Data forms the main source of learning in Machine learning. The data that is being referenced here can be in any format, can be received at any frequency, and can be of any size. When it comes to handling large datasets in the Machine learning context, there are some new techniques that have evolved and are being experimented with. There are also more big data aspects, including parallel processing, distributed storage, and execution. More on the large-scale aspects of data will be covered in the next chapter, including some unique differentiators.

When we think of data, dimensions come to mind. To start with, we have rows and columns when it comes to structured and unstructured data. This book will cover handling both structured and unstructured data in the machine learning context. In this section, we will cover the terminology related to data within the Machine learning context.

Data in the Machine learning context can either be labeled or unlabeled. Before we go deeper into the Machine learning basics, you need to understand this categorization, and what data is used when, as this terminology will be used throughout this book.

Unlabeled data is usually the raw form of the data. It consists of samples of natural or human-created artifacts. This category of data is easily available in abundance. For example, video streams, audio, photos, and tweets among others. This form of data usually has no explanation of the meaning attached.

The unlabeled data becomes labeled data the moment a meaning is attached. Here, we are talking about attaching a "tag" or "label" that is required, and is mandatory, to interpret and define the relevance. For example, labels for a photo can be the details of what it contains, such as animal, tree, college, and so on, or, in the context of an audio file, a political meeting, a farewell party, and so on. More often, the labels are mapped or defined by humans and are significantly more expensive to obtain than the unlabeled raw data.

The learning models can be applied to both labeled and unlabeled data. We can derive more accurate models using a combination of labeled and unlabeled datasets. The following diagram represents labeled and unlabeled data. Both triangles and bigger circles represent labeled data and small circles represent unlabeled data.

The application of labeled and unlabeled data is discussed in more detail in the following sections. You will see that supervised learning adopts labeled data and unsupervised learning adopts unlabeled data. Semi-supervised learning and deep learning techniques apply a combination of labeled and unlabeled data in a variety of ways to build accurate models.

A task is a problem that the Machine learning algorithm is built to solve. It is important that we measure the performance on a task. The term "performance" in this context is nothing but the extent or confidence with which the problem is solved. Different algorithms when run on different datasets produce a different model. It is important that the models thus generated are not compared, and instead, the consistency of the results with different datasets and different models is measured.

After getting a clear understanding of the Machine learning problem at hand, the focus is on what data and algorithms are relevant or applicable. There are several algorithms available. These algorithms are either grouped by the learning subfields (such as supervised, unsupervised, reinforcement, semi-supervised, or deep) or the problem categories (such as Classification, Regression, Clustering or Optimization). These algorithms are applied iteratively on different datasets, and output models that evolve with new data are captured.

Models are central to any Machine learning implementation. A model describes data that is observed in a system. Models are the output of algorithms applied to a dataset. In many cases, these models are applied to new datasets that help the models learn new behavior and also predict them. There is a vast range of machine learning algorithms that can be applied to a given problem. At a very high level, models are categorized as the following:

Logical models

Logical models are more algorithmic in nature and help us derive a set of rules by running the algorithms iteratively. A Decision tree is one such example:

Geometric models

Geometric models use geometric concepts such as lines, planes, and distances. These models usually operate, or can operate, on high volumes of data. Usually, linear transformations help compare different Machine learning methods:

A model is said to be under-fitting when it doesn't take into consideration enough information to accurately model the actual data. For example, if only two points on an exponential curve are mapped, this possibly becomes a linear representation, but there could be a case where a pattern does not exist. In cases like these, we will see increasing errors and subsequently an inaccurate model. Also, in cases where the classifier is too rigid or is not complex enough, under-fitting is caused not just due to a lack of data, but can also be a result of incorrect modeling. For example, if the two classes form concentric circles and we try to fit a linear model, assuming they were linearly separable, this could potentially result in under-fitting.

The accuracy of the model is determined by a measure called "power" in the statistical world. If the dataset size is too small, we can never target an optimal solution.

