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ML Research and Applications

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[Different Machine Learning Models - Deepika Yadav]


Machine Learning:

Discovering the New Era of Intelligence



- Overview

Machine learning (ML) is complex, and how it works varies depending on the task and the algorithm used to accomplish it. However, at its core, a ML model is a computer that looks at datal and recognizes patterns, then uses those insights to better complete an assigned task. 

Any task that relies on a set of data points or rules can be automated using ML, even those that are more complex, such as responding to customer service calls and reviewing resumes. 

For example, let’s say a machine has to predict whether a customer will buy a specific product this year (e.g., “antivirus”). The machine does this by looking at the previous knowledge/past experience i.e. the data of the products the customer purchased every year, if he buys anti-virus software every year then there is a high chance that the customer will buy anti-virus software this year as well. 

This is how ML works on a basic conceptual level. If a machine's performance in a given task improves with experience, the machine is said to be learning certain classes of tasks from past experience (data input). 

The four main machine learning models are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

Please refer to the following for more details:  


- Machine Learning (ML) Today

Machine learning (ML) today is a data analysis method that automates the construction of analytical models. It is a branch of artificial intelligence (AI) based on the idea that systems can learn from data, recognize patterns and make decisions with minimal human intervention. While AI is the broad science of imitating human capabilities, ML is a specific subset of AI that trains machines how to learn.

ML is something that performs functions using the data fed to it and gradually gets better over time. ML and Statistics have a large overlap, as both fields study data analysis. But unlike statistics, researchers focus on the algorithmic complexity of ML and computational implementation. Part of ML research is the development of tractable approximate inference algorithms.

ML today is different from ML of the past due to new computing techniques. It was born out of pattern recognition and the theory that computers could learn without being programmed to perform specific tasks; researchers interested in AL wanted to see if computers could learn from data.

The iterative aspect of ML is important because models are able to adapt independently as they are exposed to new data. They learn from previous calculations to produce reliable, repeatable decisions and results. It's not a new science, it's one that's gaining new momentum


- The Evolution of Machine Learning (ML) 

Biological evolution Inspires ML. Evolution allows life to explore almost limitless diversity and complexity. Scientists hope to recreate such open-endedness in the laboratory or in computer simulations, but even sophisticated computational techniques like ML and AI can't provide the open-ended tinkering associated with evolution.

The earliest computers were designed to perform complex calculations, and their architecture allowed for the storage of not only data but also the instructions as to how to manipulate that data. This evolved to the point where the computer processed data according to a structure model of the real world, expressible in mathematical terms. The computer did not learn but was merely following instructions. 

The next step was to create a set of instructions that would allow the computer to learn from experience, i.e., to extract its own rules from large amounts of data and use those rules for classification and prediction. This was the beginning of ML and has led to the field that is collectively defined as AI. 

A major breakthrough came with the implementation of algorithms that were loosely modeled on brain architecture, with multiple interconnecting units sharing weighted puts among them, organized in computational layers (deep learning). 

- Evolution over the Years

ML techniques have been around since 1952. It has changed dramatically over the past decade and went through several transition periods in the mid-90s. Data-driven ML methods emerged in the 1990s. 

From 1995 to 2005, there was a lot of focus on natural language, search, and information retrieval. In those days, ML tools were more straightforward than those used today. 

Popularized in the 80s, neural networks are a subset of ML, which are computer systems modeled after the human brain and nervous system. Neural networks started making a comeback around 2005. It has become one of the trending technologies of the current decade.

Does ML require coding? Yes, ML requires a programming language. First, understanding ML involves algorithms. Mathematics is a required course for learning the concept of algorithms. But when you're implementing ML to solve real-world problems, you do need to code. Python and R are the programming languages of choice in AI and data science.


- ML Techniques

The entire idea behind ML is to go from data to insight. From a given problem (by large business one) to an adequate solution. The ML algorithms help in predicting future trends, changes, and opportunities. However, large datasets are essential in this task. To harness them, data scientists use several machine learning techniques and methods.

Although the intention behind ML is to work without human assistance, to some extent, this assistance is indispensable. To put it in plain language, you have to teach your algorithm how it should work and what it ought to look for. This is exactly what the data scientists do. Does it sound familiar to you? It should! This is how humans learn–from experience. 

The ML algorithms use computational methods to “learn” information directly from available data. This is why it is crucial to input as much relevant data as it’s available. As the number of samples increases, the ML algorithm works more and more efficiently. 

ML is a type of machine intelligence that uses algorithms to learn from data and improve performance on a specific task. ML techniques include:

  • Supervised learning: Trains a model on known input and output data so that it can predict future outputs.
  • Unsupervised learning: Finds hidden patterns or intrinsic structures in input data.
  • Deep learning: A ML technique that focuses on hierarchical learning of representations, typically through neural networks.
  • Clustering: An unsupervised learning method that involves the grouping of data points.
  • Dimensionality reduction: A technique used in ML and data science to reduce the number of features or dimensions in a dataset.
  • Linear regression: A ML algorithm that attempts to model the linear relationship between one or more predictor variables and a continuous target variable.
  • Decision tree: A ML technique that involves using certain conditions or rules for the decision-making process
  • Ensemble methods: A ML technique that combines multiple base models/weak learners to create an optimal predictive model


- Supervised Learning – Task Driven (Predict Next Value)

Supervised Learning is the most popular paradigm for performing machine learning operations. It is widely used for data where there is a precise mapping between input-output data. 

