Data science for finance

[mitsumi]

Data science for finance
Last updated 03/2023
Duration: 52m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 389 MB
Genre: eLearning | Language: English[Auto]

Learn to trade in stock market using machine learning and reinforcement learning techniques

What you'll learn
Use a variety of data preparation methods with financial data
Predict future prices using advance deep learning and reinforcement learning techniques
Get hands-on financial forecasting experience using machine learning with Python, Keras, Scikit-Learn and pandas
Predict future values based on single and multiple values
Understand the process behind choosing the best performing data preparation method and model
Grasp Machine Learning forecasting on a specific real-world financial data
Apply various AI techniques for prediction
Learn how to fetch stock data from api
Requirements
Basic python syntax
Description
You will learn about how to work with stock data using AI techniques like deep learning,machine learning and reinforcement learning.
While humans remain a big part of the trading equation, AI plays an increasingly significant role. According to a recent study electronic trades account for almost 45 percent of revenues in cash equities trading. And while hedge funds are more reluctant when it comes to automation, many of them use AI-powered analysis to get investment ideas and build portfolios.
AI is shaping the future of stock trading. Using AI, companies analyze millions of data points and execute trades at the optimal price, analysts forecast markets with greater accuracy and trading firms efficiently mitigate risk to provide for higher returns.
A lot of solutions to key problems in the financial world require predicting the future patterns in data from the past to make better financial decisions right now. The evolution of modern machine learning methods and tools in recent years in the field of computer vision bring promise of the same progress in other important fields such as financial forecasting.
In this course, you'll first learn how to quickly get started with ML in finances by predicting the future currency exchange rates using a simple modern machine learning method. In this example, you'll learn how to choose the basic data preparation method and model and then how to improve them. In the next module, you'll discover a variety of ways to prepare data and then see how they influence models training accuracy. In the last module, you'll learn how to find and test a few key modern machine learning models to pick up the best performing one.
After finishing this course, you'll have a solid introduction to apply ML methods to financial data forecasting.
Who this course is for
People who are interested to use AI in finance

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