What is Machine Learning – An Introduction

What is Machine Learning?

Machine learning is a data analytics approach that trains computers to do what living creatures do innately: learn from experience. Machine learning algorithms employ computational methods to “learn” knowledge from raw data, rather than using a preconceived formula as a template. As a result, as they train repeatedly on the instances, they are able to discover patterns and make predictions about the future.

History of machine learning

Before we start with the history of ML in particular, it is important to note some important advances in computer science as a whole as well.

We begin in 1834, Charles Babbage, the inventor of the computer, devised a system that could be programmed using punch cards. This machine was never built but it is the logical structure of today’s computers. It took till 1940 to invent the first manually operated computer, “ENIAC,” which was the first electronic general-purpose computer.

In 1943, an electrical system was used to represent a biological cerebral network. In 1950, scientists began putting their theory to work, analyzing how human neurons might function.

Alan Turing presented a foundational paper on machine learning, “Computer Machinery and Intelligence,” in 1950. In his work, he posed the question, “Can machines think?”

Arthur Samuel, popularly known as the father of machine learning, invented software in 1952 that assisted the IBM machine in playing a checkers game. The more it played, the better it scored. He also went on to coin the term machine learning in 1959.

Terry Sejnowski and Charles Rosenberg devised NETtalk, a neural network capable of teaching itself how to properly speak 20,000 words in one week, in 1985.

AI Winter:

The years 1974 to 1980 were difficult for AI and ML researchers, and this period was dubbed “AI winter.” During this time, machine translation failed, and people’s interest in AI waned, resulting in lower government funding for researchers.

In 1997, IBM’s Deep Blue intelligent computer defeated chess master Garry Kasparov, becoming the first machine to defeat a human chess player.

In the year 2006, computer scientist Geoffrey Hinton renamed neural net research “deep learning,” and it has since become one of the most popular breakthroughs.Google created a

The “Eugen Goostman”, a chatbot passed the Turing Test in 2014. It was the first chatbot to persuaded the 33 percent of human judges that it was not a machine.

In 2017, Alphabet’s Jigsaw team created an intelligent system capable of learning online trolling. It used to take reading millions of comments on various websites to figure out how to combat internet trolling.

Machine Learning vs Deep Learning vs Artificial Intelligence:

Artificial intelligence (AI) is the study of the development of smart computers. Machine learning (ML) refers to systems that can learn from experience (training data), whereas deep learning refers to systems that can learn from experience on large data sets (DL). AI can be considered a subset of machine learning. Deep Learning (DL) is comparable to machine learning (ML), except it works better with large data sets.

The graphic below depicts the broad link between AI, ML, and DL:

Why Machine Learning?

Consumer demands and expectations are changing as the globe evolves. In addition, we are living through the fourth industrial revolution of data.

To gain insightful information from this data and learn from how individuals and groups interact with it, we need algorithms that can process the data and offer us findings that will help us in a variety of different ways.

Machine Learning has transformed sectors such as medicine, healthcare, manufacturing, banking, and many more. As a result, Machine Learning has become an indispensable component of the modern enterprise.

Data is powerful, and in order to harness the power of this data, along with the tremendous rise in processing power, Machine Learning has given a new dimension to the way we do things.

Machine Learning has applications all over the place. The electronic gadgets you use and the programs you use on a daily basis are driven by strong machine learning algorithms.

Furthermore, machine learning has aided in the automation of redundant operations, eliminating the need for manual labor. All of this is possible by the vast quantity of data you collect on a daily basis.

Working of Machine Learning

With the continuous growth of data, there is a need for systems that can manage this tremendous amount of information.

Machine Learning techniques, such as Deep Learning, enable the great majority of data to be handled with efficiency and accuracy.

Machine Learning has transformed how we view data and the many conclusions we may derive from it.

These machine learning algorithms accomplish categorization and future predictions by utilizing the patterns included in the training data. When the model gets fresh input, it uses its previously learned patterns to the new data in order to create future predictions. Depending on the ultimate accuracy, models may be optimized using a variety of standardized ways.

As a result, the Machine Learning model learns to adapt to new samples.

Features of Machine Learning:

• Machine learning makes use of data to find trends in given data.
• It can build on previous experience in order to improve on its own.
• Machine learning is quite similar to data mining in that it likewise deals with massive amounts of data.
• ML model is only as good as the data supplied to it.

Machine Learning Algorithms:

Machine Learning is vast. As a result, there are several approaches popping up all the time. ML Algorithms are mainly of 2 types as below:

1. Algorithm classification based on learning style.

This approach demonstrates how the ML algorithm operates when given specific input data to learn from. This also aids in the selection of the best model depending on the results.

