What is Machine Learning (ML)? A deep dive

Machine learning: What it means?

According to Fortune Business Insights, the global machine learning market size was valued at $26.03 billion in 2023 and is expected to skyrocket to $225.91 billion by 2030, growing at a CAGR of 36.2%.

Everyday examples of machine learning include:

• Filtering spam emails
• Detecting fake accounts and bot activity
• Powering recommendation engines on Netflix and YouTube
• Driving search engines and voice assistants
• Translating languages automatically

As MIT Professor Aleksander Madry notes, “Understanding machine learning principles and limitations is critical for future leaders”.

Quick reference facts

• Market CAGR: 36–37% through 2029–2030
• 2025 market size: $56–$94 billion
• 2030 market size: $225–$503 billion
• Industry adoption: 72% of surveyed companies use or develop ML
• Healthcare impact: Early disease detection and personalized care
• Finance impact: Real-time fraud detection, risk management
• Retail impact: Personalized recommendations, inventory optimization

How does machine learning work?

At its core, a machine learning model functions as a mathematical representation, where algorithms find patterns rather than following explicit instructions. A deep learning algorithm, for instance, identifies relationships in unstructured data like images or text without human intervention.

The seven key steps of machine learning

• Gathering data: Collecting relevant, high-quality data.
• Preparing data: Cleaning and organizing data into usable formats.
• Choosing a model: Selecting the most suitable machine learning algorithm.
• Training the model: Using data to adjust the model’s parameters.
• Evaluation: Measuring model performance with new data.
• Hyperparameter tuning: Optimizing algorithm settings.
• Prediction: Applying the model to real-world data for predictive modeling.

The amount of data and its quality directly impact how accurately machine learning models can make decisions.

Types of machine learning

Machine learning methods are classified into four main categories: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

Supervised learning

In supervised learning, algorithms learn from labeled datasets where input-output pairs are provided. It’s akin to teaching with answer keys.

Examples:

• Spam detection (spam vs. not spam)
• Image recognition (cat vs. dog)
• Customer churn prediction (stay vs. leave)

Common supervised learning algorithms include:

• Logistic regression for classification tasks
• Decision trees
• Support vector machines
• Supervised learning can solve two primary tasks:
• Classification (predicting categories)
• Regression (predicting continuous values)

Unsupervised learning

Unsupervised machine learning explores unstructured data without labeled outcomes. Algorithms seek patterns, similarities, or anomalies.

Key unsupervised learning tasks:

• Clustering algorithms (e.g., K-means, DBSCAN) group similar data points
• Dimensionality reduction simplifies datasets with many features
• Anomaly detection identifies outliers or rare events

Applications include:

• Customer segmentation
• Image segmentation
• Fraud detection

Unsupervised learning reveals hidden insights when human labeling is impractical or impossible.

Semi-supervised learning

Semi-supervised learning (SSL) blends the strengths of supervised and unsupervised approaches. It uses a small set of labeled data alongside a large unlabeled set, enabling better model performance without the expense of massive labeling efforts.

Example:

• Analyzing medical images where labeled data requires specialized expertise.
• SSL taps into large amounts of data efficiently, balancing the need for labels with the exploration of unlabeled structures.

Reinforcement learning

Reinforcement learning (RL) teaches algorithms to make decisions by interacting with their environment. Agents learn via rewards and punishments, refining actions over time.

Popular reinforcement learning techniques:

• Q-learning
• Deep Q-networks (DQNs) using deep learning algorithms
• Policy gradient methods

Real-world uses:

• Training self-driving cars
• Optimizing warehouse robotics
• Managing dynamic pricing strategies

Deep learning and machine learning: What’s the difference?

The conversation around machine learning vs. deep learning vs. neural networks is often confusing. Here’s a deep dive into their distinctions:

Applications of machine learning techniques

Machine learning is transforming virtually every sector by enabling smarter decision-making, automation, and personalized experiences. Let’s take a closer look at how ML applies across key industries:

• Healthcare: ML algorithms look at patient data and medical images. They help find diseases like cancer or heart problems earlier and more accurately than old methods. These systems can identify subtle patterns or anomalies that clinicians might miss, leading to faster, more precise diagnoses.
• Finance: ML algorithms monitor transactions in real time, identifying unusual patterns that may indicate fraudulent activity. These systems adjust to new fraud tactics.
• Retail: AI-powered recommendation engines look at your browsing history, purchase patterns, and real-time behavior. They provide tailored product suggestions, dynamic marketing, and personalized shopping experiences.
• Transportation: ML-powered systems use real-time data like traffic, weather, and vehicle status to adjust delivery routes on the go.
• Manufacturing: ML forecasts equipment failures, allowing proactive repairs and reducing unplanned downtime.

Machine learning is versatile. Data scientists choose and adapt techniques based on the specific challenge and available data. They may use supervised, unsupervised, or reinforcement learning methods. This flexibility is why ML continues to drive innovation and measurable impact across so many fields.

Real-world examples of AI and ML

AI-powered spam filters

Email platforms like Gmail, Microsoft Outlook, and enterprise security solutions rely heavily on AI spam filters to protect users from unwanted or malicious emails. These systems use ML algorithms that analyze a wide range of features, including:

• Sender information
• Subject lines
• Message content
• Embedded links
• User behavior patterns

The filtering process typically involves several stages:

• Feature extraction: The filter looks at important parts of an email. It checks the sender’s address, headers, subject, and body content. This helps to find useful features for analysis.
• Machine learning model processing: Machine learning models use different algorithms. These include logistic regression, decision trees, support vector machines, and Naive Bayes classifiers. They are trained on large sets of labeled emails. They help to tell spam from real messages.
• Scoring and decision: Each email gets a spam score based on how closely it matches known spam patterns. If the score is high enough, the email is marked as spam and moved out of the inbox.
• Continuous learning: Advanced AI improves over time by learning from new spam and user feedback, making it more accurate and reducing mistakes.

Some machine learning spam filters catch up to 98.5% of spam, making email much safer and easier to use. Natural language processing (NLP) also helps by understanding the context, tone, and intent of emails, boosting accuracy even more.

Self-driving cars

Self-driving cars are one of the most high-profile applications of AI and machine learning. They integrate a range of advanced technologies to interpret and respond to complex real-world environments. Companies like Tesla, Waymo, NVIDIA, Uber, and BMW are at the forefront of this innovation.

Key AI and ML applications in autonomous vehicles include:

• Perception and object recognition: Deep learning models use data from cameras, radar, and other sensors. They help identify road signs, pedestrians, vehicles, lane markings, and obstacles.
• Behavior prediction: Machine learning algorithms can predict what other road users will do. For example, they can tell when a pedestrian might cross the street or when another car may change lanes.
• Trajectory planning: AI looks at past driving patterns and live sensor data to find the safest and fastest way to drive.
• Adaptive decision-making: AI helps the car decide when to brake, speed up, or switch lanes, especially when traffic is busy or changing.
• Predictive maintenance: Machine learning checks sensor data to spot signs of problems early, helping fix issues before something breaks.

For example, Tesla’s Autopilot and Full Self-Driving systems use deep neural networks. These networks process large amounts of sensor data. This helps with advanced driver assistance and self-driving navigation.

Waymo’s self-driving cars use advanced AI for route planning and spotting hazards.

NVIDIA’s DRIVE platform helps with perception, mapping, and planning in different autonomous vehicles.