What Is Machine Learning: The Rise of Intelligent Machines

We are currently witnessing the dawn of artificial intelligence as this new revolutionary technology’s impact spans across industries. From automation to complex decision-making, artificial intelligence brings new possibilities and shapes a future where machines intelligently collaborate with humans for unprecedented advancement. But how is this possible? Have you ever wondered how AI differentiates between a cat and a dog? How can AI mimic human intelligence? This is all done through machine learning. 

This rapid change in the technological era urges the need to understand how these machines work. So long are the days of traditional computations. The new dawn of technological advancement is upon us. This is why we’ve provided this article that gives you a full guide on how machine learning works, its applications, its types, and the tools used in the field. 

What Is Machine Learning?

Machine learning is a subset of artificial intelligence (AI), often abbreviated as ML, that involves the development of algorithms and models that enable computers to learn and make predictions based on past datasets. This means that machine learning enables computers to make decisions without someone programming them to do so. 

To make it simpler, let’s make an example. When a human child first encounters a hot object, his brain will ultimately deduct that touching a hot object will hurt. This way, the brain will learn from past experiences not to touch anything of high temperature. The same way goes for computers using machine learning. If you feed a computer a substantial set of data, let’s say hundreds of thousands of pictures of a Pomeranian dog, the computer will be able to later detect this breed among other breeds without being programmed to do so. 

Another example would be social media machine learning algorithms. If you think that social media apps are spying on you to recommend specific ads tailored to you, you’re kind of right. Social media apps leverage machine learning algorithms to collect data like clicks and searches, look for patterns, and adjust program actions accordingly. 

Machine learning enables computers to spot patterns and make judgments with little human assistance. The idea is to have machines be more human-like by giving them the ability to develop their own program. As long as you feed the machine data, the learning process automates and improves. How do you train a machine? By using different kinds of algorithms. 

Traditional Programming vs. Machine Learning

What differentiates machine learning from traditional programming is that the latter uses explicit instructions to dictate behavior based on predefined rules. However, machine learning enables computers to accomplish the task based on the data it’s being fed, not instructions. This means computers powered by machine learning have to figure out how to accomplish a specific task. 

For example, if you program a specific computer to detect a specific picture of a Pomeranian dog, it will accomplish this task. However, computers that use machine learning algorithms can detect the picture of the dog and label it as a “Pomeranian” without previously knowing the picture. This is because the computer was fed a large dataset of what a Pomeranian dog would look like. 

In a technical scope, in traditional programming, you feed the input data and a well-written program into a machine that generates an output. In machine learning, however, both the input and output data are fed into the machine and it works out the program for itself. 

ML vs. AI vs. DL

Before we continue, let’s point out some key differences between artificial intelligence, machine learning, and deep learning. People often confuse these terms and think that they are interchangeable, however, they differ from one another. 

For starters, Artificial intelligence is the broad concept that encompasses machines behaving intelligently and human-like. AI aims to create systems that simulate human cognition such as reasoning, problem-solving, and learning from experience. Since Artificial intelligence is a broad term, it includes machine learning and deep learning-based programs. 

Machine learning, as discussed before, is a subset of artificial intelligence. It enables machines to learn from data patterns and improve their performance without being programmed. Machine learning depends on structured data and labeled data for making predictions. This means that ML features of the input data given to the computer have to be organized into tables. 

Deep learning is a subfield of machine learning that leverages neural network algorithms, often abbreviated as DL. Artificial neural networks are made of interconnected nodes that mimic the human brain structure. Unlike machine learning, deep learning leverages unstructured data. DL algorithms can process text and images as they automate feature extraction. That means, instead of manually establishing the features of a Pomeranian dog such as “ears” and “eye shape”, deep learning algorithms can do so with no human input. 

So, in short, AI aims for computers to have intelligent behavior, ML leverages previous data for learning and decision-making, and DL focuses on complex pattern recognition using neural networks. 

History of Machine Learning

Before we jump into the intricacies of machine learning, let’s see how ML developed over the years. We didn’t just suddenly get from zero to self-driving cars, healthcare diagnosis, and natural language processing (NLP). Believe it or not, machine learning goes back to 1943 when neurophysiologist Warren McCulloch and mathematician Walter Pitts created neural networks. 