This case is just the opposite of the under-fitting case explained before. While too small a sample is not appropriate to define an optimal solution, a large dataset also runs the risk of having the model over-fit the data. Over-fitting usually occurs when the statistical model describes noise instead of describing the relationships. Elaborating on the preceding example in this context, let's say we have 500,000 data points. If the model ends up catering to accommodate all 500,000 data points, this becomes over-fitting. This will in effect mean that the model is memorizing the data. This model works well as long as the dataset does not have points outside the curve. A model that is over-fit demonstrates poor performance as minor fluctuations in data tend to be exaggerated. The primary reason for over-fitting also could be that the criterion used to train the model is different from the criterion used to judge the efficacy of the model. In simple terms, if the model memorizes the training data rather than learning, this situation is seen to occur more often.

Now, in the process of mitigating the problem of under-fitting the data, by giving it more data, this can in itself be a risk and end up in over-fitting. Considering that more data can mean more complexity and noise, we could potentially end up with a solution model that fits the current data at hand and nothing else, which makes it unusable. In the following graph, with the increasing model complexity and errors, the conditions for over-fit and under-fit are pointed out:

Machine learning algorithms are usually robust to noise within the data. A problem will occur if the outliers are due to manual error or misinterpretation of the relevant data. This will result in a skewing of the data, which will ultimately end up in an incorrect model.

Therefore, there is a strong need to have a process to correct or handle human errors that can result in building an incorrect model.

Machine learning is meant to work with new data constantly coming into the system and learning from that data. Complexity will creep in when the new data entering the system comes in formats that are not supported by the machine learning system. It is now difficult to say if our models work well for the new data given the instability in the formats that we receive the data, unless there is a mechanism built to handle this.

Classification is a way to identify a grouping technique for a given dataset in such a way that depending on a value of the target or output attribute, the entire dataset can be qualified to belong to a class. This technique helps in identifying the data behavior patterns. This is, in short, a discrimination mechanism.

For example, a sales manager needs help in identifying a prospective customer and wants to determine whether it is worth spending the effort and time the customer demands. The key input for the manager is the customer's data, and this case is commonly referred to as Total Lifetime Value (TLV).

We take the data and start plotting blindly on a graph (as shown in the following graph) with the x axis representing the total items purchased and the y axis representing the total money spent (in multiples of hundreds of dollars). Now we define the criteria to determine, for example, whether a customer is good or bad. In the following graph, all the customers who spend more than 800 dollars in a single purchase are categorized as good customers (note that this is a hypothetical example or analysis).

Now when new customer data comes in, the sales manager can plot the new customers on this graph and based on which side they fall, predict whether the customer is likely to be good or bad.

In many cases, the data analyst is just given some data and is expected to unearth interesting patterns that may help derive intelligence. The main difference between this task and that of a classification is that in the classification problem, the business user specifies what he/she is looking for (a good customer or a bad customer, a success or a failure, and so on).

Let's now expand on the same example considered in the classification section. Here the patterns to classify the customers are identified without any target in mind or any prior classification, and unlike running a classification, the results may always not be the same (for example, depending on how the initial centroids are picked). An example modeling method for clustering is k-means clustering. More details on k-means clustering is covered in the next section and in detail in the following chapters.

In short, clustering is a classification analysis that does not start with a specific target in mind (good/bad, will buy/will not buy).

Similar to classification, forecasting or prediction is also about identifying the way things would happen in the future. This information is derived from past experience or knowledge. In some cases, there is not enough data, and there is a need to define the future through regression. Forecasting and prediction results are always presented along with the degree of uncertainty or probability. This classification of the problem type is also called rule extraction.

Let's take an example here, an agricultural scientist working on a new crop that she developed. As a trial, this seed was planted at various altitudes and the yield was computed. The requirement here is to predict the yield of the crop given the altitude details (and some more related data points). The relationship between yield gained and the altitude is determined by plotting a graph between the parameters. An equation is noted that fits most of the data points, and in cases where data does not fit the curve, we can get rid of the data. This technique is called regression.

In addition to all the techniques we defined until now, there might be situations where the data in context itself has many uncertainty. For example, an outsourcing manager is given a task and can estimate with experience that the task can be done by an identified team with certain skills in 2-4 hours.