2. Algorithm classification based on similarity.

Algorithm classification based on learning style.

1. Supervised learning

Supervised machine learning creates a model that makes predictions using evidence. If you have known data for the result you want to forecast, use supervised learning.

Supervised learning develops machine learning models through the use of classification and regression algorithms.

Classification techniques can anticipate distinct responses, such as whether an email is legitimate or spam, or if a tumor is malignant or benign. Input data is classified using classification models. Medical imaging, voice recognition, and credit scoring are examples of typical uses.

Some classification techniques:

• Support vector machine (SVM)
• Boosted and bagged decision trees
• K-nearest neighbor
• Naive Bayes
• Discriminant analysis
• Logistic regression

2. Unsupervised learning:

Unsupervised learning uncovers hidden patterns or intrinsic data structures. It is useful in generating conclusions from datasets that have input data but no labeled answers.

The most frequent unsupervised learning approach is clustering. It helps with exploratory data analysis in order to discover hidden patterns or groups in data. Cluster analysis has applications such as DNA sequence analysis, market research, and object identification.

The most common algorithms employing this concept:

• k-means and k-medoids
• hierarchical clustering
• Gaussian mixture models
• hidden Markov models
• self-organizing maps
• fuzzy c-means clustering
• subtractive clustering

3. Semi-Supervised Learning:

Semi-supervised learning is a hybrid of supervised and unsupervised learning techniques. It eliminates various drawbacks that arise in both supervised and unsupervised learning.

The downside of supervised learning is that the data must be manually labeled. This takes a long time and can be rather costly. Such activities also necessitate the use of ML engineers or professional data scientists.

Unsupervised learning has the drawback of producing less accurate outcomes. This is due to the fact that the data is not tagged and is also unknown.

These issues are eliminated by semi-supervised learning. In this case, the system trains on both labeled and unlabeled data. When compared to unlabeled data, labeled data constitutes a minor portion.

4. Reinforcement Learning:

Reinforcement Learning is the most recent and popular algorithm of Machine Learning. It is of use in a variety of autonomous systems, including automobiles and industrial robotics. This algorithm’s purpose is to achieve a goal in a changing environment. It will be able to achieve this aim thanks to the system’s many benefits.

This is most commonly employed in programming robots to do self-directed behaviors. It is also employed in the development of clever self-driving automobiles.

Consider the example of robotic navigation.

Furthermore, by experimenting with the agent in its surroundings further, we can enhance efficiency. This is the fundamental idea behind reinforcement learning.

A reinforcement learning model has comparable action sequences.

Algorithm classification based on learning style:

Some algorithms act on the principle of similarity in their capabilities.

1. Decision Tree Algorithms:

The input in trees is divided into sections based on specified factors. The leaves reflect the ultimate outcome, and the nodes show the moment at which the data is divided.

Splitting indicates that if there are two yes/no alternatives, only one outcome is available at any one moment. There are two kinds of trees: classification trees and regression trees. A classification tree is a yes/no structure. The data in the regression tree, on the other hand, is continuous.

2. Bayesian Algorithm:

It is self-evident that the Bayes theorem is present in all Bayesian approaches. The Bayes theorem is mostly about chance, and more specifically, conditional probability. It implies that if incident B has already occurred, event A will occur. The Naive Bayes theorem is the most well-known algorithm. There are various other algorithms like:

• Gaussian Naïve Bayes theorem
• Multinomial Naïve Bayes theorem
• Bayesian Belief Network

3. Clustering Algorithms:

This is an unsupervised learning strategy that is highly beneficial for data grouping. Clustering occurs when comparable sorts of data appear in a particular cluster. If the data is dissimilar, it is placed in a different group or cluster. The algorithms employed are as follows:

• K-means
• K-medians

Machine Learning Algorithms:

1. Linear Regression:

Regression models predict values depending on various factors that are reliant on several inputs.

Linear Regression is by far the most frequent type of regression in which there is a linear relationship or correlation between the predictor variable and the response variable.They predict stock prices and other time-series data.

2. Support Vector Machines:

Support Vector Machines, often known as SVMs, are machine learning algorithms that categorize a dataset into two groups or segments. SVMs apply to both linear and non-linear categorization. Using a hyperplane, an SVM classifier splits the data into two groups.

3. Association rule mining:

Association Rule Mining is a technique for discovering associations between variables in a database. It is a form of data mining approach that may uncover associations between various elements. It is useful in the sales industry.