In 1950, mathematician Alan Turing introduced the concept of the Turing Test which evaluated a machine’s ability to exhibit intelligent behavior. Two years later in 1952, Arthur Samuel developed a computer that could learn as it played the game of checkers. It was later in 1955 that John McCarthy coined the term “Artificial Intelligence” and marked the beginning of AI research. 

In 1957, Frank Rosenblatt designed the first ever neural network, the “perceptron”, which was made to receive visual inputs such as images and generate outputs such as labels. From the 1950s to the 1990s, machine learning witnessed various developments such as the Nearest Neighbor algorithm that allowed computers to recognize patterns, explanation-based learning, and parallel distributed processing.

It was not until the 1990s that machine learning witnessed a radical shift from knowledge-driven techniques to data-driven ones due to the availability of huge volumes of data thanks to the internet. In 1996, IBM’s Deep Blue became the first machine to defeat chess grandmaster Garry Kasparov. 

In the 2010s, the technology boomed as Google Brain learned to recognize cats in YouTube videos. Some of the most prominent developments in machine learning since then are Google’s AlphaGo, LipNet, Facebook’s Deepface, Google’s Sibyl, and Elon Musk’s Open AI. 

The Importance of Machine Learning

So, why is it important to learn about machine learning and how it works? British mathematician Clive Humby declared in 2006 that “data is the new oil.” Just like oil, data is useless in its raw state. It needs refining and processing. And machine learning is the engine that processes these data. 

First, machine learning can handle large volumes of data. In the era of social media, traditional data analysis has become inadequate to handle and process data. However, Machine learning algorithms have the capability to analyze extensive datasets, reveal concealed patterns, and furnish valuable insights, contributing to informed decision-making processes. 

Second, machine learning is a vital aspect of automation. By learning from data and improving upon time machine learning algorithms can execute tasks that were once manual, allowing humans to dedicate their efforts to intricate and creative tasks. 

How Does ML Work?

This section will go over how machine learning algorithms work. We will go over the life cycle of a machine learning project from gathering data to making a prediction by using the Apple or Orange example. 

Terminology

Before we jump into the example, let’s familiarize ourselves with some machine learning terminology so you can grasp the concept clearly. 

• Model: A machine learning algorithm and the training of data consist of the machine learning model. So, A machine learning model is a mathematical representation or algorithm that learns patterns from data and makes predictions or decisions.

• Feature: It’s the measurable property or parameter of the dataset. For example, color is a feature. 

• Feature Vector: A feature vector is a numerical representation of data attributes used as input for machine learning algorithms.

• Training: The machine learning algorithm takes the “training data” as input. Then, the algorithm finds patterns in the input and trains the model accordingly. The results are the output of the training process.

• Prediction: Machine learning models can be fed input data to produce a predicted output. 

• Target (Label): This is the value that the machine has to predict. 

Framework of Machine Learning Process

Okay, so let’s train a model to detect apple images from orange ones. There are many things you can we can collect data on, for example, shape, color, texture, weight, etc. To keep this short and simple, we will only pick two features: color and texture. 

#1. Gathering Data

The first step of the machine learning process is gathering enough data to train the model. The more data one gathers, the more the model will be accurate. The data here will be the color and texture of each fruit. The table below shows the data gathered and which feature relates to which label: Apple or Orange. (Color here is measured in wavelength). 

#2. Preparing Data

After gathering enough data for our machine learning model, we have to prepare data for use in machine learning training. Here we split the collected data into two parts: Training Data and Test Data. 

• Training Data: This set of data is used to train the model to recognize patterns. The training data is usually larger than the testing data since we have to feed the model with as much data as possible.
• Test Data: This is the unseen data by the model. After finishing the training step, the model has to be tested with new data. If you feed the model the same data used in training, the model will answer correctly since it already knows it. To accurately test the model, new unseen data have to be used. 

We also need to make sure that there are no data imbalances. For example, if we feed the model more data about oranges than apples, the model will be biased towards guessing that everything is oranges. In addition, some data needs adjusting and manipulation such as de-duping, normalization, error correction, and more. 

#3. Choosing a Model

The next step would be choosing the right model for training the dataset. Researchers and data scientists have developed many machine learning models with each suited for a particular task. For example, there are models suited for tasks like speech recognition, image recognition, and text or numerical inputs. There are many types of machine learning models that we’ll cover later in the article, for now, we’ll go for a simple linear regression model since we only have two variables: color and textures. 