Let's say the cost of input material may vary between $100-120 and the number of employees who come to work on any given day may be between 6 and 9. An analyst then estimates how much time the project might take. Solving such problems requires the simulation of a vast amount of alternatives.

Typically in forecasting, classification, and unsupervised learning, we are given data and we really do not know how the data is interconnected. There is no equation to describe one variable as a function of others.

Essentially, data scientists combine one or more of the preceding techniques to solve challenging problems, which are:

Optimization, in simple terms, is a mechanism to make something better or define a context for a solution that makes it the best.

Considering a production scenario, let's assume there are two machines that produce the desired product but one machine requires more energy for high speed in production and lower raw materials while the other requires higher raw materials and less energy to produce the same output in the same time. It is important to understand the patterns in the output based on the variation in inputs; a combination that gives the highest profits would probably be the one the production manager would want to know. You, as an analyst, need to identify the best possible way to distribute the production between the machines that gives him the highest profit.

The following image shows the point of highest profit when a graph was plotted for various distribution options between the two machines. Identifying this point is the goal of this technique.

Unlike the case of simulations where there is uncertainty associated with the input data, in optimization we not only have access to data, but also have the information on the dependencies and relationships between data attributes.

One of the key concepts in Machine learning is a process called induction. The following learning subfields use the induction process to build models. Inductive learning is a reasoning process that uses the results of one experiment to run the next set of experiments and iteratively evolve a model from specific information.

The following figure depicts various subfields of Machine learning. These subfields are one of the ways the machine learning algorithms are classified.

Supervised learning is all about operating to a known expectation and in this case, what needs to be analyzed from the data being defined. The input datasets in this context are also referred to as "labeled" datasets. Algorithms classified under this category focus on establishing a relationship between the input and output attributes, and use this relationship speculatively to generate an output for new input data points. In the preceding section, the example defined for the classification problem is also an example of supervised learning. Labeled data helps build reliable models but is usually expensive and limited.

When the input and output attributes of the data are known, the key in supervised learning is the mapping between the inputs to outputs. There are quite a few examples of these mappings, but the complicated function that links up the input and output attributes is not known. A supervised learning algorithm takes care of this linking, and given a large dataset of input/output pairs, these functions help predict the output for any new input value.

In some of the learning problems, we do not have any specific target in mind to solve. In the earlier section, we discussed clustering, which is a classification analyses where we do not start with a specific target in mind (good/bad, will buy/will not buy) and is hence referred to as unsupervised analyses or learning. The goal in this case is to decipher the structure in the data against the build mapping between input and output attributes of data and, in fact, the output attributes are not defined. These learning algorithms operate on an "unlabeled" dataset for this reason.

Semi-supervised learning is about using both labeled and unlabeled data to learn models better. It is important that there are appropriate assumptions for the unlabeled data and any inappropriate assumptions can invalidate the model. Semi-supervised learning gets its motivation from the human way of learning.

Reinforcement learning is learning that focuses on maximizing the rewards from the result. For example, while teaching toddlers new habits, rewarding them every time they follow instructions works very well. In fact, they figure out what behavior helps them earn rewards. This is reinforcement learning, and it is also called credit assessment learning.

The most important thing is that in reinforcement learning the model is additionally responsible for making decisions for which a periodic reward is received. The results in this case, unlike supervised learning, are not immediate and may require a sequence of steps to be executed before the final result is seen. Ideally, the algorithm will generate a sequence of decisions that helps achieve the highest reward or utility.

The goal in this learning technique is to measure the trade-offs effectively by exploring and exploiting the data. For example, when a person has to travel from a point A to point B, there will be many ways that include travelling by air, water, road or by walking, and there is significant value in considering this data by measuring the trade-offs for each of these options. Another important aspect is the significance of a delay in the rewards. How would this affect learning? For example, in games like chess, any delay in reward identification may change the result.

Deep learning is an area of Machine learning that focuses on unifying Machine learning with artificial intelligence. In terms of the relationship with artificial neural networks, this field is more of an advancement to artificial neural networks that work on large amounts of common data to derive practical insights. It deals with building more complex neural networks to solve problems classified under semi-supervised learning and operates on datasets that have little labeled data. Some Deep learning techniques are listed as follows: