About this Course

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Flexible deadlines
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Intermediate Level
Approx. 9 hours to complete
English
Subtitles: English

Skills you will gain

Machine LearningFinanceTradingInvestment
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 9 hours to complete
English
Subtitles: English

Offered by

Google Cloud logo

Google Cloud

New York Institute of Finance logo

New York Institute of Finance

Syllabus - What you will learn from this course

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Week
1

Week 1

4 hours to complete

Introduction to Trading with Machine Learning on Google Cloud

4 hours to complete
26 videos (Total 131 min), 3 readings, 5 quizzes
26 videos
Course Overview Introduction to Trading with Machine Learning on Google Cloud5m
What is AI and ML ? What is the difference between AI and ML?58s
Applications of ML in the Real World1m
What is ML?3m
Game: The importance of good data4m
Brief History of ML in Quantitative Finance11m
Why Google?1m
Why Google Cloud Platform?2m
What are AI Platform Notebooks1m
Using Notebooks1m
Benefits of AI Platform Notebooks2m
What do we want to model? Let's start simple5m
Demo: Building a model with BigQuery ML25m
How to use Qwiklabs for your Labs3m
Lab Intro: Building a Regression Model37s
Lab Walkthrough: Building a Regression Model9m
Trading vs Investing6m
The Quant Universe2m
Quant Strategies7m
Quant Trading Advantages and Disadvantages4m
Exchange and Statistical Arbitrage8m
Index Arbitrage2m
Statistical Arbitrage Opportunities and Challenges5m
Introduction to Backtesting5m
Backtesting Design6m
3 readings
Supervised Learning and Regression10m
Welcome to Introduction to Trading, Machine Learning and GCP10m
Case Study: Capital Markets in the Cloud10m
4 practice exercises
Python Skills Assessment Quiz
AI and Machine Learning5m
Google Cloud
Trading Concepts Review15m
Week
2

Week 2

3 hours to complete

Supervised Learning with BigQuery ML

3 hours to complete
6 videos (Total 29 min), 1 reading, 3 quizzes
6 videos
What is forecasting? - part 24m
Choosing the right model and BQML - part 13m
Choosing the right model and BQML - part 22m
Lab Intro: Forecasting Stock Prices using Regression in BQML36s
Lab Walkthrough: Forecasting Stock Prices using Regression in BQML12m
1 reading
Staying current with BigQuery ML model types10m
1 practice exercise
Forecasting
Week
3

Week 3

2 hours to complete

Time Series and ARIMA Modeling

2 hours to complete
11 videos (Total 52 min)
11 videos
AR - Auto Regressive7m
MA - Moving Average2m
The Complete ARIMA Model4m
ARIMA compared to linear regression7m
How can you get a variety of models from just a single series?1m
How to choose ARIMA parameters for your trading model4m
Time Series Terminology: Auto Correlation4m
Sensitivity of Trading Strategy4m
Lab Intro: Forecasting Stock Prices Using ARIMA32s
Lab Walkthrough: Forecasting Stock Prices using ARIMA7m
1 practice exercise
Time Series
Week
4

Week 4

1 hour to complete

Introduction to Neural Networks and Deep Learning

1 hour to complete
5 videos (Total 29 min), 1 reading, 2 quizzes
5 videos
Short history of ML: Modern Neural Networks8m
Overfitting and Underfitting6m
Validation and Training Data Splits4m
Course Recap + Preview of next course 1m
1 reading
Example BigQuery ML DNN code10m
2 practice exercises
Model generalization
Recap Quiz8m

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About the Machine Learning for Trading Specialization

This Specialization is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning. Alternatively, this specialization can be for machine learning professionals who seek to apply their craft to quantitative trading strategies. The courses will teach you how to create various trading strategies using Python. By the end of the Specialization, you will be able to create quantitative trading strategies that you can train and implement. You will also learn how to use reinforcement learning strategies to create algorithms that can update and train themselves. To be successful in this Specialization, you should have a basic competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL will be helpful. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and a basic knowledge of financial markets (equities, bonds, derivatives, market structure, hedging)....
Machine Learning for Trading

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