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Machine Learning – Revisited

Machine Learning – Revisited

Our introduction to Machine Learning (click here) Blog

Machine Learning is a concept of allowing machines to learn from examples and experience. So, what you do is feed data into a generic algorithm, and it builds the logic based on the given data. Arthur Samuel (1959) uttered:

“It is a field of study that gives the computers the ability to learn without being explicitly programmed.

The process of learning begins with observations or data, such as examples, direct experience in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly. It is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience.

Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. It explores the construction of algorithms that can learn from and make predictions on data.  Such algorithms overcome having to follow strictly static program instructions by making data-driven predictions or decisions through building a model from sample inputs. This study is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders, and computer vision.

Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks, it may also require additional time and resources to train it properly. Combining machine learning with AI and cognitive technologies can make it even more effective in processing large volumes of information.

Some machine learning methods

Machine learning algorithms are often categorized as supervised or unsupervised.

  • Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.
  • Unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised Learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
  • Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training – typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources in order to train it. Otherwise, acquiring unlabeled data generally doesn’t require additional resources.
  • Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.

There is another categorization when one considers the desired outputs:

  • In classification, inputs are divided into two or more classes, and the learner must produce a model that assigns unseen inputs to one or more (multi-label classification) of these classes. This is typically tackled in a supervised way.

Example:

Spam filtering is an example of classification, where the inputs are email (or other) messages and the classes are “spam” and “not spam”.

  • In regression, also a supervised problem, the outputs are continuous rather than discrete.
  • In clustering, a set of inputs is to be divided into groups. Unlike in classification, the groups are not known beforehand, making this typically an unsupervised task.
  • Density estimation finds the distribution of inputs in some space.
  • Dimensionality reduction simplifies inputs by mapping them into a lower-dimensional space. Topic modeling is a related problem, where a program is given a list of human language documents and is tasked to find out which documents cover similar topics.

REAL-LIFE SCENARIO:

Did you ever get a call from any bank or finance company asking you to secure a loan or an insurance policy? What do you think, do they call everyone? No, they call only a few selected customers who they think will purchase their product. How do they select you? This is target marketing and can be applied using Clustering. This is machine learning.

Evolution of Machines

As you know, we are living in the world of humans and machines. The Humans have been evolving and learning from their past experience for many years. But, the era of machines and robots have just begun. You can consider it in a way that currently we are living in the primitive age of machines, while the future of machines is enormous and is beyond our scope of imagination.

In today’s world, these machines or robots have to be programmed before they start following your instructions. But what if the machine started learning on their own from their experience, work like us, feel like us, do things more accurately than us? These things sound fascinating, Right? Well, just remember this is just the beginning of the new era.

Examples of machine learning

Machine learning is being used in a wide range of applications today. One of the most well-known examples is Facebook’s News Feed. The News Feed uses machine learning to personalize each member’s feed. If a member frequently stops scrolling to read or like a particular friend’s posts, the News Feed will start to show more of that friend’s activity earlier in the feed. Behind the scenes, the software is simply using statistical analysis and predictive analytics to identify patterns in the user’s data and use those patterns to populate the News Feed. Should the member no longer stop to read, like or comment on the friend’s posts, that new data will be included in the data set and the News Feed will adjust accordingly.

Machine learning is also entering an array of enterprise applications. Customer Relationship Management (CRM) systems use learning models to analyze email and prompt sales team members to respond to the most important messages first. More advanced systems can even recommend potentially more effective responses. Business intelligence (BI) and analytics vendors use machine learning in their software to help users automatically identify the most important data points. Human resource (HR) systems use learning models to identify characteristics of effective employees and rely on this knowledge to find the best applicants for open positions.

Machine learning also plays an important role in self-driving cars (autonomous cars). Deep learning neural networks are used to identify objects and determine optimal actions for safely steering

Types of machine learning algorithms

Just as there are nearly limitless uses of machine learning, there is no shortage of machine learning algorithms. They range from the fairly simple to the highly complex. Here are a few of the most commonly used models:

  • This class of machine learning algorithm involves identifying a correlation — generally between two variables — and using that correlation to make predictions about future data points.
  • Decision trees. These models use observations about certain actions and identify an optimal path for arriving at a desired outcome.
  • K-means clustering. This model groups a specified number of data points into a specific number of groupings based on like characteristics.
  • Neural networks. These deep learning models utilize large amounts of training data to identify correlations between many variables to learn to process incoming data in the future.
  • Reinforcement learning. This area of deep learning involves models iterating over many attempts to complete a process. Steps that produce favorable outcomes are rewarded and steps that produce undesired outcomes are penalized until the algorithm learns the optimal process.

The future of machine learning

While machine learning algorithms have been around for decades, they’ve attained new popularity as artificial intelligence (AI) has grown in prominence. Deep learning models in particular power today’s most advanced AI applications.

Machine learning platforms are among enterprise technology’s most competitive realms, with most major vendors, including Amazon, Google, Microsoft, IBM and others, racing to sign customers up for platform services that cover the spectrum of machine learning activities, including data collection, data preparation, model building, training and application deployment. As machine learning continues to increase in importance to business operations and AI becomes ever more practical in enterprise settings, the machine learning platform wars will only intensify.

Continued research into deep learning and AI is increasingly focused on developing more general applications. Today’s AI models require extensive training in order to produce an algorithm that is highly optimized to perform one task. But some researchers are exploring ways to make models more flexible and able to apply context learned from one task to future tasks.

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