The dataset, in this case, is labeled, meaning that the algorithm identifies the features explicitly and carries out predictions or classification accordingly. 

As the training period progresses, the algorithm is able to identify the relationships between the two variables such that we can predict a new outcome. 

Supervised learning is for example used to classify email as spam or non-spam and to detect fraud.


- Unsupervised Learning – Data Driven (Identify Clusters)

In the case of unsupervised learning algorithm, the data is not explicitly labeled into different classes, that is, there are no labels. The model is able to learn from the data by finding implicit patterns. 

Unsupervised Learning algorithms identify the data based on their densities, structures, similar segments, and other similar features. Unsupervised Learning Algorithms are based on Hebbian Learning. 

Unsupervised learning is where you’ll hear most of the excitement when people talk about ‘the future of AI’ due to its limitless potential. It’s for example used for market segmentation (i.e. clustering groups of customers based on common characteristics) and to provide product recommendations based on a shopper’s historical purchase behavior. 


- Reinforcement Learning – Learn from Mistakes

Reinforcement Learning covers more area of Artificial Intelligence which allows machines to interact with their dynamic environment in order to reach their goals. 

With this, machines and software agents are able to evaluate the ideal behavior in a specific context. With the help of this reward feedback, agents are able to learn the behavior and improve it in the longer run. This simple feedback reward is known as a reinforcement signal. 

The agent in the environment is required to take actions that are based on the current state. This type of learning is different from Supervised Learning in the sense that the training data in the former has output mapping provided such that the model is capable of learning the correct answer. Whereas, in the case of reinforcement learning, there is no answer key provided to the agent when they have to perform a particular task. 

When there is no training dataset, it learns from its own experience. The goal is find the best actions which maximize the long-term reward. The algorithm thus learns by trial and error. An example is learning to play a computer game by playing against an opponent.  

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[Central Park, New York City]

- ML Algorithms and Applications

While many ML algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. 

In the last few years, we have witnessed a renaissance in ML and AI. AI broadly refers to the ability of machines to "think" like humans and perform tasks considered "smart," without explicitly being programmed to do so. 

ML is a subset of AI. ML algorithms build a mathematical model based on training data, and they leverage the model to make predictions when new data is provided. For example, a computer-vision ML model can be trained with millions of sample images that have been labeled by humans so that it can automatically classify objects in a new image. 

ML is about building intelligent artifacts, almost by necessity, typically, that learn over time based on experience. ML uses programming through neural networks. This is where ML “learns” through training algorithms and determines the probable outcome of a situation. The process requires a human to program the information into the ML with data, hours of training and testing and fixing issues in the outcomes. 

The important thing to remember with ML is that it can only output what is input based on the large sets of data it is given. It can only check from what knowledge it has been “taught.” If that information is not available, it cannot create an outcome on its own. Therefore ML will go for the solution whether or not it is the most optimal solution. 

As many people have wisely observed, the dream of ML is not new. It has been around since the very earliest days of computing. Pioneers have always imagined ways to build intelligent learning machines. ML is one of the most disruptive technologies of the 21st century. In the coming years, we are likely to see more advanced applications that stretch its capabilities to unimaginable levels. 

Examples of ML and DL are everywhere. It's how Netflix knows which show you’ll want to watch next, how Facebook knows whose face is in a photo, what makes self-driving cars a reality, and how a customer service representative will know if you'll be satisfied with their support before you even take a customer satisfaction survey.


- Data Science and Machine Learning Models

Big data acts as an ingredient. Think of it as when you are making a cake – the data represents the flour and the actual process of baking the cake is represented through ML. AI will then be the output, or the cake if you will. 

Machine learning (ML) is a form of AI that enables a system to learn from data rather than through explicit programming. However, machine learning is not a simple process. As the algorithms ingest training data, it is then possible to produce more precise models based on that data. 

A ML model is the output generated when you train your machine-learning algorithm with data. After training, when you provide a model with an input, you will be given an output. For example, a predictive algorithm will create a predictive model. Then, when you provide the predictive model with data, you will receive a prediction based on the data that trained the model.

ML enables models to train on data sets before being deployed. Some ML models are online and continuous. This iterative process of online models leads to an improvement in the types of associations 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, it can be used in real time to learn from data. The improvements in accuracy are a result of the training process and automation that are part of machine learning.  

ML techniques are required to improve the accuracy of predictive models. Depending on the nature of the business problem being addressed, there are different approaches based on the type and volume of the data. 


[More to come ...]

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