Machine Learning is the part of computer science that gives computers the ability to learn without being explicitly programmed. It explores the study and construction of algorithms that can receive input data and use statistical analysis to predict an output value within a certain range. Machine learning is used in practice for predictive analytics, pattern recognition, classification or clustering analysis by providing software with the ability to learn from previous experience (data). The terms machine learning and AI are sometimes interchanged because of their similar characteristics; however, more precisely speaking, machine learning is only one of several sub-disciplines within Artificial intelligence. The best-known example of machine learning is Google's search algorithm, which adjusts its rankings based on user activity.
Machine learning is a field of computer science where we build algorithms that learn from data and make predictions. For example, we can train an algorithm to recognize human faces (a first-level machine learning task) and then use the same algorithm to identify specific individuals (a second-level machine learning task). This idea is called transfer learning. In this article, I will focus on supervised machine learning, that is, on algorithms that learn from labeled training data in order to make accurate predictions. We start by collecting training samples representing the phenomena we want to predict, called features or attributes. Then we create a model using these samples with some examples of correct answers, called labels. After this step, which consists of choosing a suitable mathematical expression based on the model features, we train the algorithm by adjusting its parameters. This process of training (testing) is repeated many times in order to get an accurate model.
Deep learning is a branch of Machine learning that uses multiple layers of models called neural networks to analyze data. With multiple layers, deep learning becomes progressively better at encoding abstractions in the data, making it useful for visual recognition tasks, natural language processing and speech recognition. It allows software to learn from experience by using training algorithms instead of being explicitly programmed. Deep learning algorithms are especially good at recognizing patterns in data without being taught with predefined characteristics or rules. An example of this is object detection, where AI software can recognize objects in images without having had any prior instruction on identifying these objects.
Machine learning is a very broad topic, it can be subdivided into different types of machine learning. This question on Quora has several good answers which I've summarized below. For more information, check out these resources:
5 Types of Machine Learning Algorithms - Elena Grewal's great blog post breaks down some common machine learning algorithms and discusses how they can be applied to different real-world problems. Explaining How Algorithms Learn from Data - for an in-depth explanation of some common machine learning algorithms (decision trees, support vector machines, Bayesian networks), this article by Jason Brownlee is fantastic.
There are 7 major steps that you should take to apply machine learning techniques to a new problem:
1. Define your problem 2. Collect your data 3. Explore and prepare your data 4. Choose and apply a model 5. Evaluate your results 6. Improve or iterate 7. Communicate your results.
For more information on how to go about performing these steps, please see How Machine Learning Algorithms Work.
Machine algorithms can be trained to make predictions with varying degrees of accuracy depending on how good the training set is and what kind of features were extracted from it (if any). In some cases, machine algorithms may be able-bodied enough that they can perform as well as humans - for example, the machine may be able to make a prediction by looking at a few features alone (such as an occupation predictor based on age, sex and geographical location).
On the other hand, there are some tasks which machines will never be able to do exactly like humans. An example of this is handwriting recognition. To perform this task well requires extensive training on lots of different datasets; given that one dataset only contains information about how letters look, it cannot distinguish between two very different characters (e.g., '1' vs 'l') even though these characters are easily distinguishable for us humans.
The accuracy of predictions made by machine algorithms varies greatly depending on how these algorithms have been trained and what kind of task they are trying to solve. In some cases, a trained model may be able to get close enough to an optimal accuracy without having been explicitly designed for its task. In other cases, accuracy varies significantly between models - e.g., one algorithm might not generalize well from training data because it has been overfitting but another algorithm could have learnt the same concepts with less effort using the same dataset.
It's important that you only compare algorithms on their performance on training data and test data - not just on their estimated accuracy. It would be statistically invalid to compare the estimated accuracies of different models because this means that you're likely comparing how these models performed on different amounts of training data.
Assume that you have two types of machine learning algorithm: Algorithm A and Algorithm B. You've trained both algorithms on exactly the same training set and measured their estimated accuracy; we'll call this value Estimated A and Estimated B respectively for both algorithms. If you train these algorithms further (and measure their performance again) you could get values like, say, estimated A = 85% and Estimated B = 88%. Is it fair to say that Algorithm B is better than Algorithm A? No! We need to know how these models would perform when given new test data - not just how they performed on the training data that we gave them in order to learn in the first place!
Let's say you've given both algorithms the same test data in addition to their training data. We'll call this test set Actual A and Actual B for Algorithm A and B respectively. Perhaps when trained with all of the original training set, Algorithm B out-performs Algorithm A on the new test data; Estimated B = 80% whereas Estimated A = 70%. Is it fair to conclude that Algorithm B is better than Algorithm A? No! If we already knew how well both models would perform on unseen (new) data then there would be no need to train them both in the first place; we could just go straight ahead and start using Algorithm B without wasting our time by training Algorithm A in the first place.
