3. Projects and Implementation

Just knowing lots of stuff and gathering knowledge from several different sources is not enough, if one wants to have proper grip on topic making projects is of utmost important. So after choosing course,language and learning all the stuffs everything should not come to halt because we are having no idea of how to or from where to do projects. The following are some beginner friendly projects that you can try!

1. Predict Stock Prices

First, you have many types of data that you can choose from. You can find prices, fundamentals, global macroeconomic indicators, volatility indices, etc… the list goes on and on.Second, the data can be very granular. You can easily get time series data by day (or even minute) for each company, which allows you tothink creatively about trading strategies.

Finally, the financial markets generally have short feedback cycles. Therefore, you can quickly validate your predictions on new data.

Some examples of beginner-friendly machine learning projects you could try include…

Quantitative value investing… Predict 6-month price movements based fundamental indicators from companies’ quarterly reports.

Predict 6-month price movements based fundamental indicators from companies’ quarterly reports. Forecasting… Build time series models, or even recurrent neural networks, on the delta between implied and actual volatility.

Build time series models, or even recurrent neural networks, on the delta between implied and actual volatility. Statistical arbitrage… Find similar stocks based on their price movements and other factors and look for periods when their prices diverge.

Tutorials

Python: sklearn for Investing — YouTube video series on applying machine learning to investing.

R: Quantitative Trading with R — Detailed class notes for quantitative finance with R.

Data Sources

2. Teach a Neural Network to Read Handwriting

Whenever a person starts learning anything in AI usually the first idea that strikes is handwriting scanner/checker.

The MNIST Handwritten Digit Classification Challenge is the classic entry point. Image data is generally harder to work with than “flat” relational data. The MNIST data is beginner-friendly and is small enough to fit on one computer.Handwriting recognition will challenge you, but it doesn’t need high computational power.To start, we recommend with the first chapter in the tutorial below. It will teach you how to build a neural network from scratch that solves the MNIST challenge with high accuracy.

Tutorial

Neural Networks and Deep Learning (Online Book) — Chapter 1 walks through how to write a neural network from scratch in Python to classify digits from MNIST. The author also gives a very good explanation of the intuition behind neural networks.

Data Sources

MNIST — MNIST is a modified subset of two datasets collected by the U.S. National Institute of Standards and Technology. It contains 70,000 labeled images of handwritten digits.

3. Gesture keyboard