Udemy - Applications of Machine Learning in Trading

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[ FreeCryptoLearn.com ] Udemy - Applications of Machine Learning in Trading
  • Get Bonus Crypto Downloads Here.url (0.2 KB)
  • ~Get Your Files Here ! 1. Course Introduction
    • 1. Course Introduction.mp4 (31.5 MB)
    • 1.1 AI Pioneers in Investment Management.pdf (1.2 MB)
    2. Introduction
    • 1. Introduction to Machine Learning.mp4 (300.3 MB)
    • 1.1 Intro to Machine Learning Article - Annotated notes.pdf (1.5 MB)
    • 1.2 Introduction to Machine Learning-Slides-compressed.pdf (3.5 MB)
    • 2. Lecture 2 Data Handling and Preprocessing.mp4 (190.1 MB)
    • 2.1 Data Handling and Preprocessing -Slides.pdf (12.0 MB)
    • 3. Introduction to Machine Learning Quiz.html (0.2 KB)
    • 4. Data handling and preprocessing Quiz.html (0.2 KB)
    3. Generating Signals for Quant Models with Machine Learning
    • 1. Case Study On Separating Noise from News.mp4 (295.8 MB)
    • 1.1 Leveraging machine learning to separate noise from news - Slides.pdf (7.2 MB)
    • 1.2 Noise from News - annotated paper.pdf (1.1 MB)
    • 2. Assessment of Trading Signal Quality.mp4 (165.5 MB)
    • 2.1 Assessment of Trading Signal Quality - Slides.pdf (13.4 MB)
    • 3. Generating Trading Signals by ML algorithms or time series ones.mp4 (212.3 MB)
    • 3.1 Generating Trading Signals by ML Algorithms or Time Series -Slides.pdf (14.1 MB)
    • 3.2 Time Series vs ML - Annotated Paper.pdf (1.9 MB)
    4. Refining Equity Trading Volume Prediction with Deep Learning
    • 1. Introduction to Volume Prediction.mp4 (138.8 MB)
    • 1.1 Analysis of trading volume - Slides.pdf (6.5 MB)
    • 2. Volume Prediction with Neural Networks.mp4 (231.0 MB)
    • 2.1 Volume Prediction with neural networks- Slides.pdf (5.5 MB)
    • 2.2 Volume predictions with NN - annotated.pdf (527.0 KB)
    • 3. Stock Trading Volume Prediction with Dual-Process Meta Learning.mp4 (213.5 MB)
    • 3.1 Stock Trading Volume Prediction with Dual-Process Meta-Learning - Slides.pdf (4.0 MB)
    • 3.2 Volume Prediction with Meta Learning - Annotated.pdf (343.4 KB)
    5. Sentiment Analysis
    • 1. Introduction to Sentiment Analysis.mp4 (208.6 MB)
    • 1.1 Intro to Sentiment Analysis - Slides.pdf (10.8 MB)
    • 2. Harnessing News Sentiment.mp4 (220.3 MB)
    • 2.1 Harnessing News Sentiment for FX Futures Strategies - Annotated notes.pdf (4.8 MB)
    • 2.2 Harnessing News Sentiment for FX Futures Strategies - Slides.pdf (5.8 MB)
    • 3. Bloomberg News Sentiment Case Study.mp4 (144.6 MB)
    • 3.1 Alternative Data Case Study Bloomberg News - Annotated.pdf (1.1 MB)
    • 3.2 Bloomberg News & Social Sentiment Data - Slides (1).pdf (11.5 MB)
    6. Leveraging AIAlternative Data Analysis
    • 1. AI in Finance Introduction.mp4 (198.6 MB)
    • 1.1 A Survey on Alternative Data in Finance and Business.pdf (1.4 MB)
    • 2. Statistical arbitrage powered by Explainable Artificial Intelligence.mp4 (416.0 MB)
    • 2.1 Statistical arbitrage powered by Explainable Artificial Intelligence - Annotated.pdf (4.9 MB)
    • 2.2 Statistical arbitrage powered by Explainable Artificial Intelligence-Slides.pdf (5.6 MB)
    • Bonus Resources.txt (0.4 KB)

Description

Applications of Machine Learning in Trading

https://FreeCryptoLearn.com

Published 2/2024
Created by Hudson and Thames Quantitative Research
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 14 Lectures ( 4h 41m ) | Size: 3 GB

A case study approach

What you'll learn:
Understand Core Machine Learning Concepts and Theories
gain a comprehensive understanding of the mathematical principles underlying machine learning algorithms
Explore the Frontiers of Machine Learning Theory: Students will engage with the latest research and theoretical advancements in machine learning
Ethical Considerations and Theoretical Limitations

Requirements:
Statistics and Calculus background will be beneficial but not necessary
No Machine Learning knowledge needed



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Udemy - Applications of Machine Learning in Trading


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3 GB
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Udemy - Applications of Machine Learning in Trading


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