Main menu



What is a good definition of machine learning?

What is machine learning ML?

Machine learning ( ML ) is an artificial intelligence(AI) application in which computer programs use algorithms to find patterns in data.

You can do this without relying on humans and without any special programming.

What is machine learning and how does it work?

What is machine learning and how does it work?

Machine learning (ML) is the process of using a mathematical model of data to help a computer learn without direct instructions.

It is considered a subset of artificial intelligence (AI).

Machine learning uses algorithms to identify patterns within data and uses those patterns to create data models that can make predictions.

Much like how humans improve with practice, machine learning also improves results when data and environments improve.

Because machine learning is adaptive, it is well suited for scenarios where data is always changing, the nature of a request or task is always changing, or where coding a solution is practically impossible.

The link between machine learning and AI

Machine learning is considered a subset of AI.

'Intelligent' computers think like humans and do their own thing.

 One way to train a computer to mimic the human reasoning process is to use a neural network, a set of algorithms modeled after the human brain.

The link between machine learning and predictive analytics

Machine learning is a type of predictive analytics, but the notable difference is that machine learning is much easier to implement with real-time updates because it can get more data.

Predictive analytics typically use static data sets and need to be refreshed for updates.

The link between machine learning and deep learning

Deep learning is a specialized form of machine learning that uses neural networks (NNs) to provide answers.

Deep learning, which can verify its accuracy on its own, classifies information like the human brain and powers some of the most human-like AIs.

Benefits of machine learning

The applications of machine learning are diverse and the possibilities are constantly expanding.

Here are some of the key benefits that businesses can gain from machine learning projects:

Get insights

Machine learning can help you tell the story your data tells you by identifying patterns or structures in both structured and unstructured data.

Improve data integrity

Machine learning is very useful for data mining, and it can go one step further and improve its capabilities over time.

Improved user experience

Adaptive interfaces, targeted content, chatbots, and voice-enabled virtual assistants are all examples of how machine learning can be used to optimize customer experiences.

Reduce risk

As fraud strategies are constantly changing, machine learning also monitors and identifies new patterns accordingly and catches them in the trial phase before they succeed.

Anticipating customer behavior

Machine learning mines customer-related data to uncover patterns and behaviors to optimize product recommendations and deliver the best customer experience.

Cut down the money

As one machine learning application, process automation frees up time and resources, freeing your team to focus on what matters most.

machine learning techniques

There are three main techniques used in machine learning:

supervised learning

Data that addresses datasets with labels or structures acts like a teacher, 'training' the machine, enhancing the machine's ability to make predictions or decisions.


Address data sets without labels or structures, and group data into clusters to uncover patterns and relationships.

Reinforcement learning

On behalf of a human operator, an agent, a computer program on behalf of a person or thing, helps determine a result according to a feedback loop.

How machine learning works to solve problems

Here's an overview of the machine learning process used to solve the problem:


Step 1: Data Collection and Preparation

Once the data source is identified, usable data is compiled. Depending on the type of data you have, you know what machine learning algorithms are available. When you review data, you can check for anomalies, develop structure, and troubleshoot data integrity.

Step 2: Train the model

The prepared data is split into two groups: a training set and a test set. The training set is a fairly large part of the data and is used to tune the machine learning model to the highest accuracy.

Step 3: Validate the Model

When you are ready to choose your final data model, you use a set of tests to evaluate its performance and accuracy.

Step 4: Interpret the results

Review results to find insights, draw conclusions, and predict outcomes.

What can machine learning do?

value prediction

Regression algorithms, useful for determining cause and effect between variables, use the values ​​to build models, and these algorithms are used for a variety of predictive tasks.

Regression studies are useful for forecasting the future to predict product demand, forecast sales figures, or estimate campaign outcomes.

Identification of anomalies

Anomaly detection algorithms, often used to identify potential risks, find data that deviates from expected standards. Equipment malfunctions, structural defects, text errors, and instances of fraud are examples of how machine learning can be used to solve problems.

find structure

Clustering algorithms are often the first steps in machine learning and represent the underlying structure within a data set.

