Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on models and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model of sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in the applications of email filtering, detection of network intruders, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers.
The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.How we split data in Machine Learning?
Training Data: The part of data we use to train our model. This is the data which your model actually sees(both input and output) and learn from.Validation Data: The part of data which is used to do a frequent evaluation of model, fit on training dataset along with improving involved hyperparameters (initially set parameters before the model begins learning). This data plays it’s part when the model is actually training.
Testing Data: Once our model is completely trained, testing data provides the unbiased evaluation. When we feed in the inputs of Testing data, our model will predict some values(without seeing actual output). After prediction, we evaluate our model by comparing it with actual output present in the testing data. This is how we evaluate and see how much our model has learned from the experiences feed in as training data, set at the time of training.Difference between Machine learning and Artificial Intelligence:
Artificial Intelligence : The word Artificial Intelligence comprises of two words “Artificial” and “Intelligence”. Artificial refers to something which is made by human or non natural thing and Intelligence means ability to understand or think. There is a misconception that Artificial Intelligence is a system, but it is not a system .AI is implemented in the system. There can be so many definition of AI, one definition can be “It is the study of how to train the computers so that computers can do things which at present human can do better.”Therefore It is a intelligence where we want to add all the capabilities to machine that human contain.
Machine Learning : Machine Learning is the learning in which machine can learn by its own without being explicitly programmed. It is an application of AI that provide system the ability to automatically learn and improve from experience. Here we can generate a program by integrating input and output of that program. One of the simple definition of the Machine Learning is “Machine Learning is said to learn from experience E w.r.t some class of task T and a performance measure P if learners performance at the task in the class as measured by P improves with experiences.”
- Speech Recognition (Natural Language Processing in more technical terms) : You talk to Cortana on Windows Devices. But how does it understand what you say? Along comes the field of Natural Language Processing, or N.L.P. It deals with the study of interactions between Machines and Humans, via Linguistics. Guess what is at the heart of NLP: Machine Learning Algorithms and Systems ( Hidden Markov Models being one).
- Computer Vision : Computer Vision is a subfield of AI which deals with a Machine’s (probable) interpretation of the Real World. In other words, all Facial Recognition, Pattern Recognition, Character Recognition Techniques belong to Computer Vision. And Machine Learning once again, with it wide range of Algorithms, is at the heart of Computer Vision.
- Google’s Self Driving Car : Well. You can imagine what drives it actually. More Machine Learning goodness.
But these were expected applications. Even a naysayer would have a good insight about these feats of technology being brought to life by some “mystical (and extremely hard) mind crunching Computer wizardry”.