What are machine learning basics?

The goal of machine learning is to train machines to get better at tasks without explicit programming. To achieve this goal, several steps have to take place. First, data needs to be collected and prepared. Then, a training model, or algorithm, needs to be selected. After which, the model needs to be evaluated so that hyperparameter tuning can happen and predictions can be made. It’s also important to note that there are different types of machine learning which include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

Key Features of Machine Learning

Machine learning, a dynamic branch of artificial intelligence, empowers systems to enhance their performance over time without the need for explicit programming. Its defining features include:

• Data-Driven Insights: Machine learning models depend on vast datasets to uncover patterns and generate accurate predictions.
• Algorithm-Driven Analysis: A range of algorithms is employed to analyze data, extract valuable insights, and optimize decision-making processes.
• Adaptive Learning: These models continuously improve by learning from new data, refining their accuracy with each iteration.
• Experience-Based Learning: Unlike traditional programming, machine learning systems learn by experience, eliminating the need for manual coding for every scenario.

Machine Learning vs. Traditional Programming

Machine learning is when both data and output are run on a computer to create a program that can then be used in traditional programming. And traditional programming is when data and a program are run on a computer to produce an output. Whereas traditional programming is a more manual process, machine learning is more automated. As a result, machine learning helps to increase the value of embedded analytics, speeds up user insights, and reduces decision bias.

Machine Learning and Artificial Intelligence

While machine learning is a subset of artificial intelligence, it has its differences. For instance, machine learning trains machines to improve at tasks without explicit programming, while artificial intelligence works to enable machines to think and make decisions just as a human would.

Machine Learning and Deep Learning

Deep learning is a subset of machine learning, and it uses multi-layered or neural networks for machine learning. Deep learning is well-known for its applications in image and speech recognition as it works to see complex patterns in large amounts of data.

What are the key elements of machine learning?

There are three main elements to every machine learning algorithm, and they include:

• Representation: what the model looks like; how knowledge is represented
• Evaluation: how good models are differentiated; how programs are evaluated
• Optimization: the process for finding good models; how programs are generated

What are some machine learning applications?

Machine learning helps software applications become even more accurate at predicting outcomes without being explicitly programmed. More and more industries are employing machine learning in the following ways:

• Web search and ranking pages based on search preferences.
• Evaluating risk in finance on credit offers and knowing where is best to invest.
• Predicting customer churn in e-commerce.
• Space exploration and sending probes to space.
• The advance in robotics and autonomous, self-driving cars.
• Extracting data on relationships and preferences from social media.
• Speeding up the debugging process in computer science.

Types of Machine Learning Explained

Machine learning can be categorized into three main types, each tailored to specific applications:

• Supervised Learning: In this approach, the model is trained using labeled data, where the correct output is already provided. It’s widely used for tasks like classification, such as identifying spam emails.
• Unsupervised Learning: Here, the model works with unlabeled data, discovering patterns and relationships independently. This method is ideal for clustering tasks, like segmenting customers in marketing strategies.
• Reinforcement Learning: This type of learning relies on trial and error, where the model is rewarded for taking desirable actions. It’s commonly applied in fields like robotics, gaming, and autonomous systems.

Each method serves a distinct purpose, enabling machines to solve diverse problems effectively.

How does machine learning work?

When it comes to the different types of machine learning, supervised learning and unsupervised learning play key roles. While supervised learning uses a set of input variables to predict the value of an output variable, unsupervised learning discovers patterns within data to better understand and identify like groups within a given dataset.

Supervised Learning

Supervised learning is the most common type of machine learning and is used by most machine learning algorithms. This type of learning, also known as inductive learning, includes regression and classification. Regression is when the variable to predict is numerical, whereas classification is when the variable to predict is categorical. For example, regression would use age to predict income, while classification would use age to predicate a category like making a specific purchase.

Within supervised learning, various algorithms are used, including:

• Linear regression
• Logistic regression
• Decision trees
• Random forest
• Gradient boosting
• Artificial neural networks

Unsupervised Learning

Unsupervised learning is useful when it comes to identifying structure in data. There are many situations when it can be near impossible to identify trends in data, and unsupervised learning is able to provide patterns in data which helps to inform better insights. The common type of algorithm used in unsupervised learning is K-Means or clustering.

What is there to consider when it comes to machine learning?

With machine learning, you want to understand the basics, and you also want to be aware of the algorithms that underpin machine learning. To get started, you want to:

• Collect and prepare data
• Choose a training model or algorithm
• Evaluate a model
• Hyperparameter tune
• Make predictions

Domo has created a Machine Learning playbook that anyone can use to properly prepare data, run a model in a ready-made environment, and visualize it back in Domo to simplify and streamline this process. Since building and choosing a model can be time-consuming, there is also automated machine learning (AutoML) to consider. AutoML helps to pre-process data, choose a model, and hyperparameter tune.

Domo’s ETL tools, which are built into the solution, help integrate, clean, and transform data–one of the most challenging parts of the data-to-analyzation process.

How do businesses use machine learning basics?

Machine learning helps businesses reach their desired outcomes faster. Machine learning can help businesses improve efficiencies and operations, do preventative maintenance, adapt to changing market conditions, and leverage consumer data to increase sales and improve retention. Machine learning is even being used across different industries ranging from agriculture to medical research. And when combined with artificial intelligence, machine learning can provide insights that can propel a company forward.

What does the future hold for machine learning?

There are countless opportunities for machine learning to grow and evolve with time. Improvements in unsupervised learning algorithms will most likely be seen contributing to more accurate analysis, which will inform better insights. Since machine learning currently helps companies understand consumers’ preferences, more marketing teams are beginning to adopt artificial intelligence and machine learning to continue to improve their personalization strategies. Additionally, machine learning and deep learning are going to evolve. For instance, with the continual advancements in natural language processing (NLP), search systems can now understand different kinds of searches and provide more accurate answers. All in all, machine learning is only going to get better with time, helping to support growth and increase business outcomes.

Real-World Applications of Machine Learning

Machine learning is transforming industries by boosting efficiency, enhancing decision-making, and driving innovation. Here are some of its most impactful applications:

• Recommendation Systems: Streaming platforms and e-commerce websites harness machine learning to deliver personalized content and product suggestions tailored to user preferences and behavior.
• Image Recognition: Computer vision technology powers applications like facial recognition, object detection, and image classification, playing a vital role in security systems and autonomous vehicles.
• Speech Recognition: Virtual assistants and transcription tools rely on machine learning to accurately convert spoken language into text, making communication more seamless.
• Fraud Detection: Financial institutions utilize machine learning to analyze transaction data and detect unusual patterns, helping to prevent fraudulent activities.
• Medical Diagnosis: AI-driven healthcare tools support doctors by interpreting medical images and diagnosing diseases, paving the way for more accurate and timely treatments.

Machine learning continues to reshape how we work, live, and innovate, unlocking new possibilities across countless sectors.

How Machine Learning Models Improve Over Time

Machine learning models refine their predictions and accuracy by continuously learning from new data. The key mechanisms for improvement include:

• Retraining with fresh data: As new data becomes available, models are retrained to improve performance.
• Fine-tuning hyperparameters: Adjusting algorithm parameters enhances prediction accuracy.
• Transfer learning: Pre-trained models are adapted to new but related tasks to accelerate learning.
• Eliminating biases: Continuous monitoring ensures models remain fair and accurate over time.

Additionally, advancements in model architectures, such as transformers and neural networks, enable more efficient learning and better handling of complex data. Improved computational power and scalable infrastructure also play a critical role in speeding up the training and optimization processes for machine learning models.