Machine Learning

UC Berkeley breaks out the learning system of a machine learning algorithm into three main parts.

• A Decision Process: In general, machine learning algorithms are used to make a prediction or classification. Based on some input data, which can be labeled or unlabeled, your algorithm will produce an estimate of a pattern in the data.
• An Error Function: An error function evaluates the prediction of the model. If there are known examples, an error function can make a comparison to assess the accuracy of the model.
• A Model Optimization Process: If the model can fit better to the data points in the training set, then weights are adjusted to reduce the discrepancy between the known example and the model estimate. The algorithm will repeat this “evaluate and optimize” process, updating weights autonomously until a threshold of accuracy has been met.  

Machine learning models fall into three primary categories.

• Supervised machine learning      

• Unsupervised machine learning

• Semi-supervised learning

A number of machine learning algorithms are commonly used. These include:

• Random forests
• Decision trees
• Clustering
• Logistic regression
• Linear regression
• Neural networks

Since deep learning and machine learning tend to be used interchangeably, it’s worth noting the nuances between the two. Machine learning, deep learning, and neural networks are all sub-fields of artificial intelligence. However, deep learning is actually a sub-field of machine learning, and neural networks are a sub-field of deep learning.

IBM Watson Machine Learning supports the machine learning lifecycle end to end. It is available in a range of offerings that let you build machine learning models wherever your data lives and deploy them anywhere in your hybrid multi-cloud environment. 

Perhaps the easiest way to think about artificial intelligence, machine learning, neural networks, and deep learning is to think of them like Russian nesting dolls. Each is essentially a component of the prior term.

That is, machine learning is a subfield of artificial intelligence. Deep learning is a subfield of machine learning, and neural networks make up the backbone of deep learning algorithms. In fact, it is the number of node layers, or depth, of neural networks that distinguishes a single neural network from a deep learning algorithm, which must have more than three.

There are numerous benefits for companies that are ready to embrace machine learning as part of their advertising efforts. Below, we have outlined five reasons organizations should implement AI as part of their strategy for attracting new customers and increasing ROI.

• Better personalization

• Better advertising decisions through machine learning and AI

• More personal interactions through 1:1 conversations

• Better creative based on data

• Improve performance without needing cookies.

IBM Watson Studio on IBM Cloud Pak for Data supports the end-to-end machine learning lifecycle on a data and AI platform. You can build, train and manage machine learning models wherever your data lives and deploy them anywhere in your hybrid multi-cloud environment.

Machine learning can apply data science to target and identify audience behavior, allowing for better segmentation. 41% (opens outside of ibm.com) of advertisers are using machine learning to deliver personalization at scale.  About 4 of 10 marketers are using AI to better leverage data for decision-making. Machine learning will continue to shape the industry, leading to better relationships with consumers and more successful campaign strategies.

Machine learning allows us to extract important insights from raw data, which allows us to quickly solve ‘data-rich’ business issues. Algorithms used for machine learning actually learn from the data inputted without being programmed to do so.