What is Machine Learning

Machine learning (ML) has revolutionized multiple industries, from human resources to supply chain management and finance. As more and more companies integrate machine learning processes into their functions, the demand for ML specialists is expected to grow by 40% from 2023 to 2027.

But what exactly is machine learning and how does it work? Stick around and we’ll answer all your need-to-know questions.

What is machine learning?

Machine learning is a branch of artificial intelligence (AI) that allows computers to learn and make predictions or decisions without being explicitly programmed. It involves the development of algorithms and statistical models that enable machines to recognize patterns, make sense of data, and continuously improve their performance through experience.

Check out this video for a 100 second explanation of machine learning.

Key components of machine learning include:


Data is the lifeblood of machine learning. It serves as the foundation for training algorithms. Datasets are typically divided into two subsets: training data, which the model uses to learn, and testing data, which it uses to evaluate its performance. The quality, quantity, and relevance of data are critical factors influencing the success of a machine learning model.


Machine learning algorithms are mathematical models that process and analyze data. These algorithms vary in complexity and purpose and can be categorized into supervised learning (with labelled data), unsupervised learning (without labelled data), and reinforcement learning (reward-based learning), among others. Each type of algorithm suits different use cases.


Features are attributes or characteristics extracted from data that the model uses for learning. Feature engineering involves selecting and transforming relevant data points to enhance a model's accuracy. It requires domain expertise and creativity to identify the most informative features.


The training phase of machine learning involves feeding the model with labelled data, allowing it to learn and adapt to make predictions or classifications. During training, the model adjusts its internal parameters to reduce the difference between its predictions and the actual outcomes. The goal is to create a more accurate model that can make accurate predictions on unseen data.

Evaluation and testing

After training, the model's performance is assessed using testing data. Metrics like accuracy, precision, recall, and F1 score are used to evaluate its effectiveness. From there, fine-tuning and refining the model may be necessary based on the evaluation results.


Once a machine learning model proves its efficacy, it can be deployed in real-world applications. This involves integrating the model into software or systems where it can make predictions, automate decision-making, or assist in solving complex problems.

Feedback Loop

Machine learning models are not static; they can adapt and improve over time. A feedback loop is crucial for retraining the model with new data to ensure it remains accurate and up-to-date.

The role machine learning plays in a company

Many different industries are implementing machine learning algorithms and processes into their everyday functions. Focusing on automating tasks, machines can analyze and complete the more meticulous, analytical tasks, allowing more time for their human moderators to interpret the data and complete the more complex statistical analysis and interpretation.

Data-driven decision making

Machine learning can predict consumer behaviour, market trends, and even optimize internal processes. For instance, models can analyze historical sales data to forecast demand, helping businesses optimize inventory management, pricing strategies, and resource allocation.

Customer insights

ML enables companies to better understand their customers. Customer data, such as purchase history, online behaviour, and feedback, can be analyzed to then create personalized recommendations and marketing strategies. These insights lead to improved customer engagement and loyalty.

Automation and efficiency

Machines can automate repetitive and time-consuming tasks. In customer service, chatbots powered by machine learning can answer queries 24/7. In manufacturing, ML-driven robots can handle repetitive assembly tasks with precision. This automation leads to cost savings and increased productivity by freeing employees from more mechanical tasks to focus on more complex issues.

Fraud detection and security

Machine learning models can analyze patterns of fraudulent activity, detecting credit card fraud or insurance scams and raising alerts in real-time, reducing financial losses and maintaining customer trust.

Supply chain optimization

Algorithms optimize supply chains by predicting demand, optimizing routes for delivery, and ensuring the availability of materials. This reduces operational costs and improves customer satisfaction by ensuring products are available when and where they are needed.

Predictive maintenance

In sectors like manufacturing and utilities, machines are employed for predictive maintenance. By analyzing sensor data, models can predict when equipment is likely to fail, allowing for proactive maintenance to minimize downtime and repair costs.

Employee productivity

Machines help in time tracking, scheduling, and workload management. Additionally, they can identify patterns of employee burnout or attrition, aiding HR departments in taking proactive measures.

Innovation and product development

Machine learning assists in product innovation by providing insights into market demands and trends. Companies can use sentiment analysis and social media data to identify gaps in the market and develop products or services that better align with customer preferences.

Personalized marketing

Machines power personalized marketing campaigns. By analyzing customer behaviour and preferences, companies can deliver tailored content and offers, increasing the likelihood of conversion.

Risk management

In the financial industry, ML is instrumental in risk assessment and management. It can analyze credit scores, transaction data, and market indicators to assess the creditworthiness of individuals and organizations, helping companies make sound lending and investment decisions.

Healthcare and diagnosis

Machines have revolutionized healthcare, aiding in disease diagnosis, drug discovery, and treatment optimization. It can analyze medical images, patient records, and clinical data to assist doctors in making more accurate diagnoses and treatment plans.

Quality control

In manufacturing, machines are crucial for quality control. It can inspect and detect defects in real-time, ensuring that only high-quality and safety-sealed products reach customers, reducing returns, and protecting brand reputation.

Competitive advantage

Companies that effectively implement machine learning algorithms gain a competitive edge. They can respond to market changes more quickly, deliver better customer experiences, and create innovative products and services.