Clustering to categorize common items is commonly used in market segmentation and provides insights that can aid in price selection and predicting customer preferences.

Category prediction

Classification algorithms help determine the exact category of information. Classification is similar to clustering, but differs in that it applies to supervised learning to which predefined labels are assigned.

What do machine learning engineers do?

Machine learning engineers transform raw data collected from various data pipelines into data science models that can be applied and scaled as needed. Machine learning engineers connect that structured data to models defined by data scientists they work with.

Machine learning engineers also develop algorithms and build programs that machines, computers, and robots can use to process incoming data and identify patterns.

How many industries are using machine learning

Across industries, companies are using machine learning in many different ways. Some examples of machine learning in key industries include:

Banking and finance

Risk management and fraud prevention are key areas where machine learning is adding enormous value to the financial sector.

Medical treatment

There are many examples of how machine learning can improve patient care, including diagnostic tools, patient monitoring, and outbreak prediction.


Identifying anomalous traffic volumes, optimizing delivery routes, and autonomous vehicles are examples of how machine learning can positively impact traffic.

Customer service

By answering questions, measuring customer intent, and providing virtual assistance, machine learning is being leveraged to support the customer service industry.


Machine learning helps retailers analyse buying patterns, optimize products and prices, and use the data to improve the overall customer experience.


Developing robots to solve job shortages, diagnosing plant diseases and monitoring the health of agriculture are examples of how machine learning can improve agriculture

Machine Learning Platform Features

When you choose a machine learning platform, you get a solution that includes the following features:

Cloud computing

Easy to set up and deploy, the cloud is perfect for handling workloads of all sizes, and it doesn't require advanced knowledge to connect to data sources and scale as needed.

Accessible development environment

The ideal platform supports all skill levels with accessible authoring options. These platforms allow users to reap the benefits of machine learning, whether they work extensively in code or prefer automated tools and drag-and-drop interfaces that don't require any coding experience.

Built-in support for familiar machine learning frameworks

Find platforms where you can work with the tools you're familiar with and preferred: ONNX, Python, PyTorch, scikit-learn, or TensorFlow.

Enterprise-grade security

Find a platform that provides controls that support enterprise-level governance, security, and infrastructure protection.

Machine learning algorithms

What is a machine learning algorithm?

Introduction to the mathematics and logic behind machine learning

Machine learning algorithms

Machine learning algorithms are pieces of code that help users explore, analyze, and find meaning in complex data sets. Each algorithm is a finite set of clear, step-by-step instructions that a machine can follow to achieve

a specific goal. In machine learning models, the goal is to establish or discover patterns that users can use to make predictions or classify information.

Machine learning algorithms use parameters based on training data (a subset of data representing a larger set). As the training data is scaled to represent the world more realistically, the algorithm computes more accurate results.

Different algorithms analyze data in different ways. Algorithms are grouped into commonly used machine learning techniques (supervised learning, unsupervised learning, and reinforcement learning).

The most commonly used algorithms use regression and classification to predict target categories, find anomalous data points, predict values, and search for similarities.

machine learning techniques

As you dive deeper into machine learning algorithms, you'll notice that machine learning algorithms typically fall into one of three machine learning techniques.

supervised learning

In supervised learning, an algorithm makes predictions based on a set of labelled examples supplied by the user.

 This technique is useful when you know what the result will look like.


For example, you want to provide a data set containing city populations by year for the last 100 years, and you want to know what the population of a particular city will be 4 years from now.

 The labels (population, city, and year) already in the data set are used in the results.


In unsupervised learning, data points are not labeled. These algorithms organize data or label it in a way that describes its structure.

This technique is useful when you don't know what the result will look like.

For example, you provide customer data, and you want to create a segment of customers who prefer similar products.

 The data you provide is not labeled, and the labels in the results are generated based on the similarities found between the data points.

Reinforcement learning

Reinforcement learning uses algorithms that learn from the results and decide what action to take next.

