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Idan Novogroder
Idan Novogroder Author

Idan has an extensive background in software and DevOps engineering....

Last updated on April 26, 2024

Today, machines are able to emulate human intelligence through the use of artificial intelligence technology. Approaches such as machine learning, deep learning, natural language processing, and computer vision have been crucial in enabling machines to perform tasks that were once exclusive to the human brain.

Machine learning allows systems to learn and improve from experience without the need for explicit programming. It’s truly remarkable how these systems can autonomously acquire knowledge and enhance their performance. 

But how does machine learning technology make this possible? What are the main components of machine learning? Keep reading to explore some machine learning basics you simply have to know.

What is Machine Learning?

Machine learning (ML) is a subfield of artificial intelligence (AI) that focuses on creating algorithms and models that enable computers to learn from data and make predictions or decisions without being explicitly programmed. It entails training models on labeled data and then using them to generate accurate predictions or take action on fresh, previously unseen data.

Machine Learning vs. Deep Learning vs. Artificial Intelligence

Deep learning is a subset of machine learning, so it also falls under the umbrella of artificial intelligence. 

Applications with a machine learning architecture have the ability to learn and adapt from experience, without needing explicit programming. 

Through the utilization of machine learning models, computer scientists can train a machine by providing it with vast quantities of data. The machine uses a set of rules, known as an algorithm, to analyze and draw inferences from the data. As the machine processes more data, its performance in tasks and decision-making improves.

Deep learning applications, on the other hand, use artificial neural networks to replicate the learning capabilities of the human brain.

Machine learning algorithms often require human intervention to correct errors, whereas deep learning algorithms can enhance their results through repetition, without any need for human involvement. Deep learning algorithms thrive on large and diverse data sets, which may include unstructured data.

Components of Machine Learning

Here are the key machine learning lifecycle components:


This refers to the way knowledge is represented for ML purposes. Some examples include decision trees, sets of rules, instances, graphical models, neural networks, support vector machines, model ensembles, and various others.


Abstraction simplifies the representation of a problem, allowing for more efficient problem-solving with reduced memory and computation requirements. Examples of data abstraction are decreasing the spatial and temporal resolution or dividing continuous variables into meaningful ranges that align with specific goals.


Every ML project needs a method for evaluating hypotheses. Some examples are accuracy, prediction and recall, squared error, KL divergence (relative entropy), and others.


Generalization is crucial for a model to effectively handle new, unfamiliar data that comes from the same distribution as the data used to train the model. It allows teams to gain a deeper understanding of overfitting and assess the quality of a model.

Data Storage

This one might easily get forgotten among the components of machine learning, but where your data resides is very important. Common storage solutions for machine learning include object storage, distributed file systems, and cloud-based storage.

Commonly Used Machine Learning Algorithms

Linear Regression

Linear regression analysis predicts the value of one variable depending on the value of another. The variable you’re looking to forecast is known as the dependent variable. The variable used to predict the value of another variable is known as the independent variable.

Logistic Regression

Logistic regression is a machine learning algorithm that is often used to estimate discrete values, typically binary values such as 0 or 1, based on a set of independent variables. It helps to predict the likelihood of an event by fitting data to a logit function.

K Nearest Neighbor (KNN)

This algorithm is versatile and can be used for classification and regression problems. KNN stores all the available cases and then classifies any new cases by taking a majority vote of its k neighbors. The case is then assigned to the class with the most similarities. This measurement is performed by a distance function.

K-Means Clustering

This machine learning algorithm is capable of solving clustering problems via unsupervised learning. Data sets are organized into distinct clusters, each containing data points that are similar to one another and different from the data in other clusters.

The K-means algorithm selects a specified number of points, known as centroids, for each cluster. Every data point is grouped together with the nearest centroids, resulting in K clusters. Next, it generates new centroids by considering the current cluster members.

Using these updated centroids, the algorithm calculates the closest distance for each data point. This process is iterated until the centroids remain unchanged.