Machine learning algorithms can handle vast amounts of data and adapt to changing requirements, making them scalable solutions for businesses of all sizes. For example, a company could implement machine learning-driven customer recommendation systems that scale their business by increasing sales and customer engagement through personalized product suggestions.

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Types of machine learning

Machine learning is a multifaceted field that encompasses various types and techniques, each tailored to different problem-solving scenarios. Here are some of the prominent types of machine learning:

Supervised Learning

In supervised learning, the algorithm is trained on a labelled dataset, where each data point is associated with the correct output. The model learns to map inputs to outputs and can then make predictions on new, unseen data. Common applications include image classification, language translation, and speech recognition.

Unsupervised Learning

Unsupervised learning involves working with unlabeled data to find patterns, relationships, or structure within the data. Clustering and dimensionality reduction are typical tasks in unsupervised learning. For example, clustering algorithms can group customers with similar buying habits in marketing.

Semi-Supervised Learning

Semi-supervised learning combines elements of both supervised and unsupervised learning. It uses a small amount of labelled data and a larger amount of unlabeled data to improve model performance. This approach is useful when acquiring labelled data is expensive or time-consuming.

Reinforcement Learning

Reinforcement learning focuses on training mechanical agents to make sequences of decisions in an environment. Agents learn through trial and error, receiving rewards or penalties based on their actions. Applications include game playing, robotics, and autonomous systems.

Deep Learning

Deep learning is a subset of machine learning that uses artificial neural networks (ie. teaching computers to think like a human brain) to model complex patterns and representations. It excels in tasks like image and speech recognition, natural language processing, and recommendation systems. Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are known for their ability to handle high-dimensional data.

Transfer Learning

Transfer learning leverages pre-trained models to boost the performance of a model on a specific task. By using a model trained on a vast dataset, transfer learning reduces the need for extensive data and computation. This approach is widely used in image and text processing.

Online Learning

Online learning, also known as incremental learning, involves training models continuously as new data becomes available. This is valuable for applications where the data stream is continuous and rapidly changing, like stock market prediction or fraud detection.

Self-Supervised Learning

Self-supervised learning is a variant of unsupervised learning where a model generates its own labels from the data. It's often used in natural language processing, where a model learns by predicting missing words in sentences or sentences in a sequence.

Ensemble Learning

Ensemble learning combines multiple machine learning models to improve predictive performance. Methods like bagging (e.g., Random Forest) and boosting (e.g., AdaBoost) are popular ensemble techniques, often used in classification and regression tasks.

Bayesian Learning

Bayesian learning is based on Bayesian probability theory and involves modelling uncertainty in machine learning tasks. It's useful in situations where uncertainty and probabilistic reasoning are essential, such as medical diagnosis.

Evolutionary Algorithms

Evolutionary algorithms mimic the process of natural selection to optimize solutions. They are applied in optimization problems, such as parameter tuning for machine learning models or designing neural network architectures.

The machine learning career path

Everyone starts somewhere. Machine learning is a more advanced career and many people can’t jump in straight away after getting a post-secondary education. In fact, just 6% of machine learning jobs posted online are for entry-level positions. Most individuals entering the ML field start with a strong educational background in computer science, data science, mathematics, or a related field. A bachelor's degree is often the minimum requirement, but many machine learning practitioners pursue graduate degrees (master's or Ph.D.) for more in-depth knowledge. Another option is to take a bootcamp like the Data Science Program offered by Lighthouse Labs. This program sets you up with the foundational knowledge needed for many data science careers.

Learn the fundamentals

Gain a solid understanding of the fundamentals, including statistics, linear algebra, calculus, and programming languages such as Python. Familiarize yourself with machine learning libraries and frameworks like TensorFlow and scikit-learn.

Get hands-on experience

Practical experience is crucial. Work on projects and participate in online courses and competitions. Platforms like Kaggle offer datasets and challenges to help you hone your skills.

Build a portfolio

Create a portfolio showcasing your ML projects, including code, data, and outcomes. This portfolio can be invaluable when applying for jobs.

Research potential career paths

Junior Machine Learning Engineer/Data Scientist

Entry-level positions typically involve data preprocessing, building and training ML models, and collaborating with cross-functional teams.

Machine Learning Engineer/Data Scientist

As you gain experience, you'll be tasked with more complex problems, designing and implementing ML solutions, and optimizing models for deployment.

Senior Machine Learning Engineer/Data Scientist

Senior roles involve a deeper understanding of algorithm selection, model tuning, and often leadership responsibilities. You might mentor junior team members and contribute to strategic decision-making.

Machine Learning Researcher

For those with strong research and academic backgrounds, this role focuses on pushing the boundaries of ML by developing novel algorithms and techniques. Research positions are often found in academia, research institutions, or leading technology companies.

Key learnings

  • Machine learning (ML) is a branch of artificial intelligence (AI) that allows computers to learn and make predictions or decisions without being explicitly programmed.
  • Machine learning has revolutionized several industries, automating more menial tasks thus streamlining processes and giving more time for employees to focus on more complex tasks.
  • Becoming a machine learning practitioner takes time. However, starting with a basic understanding of data science and statistical principles - either through taking a bootcamp or pursuing a university education - are the right first steps to getting into this exciting field.

Ready to take the first step in your machine learning career? Lighthouse Labs offers a Data Science Program that lays the groundwork for a career in data science. Check out the program and learn how you could join the next generation of data scientists and machine learning specialists.