Can computers think and make decisions like humans? Can they improve the productivity and efficiency of our day-to-day work? The answer is “yes”, as Machine Learning (ML) drives innovations to make this a reality.
ML is a form of Artificial Intelligence (AI) that enables computers to “learn” without being explicitly programmed. ML programs react differently when exposed to new data because they use an iterative approach to learn from the data.
Over the last few years, ML has evolved to the point where computers now have the ability to probe data for meaningful structure, even if we humans do not have any knowledge of what that structure might look like. As such, researchers have started thinking that perhaps ML is the best way for computers to approach something like human thinking.
As businesses produce an increasing volume of data, ML allows them to analyze the data to increase efficiency and competitiveness.
In addition, ML can provide social value by improving weather forecast, conducting tax audits to detect fraud and even detect fake news generated through social media. In industry, there are a number of applications for ML:
- Improve the efficiency of the product testing
- Customer support and retention
- Medical diagnostics
- Search engine optimizations
ML tasks are typically classified into four broad categories, depending on the nature of the learning signal or feedback available to a learning system.
- Supervised learning: In supervised learning is a system in which input and desired output data is provided. Based on the input and desired output, future data is processed.
- Unsupervised learning: Learning based on discovery of hidden patterns in the data.
- Reinforcement learning: The machine automatically determines the ideal behavior within a specific context to maximize performance.
- Semi-supervised learning: A mix of supervised and unsupervised learning, works on both labeled and unlabeled data.
Below are a few typical ML techniques:
- Decision tree learning: Here the goal is to create a model that predicts the target value based on few input variables. This technique is mostly used in data mining.
- Deep learning: Deep Learning focuses on a subset of Machine learning tools and techniques, and can be applied to solve any problem which requires intelligence.
- Clustering: Is a technique of grouping the similar items/objects. Clustering is the main task involved in data mining and a commonly used in statistical data analysis.
- Bayesian networks: This is a probabilistic graphical model used to build data models.
- Association rule learning: It’s a rule based machine learning technique used for discovering the relations between items/objects in the database. Many algorithms for generating association rules have been proposed. Well known algorithms are Apriori, Eclat and FP-Growth
- Inductive logical programming: ILP is a machine learning technique which uses examples (both positive and negative) and background knowledge to derive at the hypothesis.
- Genetic algorithms: are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics. As such they represent an intelligent exploitation of a random search used to solve optimization problems.
While ML has proven successful for many tasks, it should not be applied to all types of tasks and data. A systematic procedure will help to Identify where ML techniques can provide value:
- A detailed discussion or workshop to identify possible use cases
- Define the scope of deliverables
- Experiment with the algorithm on sample set of data
- Extend the technique to a larger data set before implementing
Identifying the number and type of ML features to be used is very important. In addition, choosing non-essential features can actually hurt your accuracy. Make sure each feature delivers useful information, and is independent and simple. Classifiers are only as good as the features you provide.
Consider the following typical classification and regression problems, which uses machine learning. The steps include:
- Define the problem clearly.
- Analyze and summarize the available data.
- Prepare the data, which involves sampling and formatting the dataset in a structured format.
- Evaluate the algorithm and create an acceptable baseline accuracy.
- Improve the algorithm by fine tuning it to suit the desired output. Repeat this step until you get the desired result.
- Present the results.
Many organizations are investing in AI and ML techniques to improve the efficiency and quality of their operations and to give them a competitive edge in the marketplace. The more methodical and structures they are in the development and application of these techniques, the more successful they will be.