What we need to do is train both algorithms on the original training set, then test them both on the new test data - Actual A and Actual B for Algorithm A and B respectively. We'll call this cross-validation. This is when you take one or more test sets aside in order to compare different models without using any of these models' performance values to influence how they were trained (a better idea would be to only use a tiny fraction of your available training data to help choose which features are important but that's another story).
Machine Learning Introduction; It is a subset of artificial intelligence (AI). This technology focuses on data utilization and algorithms to help machines learn as humans do. The accuracy of machines is improving gradually. The most common real-life applications of machine learning include search engines, banking software, marketing tools, email filters, face detection, and voice recognition apps. This technology has numerous other applications that are still under the phase of development. In the future, we can expect machine learning to help us in unconventional ways.
In this article, we are going to discuss how machine learning can benefit us in our day-to-day life.
Given below are the most common real-life applications of machine learning. The listed Types of Machine Learning will help you understand the benefits of this technology.
This is one of the most commonly observed Examples of Machine Learning. Face detection is an example of image recognition. During this process, the machine identifies the distinctive facial features of a person and remembers them. The database containing this information serves as a data pool for the machine. Whenever a machine is given a task of face recognition, it tries to match the current information with already stored data.
It is a technology, which identifies spoken words and converts them into text. The process works by measuring the set of numbers that are representing the speech signals. The speech signals are also segmented through the different intensities that are found within distinctive time-frequency bands. This technology can also help with simple data entry. Speech recognition is a common application that works with voice searches, voice user interface, and many other options.
This is the most beneficial Technique of Machine Learning. With this technology, it is much easier for a medical practitioner to diagnose a disease. It also involves the identification of clinical parameters and their analysis. The early and most accurate diagnosis is the key to plan therapy. Practitioners can also monitor patient’s recovery with this application. This technology serves as a pioneer when we talk about the integration of technology into healthcare.
During this process, taking unstructured data and extracting the structured information out of it. For instance, blogs, articles, web pages, emails, are the data available. The search engine extracts the information and offers the suggested content. It involves sets of documents, which are taken as input and outputs of a required structured data.
It is also possible to utilize machine learning in the field of regression. This helps us to take advantage of machine learning principles and optimize the given parameters. With this technology, we can also lower the approximation error. As a result, it becomes easy to calculate the most accurate outcome. Function optimization is another application for machine learning. It allows us to select the inputs to find the closest outcome.
Yes! And this sentence can be true when you talk about a *human*, but it's not for a machine. In supervised machine learning, your algorithm learns from your data that you have manually labeled with what you want to predict. For example, if you want to recognize human faces in photos, then in order to do so, you will need a huge dataset containing photos of humans and their corresponding labels telling them apart. Once you have such a dataset in possession, your computer can how learn how to recognize faces in an automated way.
Machine learning is a huge area of research, but it can be made easier by decomposing its different fields into categories according to what you want to predict. This idea is called task abstraction. For example, if you want to use your machine learning algorithm for image recognition, then one category will deal with computer vision tasks, another category will focus on natural language processing tasks, etc. The most common task abstractions are shown below: *Image recognition*: classification (human face or not), detection (detecting this specific object among other objects) *Natural language processing*: classification (deciding whether two sentences mean the same thing), extraction (extracting a particular piece of information) *Time series analysis*: forecasting future values, finding correlations between variables
The most common task abstractions are shown above. There is however an additional abstraction that could be useful - function approximation. This category deals with tasks such as regression analysis, polynomial interpolation and exponential smoothing. These types of tasks can be used in business data mining to improve sales prediction or customer churn rate analysis, etc. As you will see later, this abstraction makes it possible to find similarities among different features and predict new ones if we have enough training samples. Other than these two main categories: computer vision and natural language processing, you can use supervised machine learning for a large variety of applications. For example, you can predict the best way to run your company or even plan your strategy by using decision trees or ensembles.
Supervised machine learning models need you to provide labeled training data. Unsupervised models do not. Instead, they draw conclusions by themselves based on the model features (for example, find patterns among them) or even create new features by modeling your data set - this is called feature engineering. For example, if you have a large number of samples representing customers' spending habits and you want to predict their future purchases, then unsupervised techniques will allow you to discover hidden buying patterns without requiring any labels. As I mentioned before, there are other ways besides supervised and unsupervised machine learning for your algorithm to learn from data. Reinforcement learning is one of these methods where your algorithm learns how well its actions performed according to the task's reward. For example, you can use reinforcement learning in games or robotics to instruct your robot how well it is progressing toward its goal.