#4. Training the Model

This step is where the bulk load of “training” data happens. The training data is used to improve the model’s ability to predict whether a given fruit is an Orange or an Apple. To better explain this step, we might go into the mathematics behind it. Relax, it won’t be anything complex, just for us to visualize what we’re talking about. Since we picked a linear model, consider the equation below: 

Let’s dissect it:

x:  The input data (for example a certain image of an orange). 

y: The output or the prediction of the equation based on what x is. 

m: The slope of the line. M represents the “Weight” of the different features of the outputs. In this case, we only have 2 features. The coefficients for these 2 features are therefore called the weights. The m values are usually formed into a matrix.

b: The constant or y-intercept that is known as the bias of the model which is also grouped into a matrix. 

It might seem a bit complex, but for now, you only need to know that we have one constant and two variables. The only two variables we can adjust are the m and b. The process of training the machine learning model is to pick random values for them and provide inputs. The initial outputs are compared to the target output (which at first would be inaccurate), and then the differences are minimized by trying different values for weights and biases until the model reaches accuracy. 

As you see, the process goes through trial and error until reaching the correct predictions. This could be compared to driving a car. The more you train, the smoother your driving skills will get. Each time we feed the machine learning model training data and adjust the variables, the closer the model will be to an ideal separation between Oranges and Apples. 

#5. Evaluating the Model

This step is where we place the machine learning model to the test. This is done by using the testing data that we placed aside. Here, the model would be tested with never-before-seen data of oranges and apples and it’s up to it to make a correct prediction. This step is crucial to evaluate the performance of the model for real-world applications. If the model fails or doesn’t showcase satisfactory results, the previous step has to be revisited so that the cause of underperforming can be identified, and the training starts all over again. 

#6. Parameter Tuning

After successfully evaluating the machine learning model, the next step would be parameter tuning or hyperparameter tuning. This step further improves upon the positive results shown in the evaluation process. But why further improve if it’s successfully hitting the target? This would be due to the competitive nature of machine learning-based service providers. You’d want your model to be the best of the best, and parameter tuning is the right step to do so. 

One way to improve the model is by revisiting the training step and giving the model the full training dataset multiple times instead of once. This can generate higher accuracy levels. Another way is refining the initial values given to the model. There are many other parameters to define but their adjustments are a bit complex. 

#7. Making Prediction 

Alas, we made it to the final step of the machine-learning model. This is where the model would be ready for practical applications by successfully answering the question of whether a given fruit is an Orange or an Apple. Here is where the model is independent of human interference and draws its own conclusion based on its training. 

Types of Machine Learning

There are three main types of machine learning: Supervised Learning, Unsupervised Learning, and Reinforcement Learning. 

Supervised Learning

By the name, this type of machine learning is based on the supervision of humans. It uses labeled datasets to train machine learning models. That means the output is already known, the model just needs to connect the inputs to the respective output. For example, Let’s say we feed the machine learning model with an input dataset of apples. We will provide training to the machine to understand the images based on apples’ different features such as color, size, texture, and so on. 

So, when we test the model by providing a test dataset of apples, the model will be able to identify the object and predict the output successfully. In short, supervised learning has a specific target prediction to achieve. This type of machine learning is used in fraud detection, risk assessment, and spam filtering. 

There are two types of supervised learning: 

• Classification: Which solves a  problem where the output variable is a binary or categorial response. It involves the prediction of a class label. 

• Regression: Which solves the problem where there is a linear relationship between the input and the output variables. It usually involves the prediction of numerical values. 

Unsupervised Learning

Unlike supervised learning, unsupervised learning uses unlabeled data to train machines instead of labeled data. This means that there are no fixed output variables. The model learns from the data, discovers patterns, and comes out with the output. It does not require any human supervision as it becomes independent. This is commonly used for separating or categorizing unsorted data based on their features. 

Let’s say we feed an untrained model a dataset of apples and oranges. Although these images are new to the model, it will try to find patterns within these images. The patterns could be based on shape, size, texture, color, etc. Then, the model would generate an output that separates oranges from apples without any supervision. In short, unsupervised learning doesn’t have a specific target to achieve. 

The are two types of unsupervised learning:

• Clustering: This is where machines group data based on their features. 

• Association: This is where the model finds relations and connections among different variables. 