4. Bayesian Networks:

The Bayesian Network is a variant on the Bayes theorem, which is the most significant component of probability theory. The conditional probability of an occurrence is calculated using the Bayes Theorem. This is the conditional probability of a known occurrence. The hypothesis is the measure of the likelihood. And we compute this likelihood using past evidence.

5. k Nearest Neighbor (kNN):

kNN is a form of machine learning algorithm that categorizes items in a dataset using the classes of their nearest neighbors. kNN predictions operate on the assumption that items close to each other are similar. To locate the closest neighbor, we use distance metrics such as Euclidean, city block, cosine, and Chebyshev.

6. Naive Bayes:

A naïve Bayes classifier assumes that all the features in a dataset are independent. That is, it assumes that no one class/category has any dependency on any other category/class. It aims to classify fresh data on the likelihood that it belongs to one of many categories.

7. Discriminant Analysis Ensembles:

The discriminant analysis assumes that distinct categories generate information with Gaussian distributions. Finding the parameters for a Gaussian distribution for each class is the first step in training a discriminant analysis model. Boundaries, which might be linear or quadratic functions, are calculated using distribution parameters. These limits serve as a template to categorize fresh data.

8. Gaussian Process Regression:

GPR models are nonparametric machine learning models designed to forecast the value of a continuous predictor variables.The response variable is represented as a Gaussian process with covariances to the input variables.In the subject of spatial analysis, these models are commonly employed for interpolation in the presence of uncertainty. Kriging is another name for GPR.

9. Logistic Regression:

Logistic regression is a model that predicts the likelihood of a binary answer falling into one of two categories. It is a starting point for binary classification issues because of its convenience.

10. Decision Trees:

Decision Trees are a sort of supervised machine learning method. These trees are mostly employed in predictive modeling. We build a decision tree that can make decisions depending on user input. Decision Trees serve to solve both regression and classification problems. These trees present the user with graphical results.

11. Artificial Neural Networks (ANNs) (ANN)

An Artificial Neural Network (ANN) is a more advanced machine learning technology. Because these neural networks are designed after the human nervous system, they are referred to as neural networks.

Several neurons are linked together to compute information. These neurons are able to form a combined probability distribution across the input variables because they capture the statistical structure. These neural networks excel in detecting patterns in massive datasets.

Neural Networks are capable of performing classification and regression tasks with great accuracy. Furthermore, they minimize the need for expensive statistical chores in pre-processing because they are highly capable of recognising patterns on their own.

Companies Using Machine Learning:

1. Google.com

Google is one of the world’s leading technology companies, and it has made significant advances in AI and machine learning. It has a number of cloud-based machine learning algorithms, which it also employs in its search engine. It recently created a chatbot to answer your questions.

2. Microsoft Inc.

Microsoft has one of the world’s most advanced AL and ML projects. It is conducting research on automated agriculture and water management using AI and ML. There are a lot of initiatives like this.

3. Nvidia

Nvidia is among the world’s biggest and most influential GPU manufacturers. It employs machine learning and data analytics to increase the quality of GPU processing in order to provide higher performance. GPUs are found in many gaming laptops and desktops, as well as certain I5 gen laptops.

4. Intel

Intel is among the largest global semiconductor and CPU manufacturers. Its primary purpose is to create quicker and more effective processors so that your computers function better. For higher-end processors, ML approaches are increasingly useful to improve computing efficiency.

Case study for rheumatic arthritis:

A blockbuster medicine is one that is extremely effective and earns at least $1 billion in yearly sales. Blockbusters treat prevalent conditions like diabetes, hypertension, many cancer kinds, and asthma. In most markets, there are several rival items.

The term “precision medicine” refers to a treatment for groups of people who share particular features and are receptive to that therapeutic intervention.

Machine Learning Applications:

With the growth of big data, machine learning has emerged as a critical tool for addressing issues in fields such as:

• Credit scoring and algorithmic trading are examples of computational finance.
• Computer vision and image analysis for face recognition, motion detection, and object detection
• Computational medicine helps to identify tumors, find drugs, and sequence DNA.
• Generation of energy for pricing and predictive modeling
• Predictive maintenance in automotive, aerospace, and manufacturing
• For speech processing applications, natural language processing is useful.

Summary:

In this machine learning tutorial, we covered what is machine learning, its fundamentals as well as how computational complexity has changed over time to support complex machine learning techniques. We looked through the types of machine learning and then over the many sorts of machine learning algorithms and then took a quick look at some of the most prominent ML applications.