Trading, Machine Learning & GCP - Introduction
Students can use Google Cloud Platform to build machine learning models as part of this course, which covers the basics of trading, quantitative strategies, and machine learning uses in finance.
Description for Trading, Machine Learning & GCP - Introduction
Principles of Trading: Comprehend the fundamental principles of trading, such as trend, returns, stop-loss, and volatility, to establish a basis for more sophisticated subjects.
Quantitative Trading Approaches: Explore many quantitative trading strategies and their frameworks for identifying profit sources.
Arbitrage and Statistical Methods: Examine the fundamentals of exchange, statistical, and index arbitrage, encompassing the essential procedures involved in these techniques.
Financial Applications of Machine Learning: Acquire hands-on expertise in applying machine learning methodologies to financial applications, including the development and execution of backtesting frameworks utilizing Google Cloud Platform.
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 1
Offered by: On Coursera provided by New York Institute of Finance & Google Cloud
Duration: 9 hours (approximately)
Schedule: Flexible
Pricing for Trading, Machine Learning & GCP - Introduction
Use Cases for Trading, Machine Learning & GCP - Introduction
FAQs for Trading, Machine Learning & GCP - Introduction
Reviews for Trading, Machine Learning & GCP - Introduction
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Trading, Machine Learning & GCP - Introduction
The Essay Generator, along with Exam and ABCs Generators, leverages OpenAI's GPT-3 to swiftly produce high-quality content, supported by ongoing updates and accessible support from the Doctrina AI team.
This innovative tool automates investment analysis tasks, offering features such as automated earnings call analysis, financial report inquiry, comparative rankings, sentiment analysis, and practical insights, with considerations for both positive time-saving aspects and challenges like specialization demands and learning curves for users.
AlphaResearch offers a comprehensive investment research platform with features including advanced search, data visualization, document alerts, sentiment analysis, insider transaction analysis, and note-taking support.
Morphlin, driven by AI, offers traders insightful analytics, real-time data, and a robust dashboard, enhancing trading efficiency, though novice users may face a learning curve, and third-party integration may be limited.
An AI and blockchain-powered platform empowering brands with intelligent solutions for optimizing digital strategies in the Web 3.0 economy, including B2B intelligence and real-time B2C analysis.
AlphaSense is a market intelligence platform employing AI to provide professionals with enhanced insights, featuring advanced searching capabilities, data modeling tools, and access to expert analysis, while potentially overwhelming novice users with information volume.
The AI tool offers prescriptive business analytics, monitoring company health, identifying key drivers, forecasting revenue potential, integrating with third-party services, automating data collection and reporting, all through a user-friendly interface.
Ramp's Copilot Assistant is an AI tool that streamlines vendor management, accounting processes, and expense management, offering intelligent solutions to enhance efficiency and cost-effectiveness.
The Smart Contract Analysis Tool aids users in comprehending Ethereum mainnet smart contracts, featuring syntax highlighting, dark mode, larger file capacity, active Twitter support, and ongoing development for additional functionalities.
DoNotPay is an AI-powered platform that offers legal self-help services, enabling users to manage subscriptions, contest fees, locate unclaimed funds, and take legal action with ease.
Featured Tools
Learn to create and diversify portfolio strategies, apply machine learning to financial data, and utilize quantitative modeling and data analytics for investment decisions.
Explore the application, processes, case studies, and ethical implications of Generative AI tools in data analytics across various industries.
Learners will gain the fundamentals necessary to implement AI solutions on Microsoft Azure with this course specialization, which will set them up for success with the AI-900 competency.
The course gives an extensive understanding of AI, which encompasses its ethical implications, neural networks, data significance, and applications.
With an emphasis on ethics, explainability, and privacy, this specialization gives students the tools they need to apply deep learning in clinical decision support systems and electronic health records.