Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Arthur Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term “Machine Learning” in 1959 while at IBM. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output value within an acceptable range. Machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
Some machine learning methods
Supervised machine learning algorithms
This algorithm consist of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly. For example, pictures of dogs labeled “dog” will help the algorithm identify the rules to classify pictures of dogs.
Unsupervised machine learning algorithms
Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data. It is used for clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention. Examples of Unsupervised Learning: Apriori algorithm, K-means.
Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it / learn from it.
Reinforcement machine learning algorithms
It is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions. Example of Reinforcement Learning: Markov Decision Process.
Why is Machine Learning So Important
Most of the industries dealing with huge amounts of data have now recognized the value of machine learning. By gleaning hidden insights from this data, businesses can work more efficiently and can also gain a competitive edge. Besides, affordable and easy computational processing and cost-effective data storage options have made it feasible to develop models that quickly and accurately analyze huge chunks of complex data. The current growth in AI and machine learning is tied to developments in some important areas
- Superior data preparation capabilities
- Knowledge of basic and advanced algorithms
- Automation and iterative processes
- Knowledge of ensemble modelling
Machine Learning Applications
The value of machine learning technology has been recognized by companies across several industries that deal with huge volumes of data.
Companies in the financial sector are able to identify key insights in financial data as well as prevent any occurrences of financial fraud, with the help of machine learning technology. The technology is also used to identify opportunities for investments and trade. Usage of cyber surveillance helps in identifying those individuals or institutions which are prone to financial risk, and take necessary actions in time to prevent fraud. More than 90% of the top 50 financial institutions around the world are using machine learning and advanced analytics. The application of machine learning in Finance domain helps banks offer personalized services to customers at lower cost, better compliance and generate greater revenue.
Doctors and medical practitioners will soon be able to predict with accuracy on how long patients with fatal diseases will live. Medical systems will learn from data and help patients save money by skipping unnecessary tests. Radiologists will be replaced by machine learning algorithms. McKinsey Global Institute estimates that applying machine learning techniques to better inform decision making could generate up to $100 billion in value based on optimized innovation, enhanced efficiency of clinical trials and the creation of various novel tools for physicians, insurers and consumers. Doctors and medical experts can use this information to analyze the health condition of an individual, draw a pattern from the patient history, and predict the occurrence of any ailments in the future. The technology also empowers medical experts to analyze data to identify trends that facilitate better diagnoses and treatment.
Marketing and Sales
Companies are using machine learning technology to analyze the purchase history of their customers and make personalized product recommendations for their next purchase. This ability to capture, analyze, and use customer data to provide a personalized shopping experience is the future of sales and marketing.
Based on the travel history and pattern of traveling across various routes, machine learning can help transportation companies predict potential problems that could arise on certain routes, and accordingly advise their customers to opt for a different route. By 2030, there will be a solution for each unique travel purpose. Instead of commuting to work and stressing about finding parking, you can take a ride sharing service. For leisurely trips, self-driving cars will be able to handle transportation, while your relax and watch a movie.
Oil and Gas
This is perhaps the industry that needs the application of machine learning the most. Right from analyzing underground minerals and finding new energy sources to streaming oil distribution, ML applications for this industry are vast and are still expanding.
Machine learning offers the most efficient means of engaging billions of social media users. From personalizing news feed to rendering targeted ads, machine learning is the heart of all social media platforms for their own and user benefits. Social media and chat applications have advanced to a great extent that users do not pick up the phone or use email to communicate with brands – they leave a comment on Facebook or Instagram expecting a speedy reply than the traditional channels.
Government agencies like utilities and public safety have a specific need FOR Ml, as they have multiple data sources, which can be mined for identifying useful patterns and insights. For example sensor data can be analyzed to identify ways to minimize costs and increase efficiency. Furthermore, ML can also be used to minimize identity thefts and detect fraud.
List of Common Machine Learning Algorithms
- Naïve Bayes Classifier Algorithm
- K Means Clustering Algorithm
- Support Vector Machine Algorithm
- Apriori Algorithm
- Linear Regression
- Logistic Regression
- Artificial Neural Networks
- Random Forests
- Decision Trees
- Nearest Neighbours
Machine Learning Challenges
A fundamental challenge facing data scientists has nothing to do with ensemble algorithms, optimization methods, or computing power. Communication – prior to any analysis or data engineering – is crucial to solving an ML problem quickly and painlessly.There are many, many questions ML can solve: this is an incredibly powerful tool for making sense of the world around us. However, these questions have to be specific and formulaic in a way that the people responsible for identifying the problem, such as management or marketing, might be unfamiliar with.
Memory networks or memory augmented neural networks still require large working memory to store data. This is a major hurdle that ML needs to overcome. To attain truly efficient and effective AI, we have to find a better method for networks to discover facts, store them, and seamlessly access them when needed.
Natural language processing (NLP)
Although a lot of money and time has been invested, we still have a long way to go to achieve natural language processing and understanding of language.This is still a massive challenge even for deep networks. At the moment, we teach computers to represent languages and simulate reasoning based on that. However, this has been consistently poor.