Decision Tree

The Decision Tree algorithm is widely used in machine learning as a supervised learning method for classifying problems. It performs well in accurately categorizing both categorical and continuous dependent variables. The algorithm partitions the population into two or more homogeneous sets by considering the most significant attributes or independent variables.

Random Forest

Random Forest refers to a group of decision trees working together. When it comes to classifying a new object based on its attributes, every tree is involved in the process and contributes its “vote” for the appropriate class. The forest selects the classification with the highest number of votes, considering all the trees in the forest.

Support Vector Machines (SVM)

The SVM algorithm is a classification method that involves plotting raw data as points in an n-dimensional space, where n represents the number of features. Each feature’s value is then associated with a specific coordinate, simplifying the data’s classification process. Classifiers are useful for dividing data and visually representing it on a graph.

Naïve Bayes

A Naïve Bayes classifier operates under the assumption that the presence of a specific feature in a class is independent of the presence of any other feature. Although these features are interconnected, a Naïve Bayes classifier would treat each property as independent when calculating the probability of a specific outcome.

Building a Naïve Bayesian model is a straightforward process that proves highly valuable when dealing with large datasets.

Common Machine Learning Applications

Machine learning enhances software applications by enabling them to make accurate predictions without the need for explicit programming. Industries across various sectors are increasingly adopting machine learning in the following ways:

  • Web search and ranking pages based on search preferences.
  • Assessing risk in finance, particularly in credit offers, and identifying optimal investment opportunities.
  • Anticipating customer attrition in the e-commerce industry.
  • Space exploration and the deployment of probes into outer space.
  • The progress in robotics and the development of autonomous, self-driving cars.
  • Gathering information on relationships and preferences from social media.
  • Accelerating the debugging process in computer science.

Product recommendations 

Targeted marketing in retail uses machine learning to categorize customers according to their purchasing patterns or demographic similarities. It can also predict the preferences of one individual based on the buying behavior of others. 

With a deep understanding of data analysis and predictive modeling, machine learning has the ability to uncover hidden connections and anticipate your desires even before you are aware of them. When the data is incomplete, there is a risk of receiving inaccurate recommendations.

Facial recognition

Facial recognition is a clear and prominent application of machine learning. Previously, individuals were provided with name suggestions for their mobile photos and Facebook tagging. The process has now evolved to instantly tag and verify someone by analyzing facial contours and comparing patterns. 

Facial recognition combined with deep learning has proven to be incredibly valuable in the healthcare industry, enabling the detection of genetic diseases and providing more precise tracking of a patient’s medication usage. The number of applications and industries impacted by it is continuously increasing.

Email automation and spam filtering 

By automating certain tasks, ML helps users save time and prioritize important emails. Additionally, spam filtering keeps your inbox free from unwanted and potentially harmful messages. These features are designed to streamline the email experience and make it more manageable.

Effective spam filtering involves analyzing and identifying patterns in email content that are considered undesirable. This encompasses information from email domains, the geographical location of the sender, the content and structure of the message, and IP addresses. It also relies on user assistance in identifying and flagging misfiled emails. Every time an email is marked, the application adds a new data reference to enhance its future accuracy.

Predictive analytics

Predictive analytics is a fascinating field within advanced analytics that leverages data to make accurate predictions about future events. Methods like data mining, statistics, and modeling utilize machine learning and advanced algorithms to analyze current and past data. 

By identifying patterns and anomalies, these techniques can help uncover potential risks and opportunities and reduce the likelihood of human errors. Additionally, they boost the speed and comprehensiveness of data analysis. 

Precise financial calculations

The financial services industry has greatly benefited from the advent of machine learning, as the majority of systems have transitioned to digital platforms. Machine learning plays a crucial role in analyzing numerous financial transactions that are beyond human monitoring capabilities. 

It efficiently detects fraudulent activities, ensuring enhanced security. Machine learning plays a crucial role in determining credit scores and making lending decisions, as it assesses both the creditworthiness of individuals and analyzes financial risk. Incorporating data analytics with artificial intelligence, machine learning, and natural language processing is revolutionizing the customer experience in banking.

