles and the challenges of limited historical data.

Introduction to Data Science

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Overfitting: Symptoms and Characteristics

Explores overfitting in polynomial regression, emphasizing the importance of generalization in machine learning and statistics.

Machine Learning: Supervised and Unsupervised Learning Techniques

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Deep Learning: Data, Models, and Challenges

Provides an overview of deep learning concepts, focusing on data, model architecture, and challenges in handling large datasets.

Data Mining: Introduction

Covers the challenges and opportunities of data mining, practical questions, algorithm components, and applications like shopping basket analysis.