What is Machine Learning? And which are the best practices?
As machine learning continues to address common use cases it is crucial to consider what it takes to operationalize your data into a practical, maintainable solution. This is particularly important in order to predict customer behavior more accurately, make more relevant product recommendations, personalize a treatment, or improve the accuracy of research. In this blog, we will attempt to understand the meaning of Machine Learning, what it takes for it to work and which are the best machine learning practices.
What is ML?
Machine learning is a computer programming technique that uses statistical probabilities to give computers the ability to 'learn' without being explicitly programmed. Simply put, machine learning 'learns' based on its exposure to external information. Machine learning makes decisions according to the data it interacts with and uses statistical probabilities to determine each outcome. These statistics are supported by various algorithms modeled on the human brain. In this way, every prediction it makes is backed up by solid factual, mathematical evidence derived from previous experience.
A good example of machine learning is the sunrise example. A computer, for instance, cannot learn that the sun will rise every day if it does not already know the inner workings of the solar system and our planets, and so on. Alternatively, a computer can learn that the sun rises daily by observing and recording relevant events over a period of time.
After the computer has witnessed the sunrise at the same time for 365 consecutive days, it will calculate, with a high probability, that the sun will rise again on the three hundred and sixty-sixth day. That is, of course, there will still be an infinitesimal chance that the sun won't rise the day after as the statistical data collected thus far will never allow for a 100% probability.
There are three types of machine learning:
1. Supervised Machine Learning
In supervised machine learning, the computer learns the general rule that maps inputs to desired target outputs. Also known as predictive modeling, supervised machine learning can be used to make predictions about unseen or future data such as predicting the market value of a car (output) from the make (input) and other inputs (age, mileage, etc).
2. Un-supervised Machine Learning
In un-supervised machine learning, the algorithm is left on its own to find structure in its input and discover hidden patterns in data. This is also known as 'feature learning'.
For example, a marketing automation program can target audiences based on their demographics and purchasing habits that it learns.
3. Reinforcement Machine Learning
In reinforcement machine learning, a computer program interacts with a dynamic environment in which it must perform a certain goal, such as driving a vehicle or playing a game against an opponent. The program is given feedback in terms of rewards and punishments as it navigates the problem space, and it learns to determine the best behavior in that context.
Making ML work with data quality
Machine Learning depends on data. Good quality data is needed for successful ML. The more reliable, accurate, up-to-date and comprehensive that data is, the better the results will be. However, typical issues including missing data, inconsistent data values, autocorrelation and so forth will affect the statistical properties of the datasets and interfere with the assumptions made by algorithms. It is vital to implement data quality standards with your team throughout the beginning stages of the machine learning initiative.
Democratizing and operationalizing
Machine Learning can appear complex and hard to deliver. But if you have the right people involved from the beginning, with the right skills and knowledge, there will be less to worry about.
Get the right people on your team involved who:
- can identify the data task, chose the right model and apply the appropriate algorithms to address the specific business case
- have the skills in data engineering are useful, as machine learning is all about data
- will choose the right programming language or framework for your needs
- have a background in general logic and basic programming is vital in order
- have a good understanding of core mathematics to help you manage most standard machine learning algorithms effectivelym, especially Linear Algebra, Calculus, Probability, Statistics and Data and Frameworks
Most importantly, share the wealth. What good is a well-designed machine learning strategy if the rest of your organization cannot join in on the fun. Provide a comprehensive ecosystem of user-friendly, self-service tools that incorporates machine learning into your data transformation for equal access and quicker insights. A single platform that brings all your data together from public and private cloud as well as on-premise environments will enable your IT and business teams to work more closely and constructively while remaining at the forefront of innovation.
Machine Learning best practices
Now that you are prepared to take a data integration project that involves machine learning head-on, it is worth following these best practices below to ensure the best outcome:
- Understand the use case – Assessing the problem you are trying to solve will help determine whether machine learning is necessary or not.
- Explore data and scope – It is essential to assess the scope, type, variety and velocity of data required to solve the problem.
- Research model or algorithm – Finding the best-fit model or algorithm is about balancing speed, accuracy and complexity.
- Pre-process – Data must be collated into a format or shape which is suitable for the chosen algorithm.
- Train – Teach your model with existing data and known outcome.
- Test – Test against non-associated data without known outcomes to test accuracy
- Operationalize – After training and validating, start calculating and predicting outcomes with new data.
As data increases, more observations are made. This results in more accurate predictions. Thus, a key part of a successful data integration project is creating a scalable machine learning strategy that starts with good quality data preparation and ends with valuable and intelligible data.
Author: Javier Hernandez