Basic Introduction to Machine Learning: Types, Applications & Examples
Machine Learning is a large sub-field of AI dealing with the field of study that gives computers the ability to learn without being explicitly programmed. This means a single program, once created, will be able to learn how to do some intelligent activities outside the notion of programming. This contrasts with purpose-built programs whose behavior is defined by hand-crafted heuristics that explicitly and statically define their behavior. So, Machine Learning is an approach to achieve Artificial Intelligence.
Machine learning combines data with statistical tools to predict an output. This output is then used by corporate to makes actionable insights. Machine learning is closely related to data mining and Bayesian predictive modeling. The machine receives data as input, use an algorithm to formulate answers.
Machine Learning vs Traditional Programming
Traditional programming differs significantly from machine learning. In traditional programming, a programmer code all the rules in consultation with an expert in the industry for which software is being developed. Each rule is based on a logical foundation; the machine will execute an output following the logical statement. When the system grows complex, more rules need to be written. It can quickly become unsustainable to maintain.
Machine learning is supposed to overcome this issue. The machine learns how the input and output data are correlated and it writes a rule. The programmers do not need to write new rules each time there is a new data. The algorithms adapt in response to new data and experiences to improve efficacy over time.
Working of Machine Learning
Machine learning is the brain where all the learning takes place. The way the machine learns is similar to the human being. Humans learn from experience. The more we know, the more easily we can predict. By analogy, when we face an unknown situation, the likelihood of success is lower than the known situation. Machines are trained the same. To make an accurate prediction, the machine sees an example. When we give the machine a similar example, it can figure out the outcome. However, like a human, if its feed a previously unseen example, the machine has difficulties to predict.
The core objective of machine learning is the Learning and Inference.
Learning
First of all, the machine learns through the discovery of patterns. This discovery is made thanks to the data. One crucial part of the data scientist is to choose carefully which data to provide to the machine. The list of attributes used to solve a problem is called a feature vector. A Feature vector can be thought as a subset of data that is used to tackle a problem.
The machine uses some fancy algorithms to simplify the reality and transform this discovery into a model. Therefore, the learning stage is used to describe the data and summarize it into a model.
Infering
When the model is built, it is possible to test how powerful it is on never-seen-before data. The new data are transformed into a features vector, go through the model and give a prediction. This is all the beautiful part of machine learning. There is no need to update the rules or train again the model. The model previously trained can be used to make inference on new data.
Types of Machine Learning
Even though there are many other algorithms, Machine Learning can be grouped into three broad learning tasks:
Supervised Learning
The program is “trained” on a pre-defined set of “training examples”, which then facilitate its ability to reach an accurate conclusion when given new data. Here, algorithm uses training data and feedback from humans to learn the relationship of given inputs to a given output. For instance, a practitioner can use marketing expense and weather forecast as input data to predict the sales of cans.
Supervised learning is used when the output data is known. The algorithm will predict new data. Supervised learning requires that the data used to train the algorithm is already labeled with correct answers.
Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output Y = f(x). The goal is to approximate the mapping function so well that when you have new input data (x) that you can predict the output variables (Y) for that data.
Supervised learning can be further grouped into Regression and Classification tasks.
• Classification: A classification task is when the output variable is a category, such as “red” or “blue” or “disease” and “no disease”. A classification model attempts to draw some conclusion from observed values. Given one or more inputs a classification model will try to predict the value of one or more outcomes.
For example, when filtering emails “spam” or “not spam”, when looking at transaction data, “fraudulent”, or “authorized”.
In short, Classification either predicts categorical class labels or classifies data (construct a model) based on the training set and the values (class labels) in classifying attributes and uses it in classifying new data. There are a number of classification models. Classification models include logistic regression, decision tree, random forest, gradient-boosted tree, multilayer perceptron, one-vs-rest, and Naive Bayes.
- Regression: When the output is a continuous value, the task is a regression. For instance, a financial analyst may need to forecast the value of a stock based on a range of feature like equity, previous stock performances, macroeconomics index. The system will be trained to estimate the price of the stocks with the lowest possible error.
Un-Supervised Learning
In unsupervised learning, the training dataset doesn’t have well defined relationships and patterns laid out for program to learn.
The basis difference between the above-mentioned learnings is that for supervised learning, a portion of output dataset is provided to train the model, in order to generate the desired outputs. On the other hand, in unsupervised learning no such dataset is provided for learning, rather the data is clustered into classes. In unsupervised learning, an algorithm explores input data without being given an explicit output variable (e.g., explores customer demographic data to identify patterns).
Reinforced Learning
Reinforced learning involves learning and updating the parameters of model based on the feedback and errors of the output. Any dataset would be divided into two categories, training set and test set. The program is trained using the well-defined training dataset and is then fine-tuned using feedback from the results of test dataset.
To learn more about Reinforcement Learning, read
“What is Reinforcement Learning?”