Similar to supervised machine learning, there are two common task abstractions in unsupervised learning: *Clustering *: finding groups among different samples based on their attributes. Clustering algorithms separate input data into multiple groups without any supervision which means that these groups have no labels associated with them. This way, your algorithm learns by itself what makes different samples dissimilar and whether they belong to a particular group or not without requiring any manual labeling of training samples. For example, you might want to find clusters of similar customers if you are an online retailer *Association rule mining*: These tasks are used to identify relationships between different features in your data. Association rules allow you to find the probability of buying a new product given certain conditions (*if A or B happen, then it is more likely for customer X to buy C*). For example, if you have an e-commerce site and want to keep track of all your customers' purchases, you might want to find out if any products are frequently bought together.
In 2012, Gartner predicted that by the year 2020, there will be 40 times more information than what we have today. This is not an optimistic forecast for businesses because it means that they need to process much more data in much shorter timeframes. The question then arises - how can you use all this available data if it's not labeled? Enter semi-supervised learning. Semi-supervised machine learning differs from supervised and unsupervised learning because it requires much less supervision compared to the first two types of models. For example, zero or very little training labels might be required which makes it easier to use your resources effectively. Training samples must however remain relevant and representative for your task. In the e-commerce example I mentioned earlier, semi-supervised techniques can help you find hidden patterns among your purchase history. Your algorithm learns automatically what makes different customer groups dissimilar and separates them into clusters without spending too much time labeling training samples. You can then use these clusters to improve customer experience across all channels.
Like unsupervised machine learning, there are two common task abstractions in semi-supervised learning: *Density estimation*: modeling dependencies between variables using high dimensional data with few labeled samples. For example, if you want to understand which factors influence buying decisions, you might not have enough training labels but you still have access to the of multiple customers who bought or did not buy a product. You can then use your available data for this task by identifying relevant variables that are relevant to customers' decisions. *Clustering*: finding groups among different training samples based on their attributes with few labeled samples. For example, if you have access to five customers who bought products from your e-commerce site and want to find out which customer segments they belong to, then clustering algorithms can be used for this purpose without spending too much time labeling them.
Semi-supervised machine learning is a powerful technique because it lets you process larger amounts of data using fewer resources than unsupervised or supervised techniques while still achieving significant results. Let's say that you have a dataset of customer reviews from your online e-commerce site. If you would like to find similarities between the products these customers bought, then you can use semi-supervised algorithms to do that without spending too much time labeling training examples. In contrast, if you want to find groups on this same platform using unsupervised learning techniques (for example with K-means clustering), then you need to manually label each set of samples before running them through your algorithm. Since labeled data is scarce in both cases, semi-supervised machine learning approaches are the right tool for the job. Using techniques like *graph embedding*, *k-nearest neighbors* or *latent Dirichlet allocation*, your model will learn patterns and regularities in your data without spending too much time on the labeling process.
Our daily lives are surrounded by semi-supervised learning opportunities. You only need to think of simple things like automatic face detection on Facebook or Google Photos. Automated phone assistants like Siri, Alexa, Cortana and Bixby also belong to this category of models. They all use topic modeling techniques which combine unsupervised and supervised methods to create very accurate recommendation engines. This type of algorithm learns from their mistakes, so you can ask it again if something goes wrong during your interaction with it. It is very unlikely that you will be able to teach a computer how to exactly communicate with humans but with machine learning, you can teach it to get closer and closer.
Semi-supervised machine learning is a type of algorithm that does not require labeled training samples. You can apply this type of model if your data set has few labeled samples and large amounts of unlabeled ones. With fewer labels, your model will learn patterns and regularities in the dataset instead of specific values which makes this approach useful for many use cases like: *clustering*: dividing similar objects into different groups without requiring their characteristics to be exactly defined. For example, grouping customers according to their purchase history or finding similarities among different product review submissions are basic examples of clustering tasks where semi-supervised algorithms can do a great job *classification*: learning to predict the class of objects that were not labeled based on their similarity to existing ones.
Machine learning can help us develop a mechanism that would serve as a “Personal assistant” and help us to manage our lives. Besides, this technology can help us to introduce the best possible improvements into the transport system by relying on autonomous vehicles. Another application of machine learning is the advancement in security mechanisms. These and numerous other implications clearly indicate that how machine learning can be beneficial for our society. In addition, the healthcare system can also seek benefit through machine learning by offering accurate diagnostics and personalized treatment. Given below are the most common real-life applications of machine learning. The listed information will help you understand the benefits of this technology.
List of Common Machine Learning Algorithms
Here is the list of commonly used machine learning algorithms. These algorithms can be applied to almost any data problem:
9.Dimensionality Reduction Algorithms
10.Gradient Boosting algorithms
There are three types of machine learning models: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
To conclude, machine learning is a revolution in computing-based technology. It is a breakthrough, which is capable of bringing us closer to a more complex type of artificial intelligence. This can also help to improve our lives by integrating unique and innovative technology.