What is machine learning? "The high level and most commonly accepted definition is: machine learning is the ability for computers to learn and act without being explicitly programmed." Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly. A lot of people get confused with ML and AI. A few use the terminologies interchangeably. Artificial Intelligence (AI) is all about simulating intelligence in machines. Machine Learning (ML) is a subset of AI. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. Machine learning is a subset of AI. The theory is simple, machines take data and ‘learn’ for themselves. To master Machine Learning (ML) one has to be good at maths, programming, and domain knowledge. Domain knowledge (Eg: how to deal with images, audio, financial time series etc) changes from one class of problem to another, so let us focus on first two. Why Maths?: we need maths to understand the machine learning algorithms/ models or to implement new ones. There are large number of models which are already built. Even when we are using existing models we need to understand the internal working of the algorithm so that we can tune the hyper parameters. Single model may not give best results for all the problems (no free lunch). Which model to use for the given problem is very important and to choose the right model, one needs to understand the internal working/ maths. Thankfully you don’t need all the math but only some sub-branches:
Why Programming? Programming is needed to use ML models (or build new one), get the data from various sources, clean the data, choose the right features and to validate if the model has learned correctly. Thankfully you don’t have to be an expert programmer. Some programming languages are preferred for doing ML than others because they have large number of libraries with most of the ML models already implemented. Languages suited for ML
Books:
Practice: In the next blog, I will write about a practical approach on ML, setting up a Lab, getting our hands dirty with coding and making some cool predictions on a given dataset.
Stay tuned.
0 Comments
Leave a Reply. |
AuthorRoshan Zameer is a Data and Security Enthusiast. He works as a Data Engineer at a UK based Market Intelligence firm. ArchivesCategories |