 After each action, the algorithm receives feedback to help it decide whether a choice was correct, neutral, or wrong.

This is a good technique to use for automated systems that need to make a lot of small decisions without human guidance.

For example, let's say you're designing a self-driving car, and you want to make sure it obeys the law and keeps people safe.

As the car gains experience and reinforcement over time, it learns to stay in its lane, follow the speed limit, and brake for pedestrians.


What Machine Learning Algorithms Can Do

Machine learning algorithms help answer questions that are too complex to answer through manual analysis.

Although there are many different types of machine learning algorithms, use cases for machine learning algorithms typically fall into one of the following categories:

Target Category Prediction

A two-class (binary) classification algorithm divides data into two categories. This algorithm is useful for questions that have only two mutually exclusive possible answers (including yes/no questions).

See the following example.

Will this tire explode during the next 1,000 miles?

Which option can I get more referrals for? (10 USD credit or 15% off).

Multiclass (polynomial) classification algorithms

Divide data into three or more categories.

This algorithm is useful for questions with three or more possible answers that are mutually exclusive. See the following example.

What month do most travelers buy their tickets?

What emotions does the person in this picture show?


Find abnormal data points

Anomaly detection algorithms identify data points that do not fall within the range of parameters defined for what is “normal”.

For example, it uses an anomaly detection algorithm to answer questions such as:

Where is the defect in this batch?

What credit card purchases can be fraudulent?

value prediction

A regression algorithm predicts the value of a new data point based on historical data. This algorithm can answer the following questions.

What will the average cost of a two-bedroom house in my city be next year?

How many patients visit the hospital on Tuesday?

See how values ​​change over time

A time series algorithm shows how a given value changes over time.

With time series analysis and time series forecasting, you collect data at regular time intervals and use that data to make forecasts and identify trends, seasonality, periodicity, and irregularities.

Time series algorithms are used to answer questions such as:

Will the price of a particular stock go up or down next year?

How much will it cost next year?


Similarity Search

Clustering algorithms divide data into groups by determining the level of similarity between data points.

The clustering algorithm works well for questions like:

What kind of viewers like the same kind of movies?

Which printer model does the error occur in the same way?


Classification algorithms use predictive calculations to assign data to preset categories.

A classification algorithm is trained using input data and used to answer questions such as:

Is this spam?

What is the sentiment (positive, negative, or neutral) of the given text?


What Machine Learning Algorithms Can Do

A linear regression algorithm displays or predicts a relationship between two variables or factors by fitting a continuous straight line to the data. This line is usually calculated using the squared error cost function. Linear regression is one of the very popular types of regression analysis.



What Machine Learning Algorithms Can Do

A logistic regression analysis algorithm fits a continuous sigmoidal curve to the data. Logistic regression is another popular type of regression analysis.


What Machine Learning Algorithms Can Do

The Naïve Bayes algorithm calculates the probability that an event will occur based on the occurrence of a related event.

What Machine Learning Algorithms Can Do

A support vector machine draws a hyperplane between the two nearest data points. This will ignore the class and maximize the distance between the two data points to make the distinction clearer.


What Machine Learning Algorithms Can Do

Decision tree algorithms partition data into two or more sets of the same type. Use the if–then rule to separate data according to the most important differentiators between data points.

What Machine Learning Algorithms Can Do

The K- Nearest Algorithm stores all available data points and classifies each new data point according to the nearest data point measured as a function of distance.

What Machine Learning Algorithms Can Do

The random forest algorithm is based on a decision tree, but rather than creating a single tree, it creates a forest of trees and then randomizes the trees in that forest. It then aggregates the votes of other random constructs in the decision tree to determine the final class of the test object.

What Machine Learning Algorithms Can Do

Gradient boosting algorithms produce predictive models that bundle weak predictive models (usually decision trees) through an ensemble process that improves the overall performance of the model.

What Machine Learning Algorithms Can Do


The K-means algorithm classifies data into clusters. where K is equal to the number of clusters. The data points within each cluster are of the same type and different from the data points in the other clusters.