A question that typically props up is: 

“How can we make money using machine learning?” 

You can get yourself a job or position with your machine learning capabilities as a machine learning engineer, data analyst, or data scientist. That is the objective of several of the masses that are looking to get into machine learning. 

There are also other options. 

In this blog article, we wish to illustrate some of those other options and attempt to get your gears turning. 

There are several many opportunities provided the massive amounts of data available, you just require to contemplate about and find out about the valuable questions. These are the questions to which individuals and businesses will pay to have answered. 

Impact-First 

Let’s begin with some methodology prior to diving into instance domains. 

Like any other machine learning problem, you are following the procedure of applied machine learning, but you are choosing  domain and question where there is a market to have the question solved. 

1] Begin with a question in a field (define your problem well). Select a question on the basis of the impact is has had on the field. Here, impact might be a return. Play thought experiments with an idealized model that can make ideal forecasts. 

2] Gather the data you require to address the question (data selection) 

3] Clean and prep the data in order to make it apt for modelling (data prep) 

4] Spot check algorithms on the problem. Ensure to begin with the simplest potential models and leverage them as a baseline.  

5] Tune the models with ideal performance and leverage methods such as thresholding and ensembles to obtain the most out of the models you have chosen. (enhance outcomes) 

6] Put forth the outcomes or put the system into operations and establish close watches (current outcomes). 

Ideally, the more precisely you tackle the question, the bigger the return. (or the bigger the makes you can make). 

Your Startup 

If you own your own business or web startup, then you ought to look very hard at harnessing the information you are already gathering. It is not unusual to execute a number of data collection services in a web startup, like KissMetrics, Google Analytics, and several others. How can this information be leveraged to impact your bottom line? 

In our experience, this is more the job profile of a data analyst than machine learning-related, however, you can always bust out a regression model and observe if it offers more lift than a simple quintile model. 

We went through this is in a prior post. Nonetheless, here are some spheres of interest you could look into: 

• Customer conversions: Model the features of clients that convert or don’t convert. 
• Up-sell and cross-sell: Model the features of clients that convert on up-sell or cross-sell offers. 
• Acquisition Strategies: Model the value of clients by their acquisition techniques. 
• Client churn: Model the features of clients that churn or don’t churn. 
• Retention techniques: Model the ROI of client retention strategies. 

Begin with the impact on the bottom-line and work backward to the queries you require to ask in order to make decisions. After you can find a solution to the question and make a forecast for a provided new client, spend time devising, and evaluating intervention strategies you can leverage to influence or capitalize on the forecast. 

Development 

You might be a developer or programmer that is aware of how to develop, create, and release software. Contemplate about valuable questions online that you could find a solution to with machine learning strategies. 

Are there forecasts or recommendations you can make that are valuable? 

Some instances off-the-cuff that come off the top of our heads consist of an array of publicly available social media data: 

• Kick Starter: Model the features of a successful or unsuccessful kick starter campaign. 
• Social Media Profile: Model the features of an efficient social media profile (visits or page rank) on websites such as LinkedIn, Google+, or Facebook. 
• Social News: Model the features of an efficient posting to a social news site like hacker news or Reddit. 
• Sales page: Model the features of an efficient product sales page like for e-commerce on information products. 

Making money from insights into social media information is a crowded space. If you wish to take this concept seriously, you are going to have to become really creative with the features you leverage to model the problem. The feature engineering will be your contribution, probably more than the actual models themselves. 

This strategy will most probably need the gathering and processing of awkward datasets. These are datasets that are not clean matrices of features. The modelling procedure would be to characterize the desirable result to begin with, evaluate its forecasting power and then make forecasts that you provide to clients. 

Finance and Gambling 

The clear options for making money with machine learning are finance and gambling. We are reticent to highlight these spheres. We believe they can be very probably dangerous sirens. Like Venus Flytraps, they lure programmers and ML practitioners and digest them. 

The advantage is that the decisions are very obvious. (which horse will win or which stock to buy/sell) and you can deploy your own capital that underlie the decisions. We’d indicate modelling problems that are simple for you to comprehend, some financial instruments can be very complicated. 

We’ve had our toes in high-frequency trading and portfolio optimization. It can be frightening stuff, also exciting. We recommend paper trading for a bit, there are amazing APIs you can leverage for your data source. Also, you might wish to take a peek at Quantopian. 

We have not attempted any gambling problems, but we have leveraged some of the strategies like rating systems that we expect to feature heavily in the literature. Take up looking into horse and dog racing, sports betting (two player games) and card games such as poker. 

Race yourself. Concentration on the problem, gather the data and swiftly define some baseline outcomes. Your objective is to enhance upon your own ideal outcomes and to harness anything and everything that might assist. You objective is not to outperform domain specialists, at least not for a long time. 

Competitions 

You can earn money by taking part in machine learning contests. Our advise is that the cash prizes not be the main motivating factor for taking part in contests. You can make a ton more money by identifying consulting clients directly. Nonetheless, top contestants can win monetary prizes. 

Some places you can identify machine learning contests consist of: 

• Kaggle 
• Challenge.gov 
• Innocentive 
• Tunedit 

Contests can be an incredible avenue for learning, testing, and enhancing your abilities. There is usually a ton of data sharing on these websites and you can identify what algorithms and utilities are hot. 

Conclusion 

We touched on four spheres that you could think about in order to earn money from machine learning for your own business, from social information, finance, and gambling and competitions. The ideal strategy that we have identified people with problems that you can find solutions to simply accessible data. (i.e. consulting)