Reinforcement Learning

Reinforcement learning doesn’t require labeled or unlabeled data, instead, the model learns by experience. Learning happens based on a feedback-based process. The model explores the data, notes features, and learns from past experience. The model agent is then rewarded when the output is accurate and punished when it is not. So basically, it’s an agent operating in an environment based on the feedback or reward given by the environment in which it is operating. 

For example, if we feed a model with a dataset of apples, the model goes through a trial and error process based on feedback to conclude that the input is, in fact, an “apple”. 

Machine Learning Algorithms

Here are some of the most popular machine learning algorithms.

• Regression Algorithms: Regression is a supervised learning process in which the machine learning model identifies the relationship between the target output variables and the input features to make a prediction. Top regression algorithms include:

• • • Linear Regression
    • Lasso Regression
    • Logistic Regression

• Instance-based Algorithms: Make predictions based on similar past instances, measuring similarity through distance metrics. Top instance-based algorithms include:

• • • K-Nearest Neighbor
    • Learning Vector Quantization
    • Self-Organizing Map

• Decision Trees Algorithms: Create a tree-like model to classify or predict by splitting data based on features, making sequential decisions. Top decision tree algorithms include:

• • • Classification and Regression trees
    • C4.5 and C5.0
    • Conditional Decision Trees

• Bayesian Algorithms: Use Bayes’ theorem to infer probabilities and classify based on conditional probabilities of features. Top Bayesian algorithms include: 

• • • Naive Bayes
    • Gaussian Naive Bayes
    • Bayesian Belief Network

• Clustering Algorithms: Group similar data points together in an unsupervised manner, revealing underlying patterns or structures within the data. Top clustering algorithms include:

• • • K-Means
    • K-Medians
    • Hierarchical Clustering
    • Gaussian Mixture

• Artificial Neural Network Algorithms: Simulate the human brain with interconnected nodes (neurons) to learn complex patterns, used in various machine learning tasks. Top neural network algorithms include:

• • • Perceptron
    • Multilayer Perceptron
    • Back-propagation
    • Hopfield Network

• Deep Learning Algorithms: Use neural networks to automatically learn and extract patterns from data, enabling complex tasks like image recognition and natural language processing. Top deep learning Algorithms include:

• • • Convolutional Neural Networks
    • Recurrent Neural Networks
    •  Deep Boltzmann Machines
    • Auto-Encoders Deep Belief Networks

Benefits and Drawbacks of ML

Here are the advantages and disadvantages of machine learning. 

Benefits

• Automation: Machine learning enables automation of tasks that would be time-consuming or even impossible for humans to perform manually. This leads to increased efficiency and reduced human error.

• Continuous Improvement: Machine learning models can learn from new data and adapt over time, allowing them to improve their performance as more data becomes available. 

• Wide Range of Applications: Machine learning finds use in diverse fields: healthcare diagnosis, financial forecasting, autonomous vehicles, language translation, image recognition, recommendation systems, and more, transforming industries through data-driven insights.

• Handling Complex Data: ML can handle complex and high-dimensional data, making it possible to extract meaningful information from data types such as images, audio, and text.
• Trend and Pattern Identification: Machine learning analyzes data to uncover recurring behaviors and relationships, enabling informed decisions and predictive insights across various domains.

Drawbacks

• Data Dependency: The quality and quantity of data used for training significantly impact the performance of machine learning models. Biased or inadequate data can lead to biased or unreliable results.

• Complexity: Developing and deploying machine learning models can be complex and require specialized expertise in areas like data preprocessing, feature selection, model selection, and hyperparameter tuning.

• Lack of Common Sense: ML models lack common sense reasoning and may produce unexpected or illogical outputs in certain situations, especially when dealing with new data..

• Expensive: Implementing machine learning requires investment in infrastructure, software, and skilled personnel, which might be prohibitive for smaller organizations.

• Ethical and Bias Concerns: ML models can inadvertently learn biases present in the training data, leading to biased outcomes. This is a significant concern, especially in applications involving sensitive attributes like race or gender.

The True Power of Machine Learning

So, Machine learning is vital as it enables computers to learn from data and make informed decisions, enhancing automation, predictions, and problem-solving across diverse fields like healthcare, finance, and technology. Its ability to extract insights from complex data drives innovation and optimizes processes, revolutionizing modern decision-making. 

The profound impact of machine learning is evident in its ability to unravel intricate data structures, enabling insights that were once unattainable. This technology not only fuels efficiency and competitiveness but also propels society toward a future driven by data-driven insights and informed decision-making.