Healthcare advancements

With each passing day, we are making significant progress towards a complete shift to electronic medical records. Healthcare information for clinicians can be enhanced with analytics and machine learning to gain valuable insights that can support better planning and patient care, improved diagnoses, and lower treatment costs. 

Integrating machine learning with radiology, cardiology, and pathology, for instance, may result in earlier detection of abnormalities and increased focus on areas of concern. In the future, machine learning will prove valuable for family practitioners or internists in treating patients at the bedside. 

By analyzing data trends, they will be able to predict health risks such as heart disease. For instance, wearables collect vast amounts of data on the wearer’s health and use AI and machine learning to notify them or their doctors about potential problems, enabling proactive measures and rapid response to emergencies.

Mobile voice-to-text and predictive text

Machines can also learn languages in different formats. Similar to Siri and Cortana, voice-to-text applications have the ability to learn words and language, enabling them to accurately transcribe audio into written text.

Supervised learning is a straightforward method that trains the process to recognize and predict common words or phrases based on the context of the text. Unsupervised learning takes it a step further, fine-tuning predictions based on the available data.

Machine Learning Classification

In ML, you’re looking at several types of learning. Let’s dive into them to understand their benefits and challenges.

Supervised Machine Learning Algorithms

Supervised learning is widely used in machine learning and serves as the foundation for many algorithms. This form of learning encompasses regression and classification. Regression involves predicting numerical variables, while classification involves predicting categorical variables. 

Supervised learning involves the utilization of different algorithms, such as:

  • Linear regression
  • Logistic regression
  • Decision trees 
  • Random forest
  • Gradient boosting

Benefits of supervised learning

  • Supervised learning models can achieve impressive accuracy due to their training on labeled data.
  • This type of learning is often used in pre-trained models, which can significantly save time and resources during the development of new machine learning models.
  • Such models often provide interpretable decision-making processes.

Challenges of supervised learning

  • It may have certain limitations when it comes to recognizing patterns and could face difficulties with unfamiliar or unforeseen patterns that weren’t included in its training data.
  • It can be quite a time-consuming and expensive process, as it heavily depends on labeled data.
  • There is a risk of making inaccurate generalizations when considering new data.

Applications of supervised learning

  • Image classification
  • Extracting information from text, 
  • Speech recognition 
  • Recommendation systems
  • Predictive analytics
  • Detecting fraud
  • Email spam detection

Semi-Supervised Machine Learning Algorithms

Semi-supervised learning leverages both labeled and unlabeled data to enhance its performance. It can be incredibly valuable in situations where acquiring labeled data is expensive, time-consuming, or requires a lot of resources. 

Teams working with labeled data that require expertise and access to appropriate resources for training or learning purposes typically choose semi-supervised learning.

This type of learning comes in handy when you have a small amount of labeled data and a larger portion of it is unlabeled. Unsupervised techniques can be used to make label predictions, which can then be passed on to supervised techniques. 

Here are a few methods used in this type of learning:

  • Graph-based semi-supervised learning uses a graph to depict the connections between the data points. 
  • Label propagation involves the iterative propagation of labels from labeled data points to unlabeled data points, taking into account the similarities between the data points.
  • Co-training refers to training two distinct machine learning models on separate subsets of the unlabeled data. 
  • Generative adversarial networks (GANs) are a type of deep learning algorithm capable of generating synthetic data. GANs are commonly employed in semi-supervised learning to produce unlabeled data. 

Benefits of semi-supervised learning

  • It improves generalization compared to supervised learning by incorporating both labeled and unlabeled data.
  • It’s applicable to a diverse array of data.

Challenges of semi-supervised learning

  • Implementing semi-supervised methods can be more complex than other approaches.
  • Acquiring the necessary labeled data can sometimes be a challenge, as it may not always be readily accessible.
  • The unlabeled data can have a significant impact on the performance of the machine learning model.

