What is Machine Learning?
Machine learning is a subset of artificial intelligence that enables systems to learn and predict outcomes without explicit programming. It is often used interchangeably with the term AI because it is the AI technique that has made the greatest impact in the real world to date, and it’s what you’re most likely to use in your business. Chatbots, product recommendations, spam filters, self-driving cars and a huge range of other systems leverage machine learning, as do “intelligent agents” like Siri and Cortana.
In this Fortune article, What is the Difference Between Artificial Intelligence and Machine Learning, the author clearly describes the difference between AI and Machine Learning: “AI is the broader concept of machines being able to carry out tasks in a way that we would consider smart,” while machine learning is “a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.”
How Does Machine Learning Work?
Instead of writing algorithms and rules that make decisions directly or trying to program a computer to “be intelligent” using sets of rules, exceptions and filters, machine learning teaches computer systems to make decisions by learning from large data sets. Rule-based systems quickly become fragile when they have to account for the complexity of the real world; machine learning can create models that represent and generalize patterns in the data you use to train it, and it can use those models to interpret and analyze new information.
Machine learning is suitable for classification, which includes the ability to recognize text and objects in images and video, as well as finding associations in data or segmenting data into clusters (e.g., finding groups of customers). Machine learning is also adept at prediction, such as calculating the likelihood of events or forecasting outcomes. Machine learning can also be used to generate missing data; for example, the latest version of CorelDRAW uses machine learning to interpolate the smooth stroke you’re trying to draw from multiple rough strokes you make with the pen tool.
Two Machine Learning Modes: Supervised and Unsupervised
When software is used to tell the algorithm specifically what we want to discover, it is called supervised ML. The machine learning algorithms use a ‘target’ variable or attribute to ‘train’ a model based on the data in the target variable.
Unsupervised ML is when the machine can learn to identify complex processes and patterns without a human to provide guidance along the way. It uses clustering as an application, where the dataset records are automatically segmented into groups. Those groups are similar to records in their own group and dissimilar to records in other groups.
What are Applications of Machine Learning?
Almost every business industry can benefit from machine learning, including applications such as forecasting, fraud detection and human resources.
When it comes to demand forecasting, machine learning algorithms can be especially helpful in complex scenarios. Machines can do a much better job of forecasting difficult situations than humans. They leverage the knowledge, experience and skills of planners, and other experts in a highly efficient and effective way across a broad range of data.
- Fraud Detection
Many financial companies have taken steps using AI tools to detect suspicious activity and separate false alarms from true fraud. The goal is not only to reduce the number of false alarms, but also to avoid blocking the accounts of legitimate customers as well.
If the same account has been accessed by five internet protocol addresses within five days, traditional analytics software sees a pattern and flags that as suspicious. But with machine learning, a system can look at each case more closely and see that the user is an executive buying gifts for his family while on the job. And thereby avoid blocking the executive’s transactions that were completely legitimate.
- Human Resources
Artificial intelligence helps HR managers and directors make decisions with greater confidence. The technology helps with better management of data-heavy tasks, spotting top talent in unlikely places, and even improving employee satisfaction to reduce turnover rates.
Machine learning algorithms can assess candidates to find the best vacancy for them. Machines can detect patterns faster and with a greater degree of accuracy than HR departments can. Programs can determine where they might fit best in the company by correlating candidate’s’ past experiences and relevant skills.
What Skills Are Required for a Career in Machine Learning?
If you are considering a career in machine learning, here are some of the skills that will help you excel in this growing field.
- Statistics: Tools and tables are essential in machine learning to create models from data. Statistics and its branches such as analysis of variance and hypothesis testing are crucial for building algorithms. And machine learning algorithms are built upon statistical models. So it is vital to gain knowledge on statistical tools to accelerate your career in machine learning.
- Probability: Probability helps in predicting future consequences, and most of the algorithms in machine learning work based on uncertain conditions, yet they must make reliable decisions. Mathematical equations in probability such as derivative techniques, Bayes Nets and Markov decisions would help machine learning in predicting the future.
- Data Modeling: The critical task of machine learning is to analyze unstructured data models, which requires the science of data modeling. Data modelling helps identify the underlying data structures, finding the patterns, and filling the gaps between the places where data is nonexistent. Having explicit knowledge of data modeling concepts would help in creating efficient algorithms.
- Programming Skills: Since machine learning mostly depends on algorithms, it’s important to have a sound knowledge of programming languages. Dealing with machine concepts is entirely different from handling smaller tasks such as creating simple applications or scripting web pages. It demands extra knowledge and skill of programming. Below are skills that will help you become a professional in machine learning.
Below are skills that will help you become a professional in machine learning:
- Programming Fundamentals and Computer Science: Machine learning involves computation on large data sets so it’s important to have strong fundamental skills in areas such as computer architecture, algorithms, and data structures.
- Applying ML Libraries & Algorithms: As a machine learning engineer, you don’t need to start from scratch; you can take advantage of algorithms and libraries that are developed by other developers and organizations. You can choose from a wide variety of API packages and libraries such as Microsoft Azure ML Studio, Apache Spark MLlib, and Google TensorFlow.
- Software Design: Machine learning requires integration so you’ll need to develop algorithms and systems that can easily integrate and communicate with existing technologies. Strong skills working with APIs such as web APIs and dynamic and static libraries will be extremely valuable.