Machine learning has become one of the most important topics within development organizations that are looking for innovative ways to leverage data assets to help the business gain a new level of understanding. Why add machine learning into the mix? With the appropriate machine learning models, organizations have the ability to continually predict changes in the business so that they are best able to predict what’s next. As data is constantly added, the machine learning models ensure that the solution is constantly updated. The value is straightforward: If you use the most appropriate and constantly changing data sources in the context of machine learning, you have the opportunity to predict the future.

What Is Machine Learning? 

Machine learning is having a dramatic impact on the way software is designed so that it can keep pace with busi- ness change. Machine learning is so dramatic because it helps you use data to drive business rules and logic. How is this different? With traditional software development models, pro- grammers wrote logic based on the current state of the business and then added relevant data. However, business change has become the norm. It is virtually impossible to anticipate what changes will transform a market. The value of machine learning is that it allows you to continually learn from data and predict the future. This powerful set of algo- rithms and models are being used across industries to improve processes and gain insights into patterns and anomalies within data. But machine learning isn’t a solitary endeavor; it’s a team process that requires data scientists, data engineers, business analysts, and business leaders to collaborate. The power of machine learn- ing requires a collaboration so the focus is on solving business.

Iterative Learning Machine Learning

Iterative learning Machine Learning from data Machine learning enables models to train on data sets before being deployed. Some machine learning models are online and contin- uously adapt as new data is ingested. On the other hand, other models, called offline machine learning models, are derived from machine learning algorithms but, once deployed, do not change. This Iterative learning Machine Learning process of online models leads to an improvement in the types of associations that are made between data elements. Due to their complexity and size, these patterns and associations could have easily been overlooked by human observation. After a model has been trained, these models can be used in real time to learn from data.

Big Data

Defining Big Data Big data is any kind of data source that has at least one of four shared characteristics.
  1. Extremely large Volumes of data
  2. The ability to move that data at a high Velocity of speed
  3.  An ever-expanding Variety of data sources
  4.  Veracity so that data sources truly represent truth.

The Roles of Statistics and Data Mining with Machine Learning The disciplines of statistics, data mining, and machine learning all have a role in understanding data, describing the character- istics of a data set and finding relationships and patterns in that data to build a model. There is a great deal of overlap in how the techniques and tools of these disciplines are applied to solving business problems. Many of the widely used data mining and machine learning algo- rithms are rooted in classical statistical analysis. Data scientists combine technology backgrounds with expertise in statistics, data mining, and machine learning to use all disciplines in collabo- ration. Regardless of the combination of capabilities and tech- nology used to predict outcomes, having an understanding of the business problem, business goals, and subject matter expertise is essential. You can’t expect to get good results by focusing on the statistics alone without considering the business side.

Supervised learning

 Supervised learning typically begins with an established set of data and a certain understanding of how that data is classified. Supervised learning is intended to find patterns in data that can be applied to an analytics process. This data has labeled features that define the meaning of data. For example, there could be mil- lions of images of animals and include an explanation of what each animal is and then you can create a machine learning appli- cation that distinguishes one animal from another. By labeling this data about types of animals, you may have hundreds of cat- egories of different species. Because the attributes and the mean- ing of the data have been identified, it is well understood by the users that are training the modeled data so that it fits the details of the labels. When the label is continuous, it is a regression; when the data comes from a finite set of values, it known as classifica- tion. In essence, regression used for supervised learning helps you understand the correlation between variables. An example of supervised learning is weather forecasting. By using regression analysis, weather forecasting takes into account known historical weather patterns and the current conditions to provide a predic- tion on the weather. The algorithms are trained using preprocessed examples, and at this point, the performance of the algorithms is evaluated with test data. Occasionally, patterns that are identified in a subset of the data can’t be detected in the larger population of data. If the model is fit to only represent the patterns that exist in the training subset, you create a problem called overfitting. Overfit- ting means that your model is precisely tuned for your training data but may not be applicable for large sets of unknown data. To protect against overfitting, testing needs to be done against unforeseen or unknown labeled data. Using unforeseen data for the test set can help you evaluate the accuracy of the model in predicting outcomes and results. Supervised training models have broad applicability to a variety of business problems, including fraud detection, recommendation solutions, speech recognition, or risk analysis. Unsupervised learning Unsupervised learning is best suited when the problem requires a massive amount of data that is unlabeled. For example, social media applications, such as Twitter, Instagram, Snapchat, and 16 Machine Learning For Dummies, IBM Limited Edition These materials are © 2018 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited. so on all have large amounts of unlabeled data. Understand- ing the meaning behind this data requires algorithms that can begin to understand the meaning based on being able to classify the data based on the patterns or clusters it finds. Therefore, the supervised learning conducts an Iterative learning Machine Learning process of analyz- ing data without human intervention. Unsupervised learning is used with email spam-detecting technology. There are far too many variables in legitimate and spam emails for an analyst to flag unsolicited bulk email. Instead, machine learning classifiers based on clustering and association are applied in order to iden- tify unwanted email.

Unsupervised learning

Unsupervised learning algorithms segment data into groups of examples (clusters) or groups of features. The unlabeled data cre- ates the parameter values and classification of the data. In essence, this process adds labels to the data so that it becomes supervised. Unsupervised learning can determine the outcome when there is a massive amount of data. In this case, the developer doesn’t know the context of the data being analyzed, so labeling isn’t possible at this stage. Therefore, unsupervised learning can be used as the first step before passing the data to a supervised learning process. Unsupervised learning algorithms can help businesses under- stand large volumes of new, unlabeled data. Similarly to super- vised learning (see the preceding section), these algorithms look for patterns in the data; however, the difference is that the data is not already understood. For example, in healthcare, collecting huge amounts of data about a specific disease can help practitio- ners gain insights into the patterns of symptoms and relate those to outcomes from patients. It would take too much time to label all the data sources associated with a disease such as diabetes. Therefore, an unsupervised learning approach can help determine outcomes more quickly than a supervised learning approach. Reinforcement learning Reinforcement learning is a behavioral learning model. The algorithm receives feedback from the analysis of the data so the user is guided to the best outcome. Reinforcement learning dif- fers from other types of supervised learning because the system isn’t trained with the sample data set. Rather, the system learns through trial and error. Therefore, a sequence of successful deci- sions will result in the process being “reinforced” because it best These materials are © 2018 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited. One of the most common applications of reinforcement learn- ing is in robotics or game playing. Take the example of the need to train a robot to navigate a set of stairs. The robot changes its approach to navigating the terrain based on the outcome of its actions. When the robot falls, the data is recalibrated so the steps are navigated differently until the robot is trained by trial and error to understand how to climb stairs. In other words, the robot learns based on a successful sequence of actions. The learn- ing algorithm has to be able to discover an association between the goal of climbing stairs successfully without falling and the sequence of events that lead to the outcome. Reinforcement learning is also the algorithm that is being used for self-driving cars. In many ways, training a self-driving car is incredibly complex because there are so many potential obstacles. If all the cars on the road were autonomous, trial and error would be easier to overcome. However, in the real world, human drivers can often be unpredictable. Even with this complex scenario, the algorithm can be optimized over time to find ways to adapt to the state where actions are rewarded. One of the easiest ways to think about reinforcement learning is the way an animal is trained to take actions based on rewards. If the dog gets a treat every time he sits on command, he will take this action each time.