For Financial Analysts, Traders, and Investment Enthusiasts:
Want to Learn How to Use Machine Learning to Make You More Money in Your Job and in Your Trading?
For Financial Analysts, Traders, and Investment Enthusiasts:
Want to Learn How to Use Machine Learning to Make You More Money in Your Job and in Your Trading?
Machine Learning for Finance
Learn the fundamentals of Machine Learning and how to apply this to the world of Finance to help you both in your career, and with your investing!
TOP-RATED COURSE + UNIQUE APPROACH
This is the best finance focused data science/python course, rated 4.8 out of 5!
STUDENTS HAVE TAKEN OUR COURSES
We have helped thousands of students all over the world with our courses.
LIFETIME ACCESS (INCLUDING UPDATES)
We’re always adding new material – and you’ll get lifetime access to the content!
CERTIFICATE OF COMPLETION
After successfully passing the course you will receive a certificate of completion that you can show to employers.
Increase Your Salary
Create Projects You Can Use or Add to Your Resume
Make More Money From Your Investments
Level Up Your Skills
Try a Free Live Class and Get More Course Info
Fill the form to receive access to a FREE LIVE CLASS and get the Course Information PDF
Our Course Is For:
- Finance professionals new to data science who want to learn Machine Learning to take their career to the next level,
- Experienced programmers looking to work in a Finance context,
- Individuals who want to learn Python to analyze their investments and automate their trades to help make them more money,
- Individuals who are interested in a Data Science job or a developer job in a financial institution.
What Will You Be Able To Do?
Here are just a few things you’ll be able to do after completing our course.
- Learn the fundamentals of Machine Learning, and how to apply it to the world of finance
- Make better investment and trading decisions using data
- Learn how to properly apply Machine Learning to time series data
- Use natural language processing to forecast events (e.g. how a particular news story would affect a stock price)
- How deep learning is applied to finance
- Analyze data to be used for Algorithmic Trading
- Carry out in-depth investment analysis
- Download and use news data to forecast investment outcomes
- Use Machine Learning to predict a stock’s price movements
- How to run Machine Learning models on Google’s AI platform
- How to optimize your Machine Learning models, especially for financial data
- Learn about the most common/effective Machine Learning models and their financial use cases
The Classroom Comes to You
We have several plans for our course each designed to fit your learning needs and style. Our classes are taught online live and one-on-one, or you can just get the videos only. Whatever plan you choose, we are here to support you and make sure you are learning and achieving your goals.
Videos Only
Videos only is a great way to get started on your learning with hours of engaging video lecture and reusable code.
Live Learning
Live learning is a fun way to learn allowing you to interact with other students and the instructor in real time fostering collaboration and discussion about the course material.
One-On-One Learning
One-on-one learning is a great way to learn allowing you to go at your own pace with the instructor guiding you along the way.
The Classroom Comes to You
We have several plans for our course each designed to fit your learning needs and style. Our classes are taught online live and one-on-one, or you can just get the videos only. Whatever plan you choose, we are here to support you and make sure you are learning and achieving your goals.
Videos Only
Videos only is a great way to get started on your learning with hours of engaging video lecture and reusable code.
Live Learning
Live learning is a fun way to learn allowing you to interact with other students and the instructor in real time fostering collaboration and discussion about the course material.
One-On-One Learning
One-on-one learning is a great way to learn allowing you to go at your own pace with the instructor guiding you along the way.
Course Curriculum & The Projects
Week 1: Student Introductions, Helpful Resources, Etc.
- Introductions
- Course Layout and Tools
- System Setup
- Q&A
Week 2: Machine Learning Basics
- Why and What is Machine Learning
- Linear Regression
- Random Forest
- Training Process
Week 3: Deep Learning
- Shallow vs. Deep Learning
- Why, What and When to Use Deep Learning
- Training and Validating Deep Learning Models
Week 4: Optimizing Models
- Google AutoML Tables
- Google AI Platform
- Experiment Sheet
Week 1: Time Series Forecasting
- Time Series vs. Non-Time Series Model Difference
- Theory
- Training and Validation Process
Week 2: Building a Stock Forecasting Model
- Feature Selection
- Error Measurement
- Project Setup
Week 1: Natural Language Processing
- Importance
- How It Works
- Using Google Cloud Natural Language
Week 2: Practical Stock Forecasting Model
- Hooking Up To Newsfeeds
- Sentiment Analysis
- Project Setup
Project 1: Forecasting Stock Prices Using Machine Learning
In this project, we will use the latest machine learning techniques to forecast a stock’s price 15 minutes into the future on a rolling basis. This technique can be applied for your trading to help you get better returns, and can also be used in many other finance applications such as forecasting revenue, profit, and other time-series data.
Project 2: Using Machine Learning to Trade on the News
For this project, you will pull data from a news source (e.g. Bloomberg, Twitter, Reddit, etc) and then train a machine-learning algorithm to read the news and suggest trades. Some examples of this could be breaking news, analyst recommendations, social media interest, and more, where your algorithm could react hundreds of times faster than a human being.
Meet Your Instructor
Greg Tanaka
Greg is CEO and Co-founder of Pika Group. Pika strives to make algorithmic trading more accessible for individual traders. Pika helps day traders develop machine learning models and automate execution. Pika is unique given its specialization in deep learning-based algo trading.
Pika Group is a wholly-owned subsidiary of Percolata. Percolata raised $10M and is funded by Google Ventures, Andreessen Horowitz, Menlo Ventures and others. Percolata Forecast as a Service (FaaS) gathers and forecasts shopper traffic and transactions with 4x higher accuracy for retailers using weather forecasts, marketing calendars, and other data via proprietary deep learning technology.
Greg led the Percolata Machine Learning team to score in the top 5% out of 5.6k team that entered the M5 Forecasting (https://www.kaggle.com/gltanaka). M5 was held by The Makridakis Open Forecasting Center (MOFC). The MOFC is well known for its Makridakis Competitions, the first of which ran in the 1980s and is the most prestigious forecasting competition in the world.
Greg is also on the Palo Alto City Council. He is a Caltech and UC Berkeley alumni and SITN @Stanford.
Meet Your Instructor
Greg Tanaka
Greg is CEO and Co-founder of Pika Group. Pika strives to make algorithmic trading more accessible for individual traders. Pika helps day traders develop machine learning models and automate execution. Pika is unique given its specialization in deep learning-based algo trading.
Pika Group is a wholly-owned subsidiary of Percolata. Percolata raised $10M and is funded by Google Ventures, Andreessen Horowitz, Menlo Ventures and others. Percolata Forecast as a Service (FaaS) gathers and forecasts shopper traffic and transactions with 4x higher accuracy for retailers using weather forecasts, marketing calendars, and other data via proprietary deep learning technology.
Greg led the Percolata Machine Learning team to score in the top 5% out of 5.6k team that entered the M5 Forecasting (https://www.kaggle.com/gltanaka). M5 was held by The Makridakis Open Forecasting Center (MOFC). The MOFC is well known for its Makridakis Competitions, the first of which ran in the 1980s and is the most prestigious forecasting competition in the world.
Greg is also on the Palo Alto City Council. He is a Caltech and UC Berkeley alumni and SITN @Stanford.
Try a Free Live Class and Get More Course Info
Fill the form to receive access to a FREE LIVE CLASS and get the Course Information PDF
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