Unsupervised Machine Learning Algorithms

Unsupervised learning algorithms uncover patterns and relationships by analyzing unlabeled data. Unlike supervised learning, it doesn’t require the algorithm to be provided with labeled target outputs. 

Unsupervised learning aims to uncover concealed patterns, similarities, or clusters within the data. These findings can be applied to a range of tasks, including data exploration, visualization, and dimensionality reduction.

There are two primary categories of unsupervised learning:

  • Clustering – It involves the grouping of data points into clusters, taking into account their similarity. This method proves to be valuable in detecting patterns and connections within data, eliminating the necessity for labeled examples.
  • Association – A method used to uncover connections between items within a dataset. It detects patterns that suggest the occurrence of one item is likely to be accompanied by another item.

Benefits of unsupervised learning

  • It’s beneficial for uncovering concealed patterns and diverse relationships within the data.
  • It’s commonly employed for tasks like customer segmentation, anomaly detection, and data exploration.
  • It doesn’t need labeled data and minimizes the need for data labeling.

Challenges of unsupervised learning

  • Since you’re not using labels, it can be challenging to anticipate the accuracy of the machine learning model output.
  • Cluster interpretability can often be lacking, with interpretations that may not be easily understood or meaningful.
  • It comes with various techniques, like autoencoders and dimensionality reduction, that can effectively extract significant features from raw data.

Reinforcement Machine Learning Algorithms

Reinforcement learning involves interacting with the environment, producing actions, and identifying errors. Experimentation, mistakes, and time-consuming processes are key aspects of reinforcement learning. 

In this technique, the machine learning model continuously improves its performance by using reward feedback to understand and learn patterns and behaviors. These algorithms are tailored to address specific challenges, such as the Google Self Driving car or AlphaGo, where a bot continuously improves its performance by competing against humans and itself in the game of Go. With every input of data, they acquire knowledge and incorporate it into their training data. As it continues to learn, its training improves, making it more experienced.

Some of the most common algorithms used in reinforcement learning are:

  • Q-learning
  • Deep Q-learning

Benefits of reinforcement learning

  • It has the capability for autonomous decision-making, making it highly suitable for tasks that require the ability to learn and make a series of decisions, such as robotics and game-playing.
  • It’s recommended for achieving long-term results that can be quite challenging to attain.
  • It’s used to tackle intricate problems that are beyond the capabilities of traditional methods.

Challenges of reinforcement learning

  • Its agents can be resource-intensive and require significant time to compute.
  • It’s not an ideal approach for solving simple problems.
  • It requires a substantial amount of data and computation, this approach proves to be impractical and expensive.

Common Terms Used in Machine Learning

Term Description
Bias Bias is a deviation or displacement from a starting point, present due to the fact that not all models have their starting point at the origin (0,0). Bias should not be mistaken for bias in ethics and fairness or prediction bias.
Cross-Validation Bias Using cross validation score in conjunction with the train score and test score from a basic train-test split can help identify bias and variance problems in a model.

Both bias and variance play a role in the errors that the model makes on unseen data, ultimately impacting its ability to generalize. The goal of an ML team is to minimize both.
Underfitting When a statistical model or machine learning algorithm is too simple to capture the complexities of the data, it’s considered to be underfitting. It indicates a lack of effectiveness in the model’s ability to learn the training data, resulting in subpar performance on both the training and testing data. An underfitting model exhibits a high bias and low variance.
Overfitting When a statistical model is overfitted, it fails to accurately predict outcomes from testing data. If you train a model with a large amount of data, it starts to learn from the noise and inaccurate data entries in the dataset. And when conducting tests using test data, there is a significant amount of variance in the results.
Then the model fails to accurately categorize the data due to an excessive amount of detail and noise.


Given the growing presence of machine learning in various industries such as healthcare, finance, and transportation, we can expect to see significant improvements in efficiency, accuracy, and innovation.

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