Machine Learning 101: A Beginner’s Guide to Understanding the Basics

Learn all the basic machine learning concepts beginners need.

5 min readDec 8, 2022

What is Machine Learning?

Overall, machine learning has many potential applications and is being used in many different fields to solve complex problems and make better decisions using data.

Machine learning is a type of artificial intelligence that allows systems to improve their performance on a specific task by learning from data, without being explicitly programmed. Machine learning algorithms use data to learn about patterns and relationships in the data, and can then use that knowledge to make predictions or take actions.

Machine learning is useful in many different fields, including healthcare, finance, and e-commerce. For example, machine learning can be used to process large amounts of medical data to identify patterns and trends, or to make predictions about the likelihood of a patient developing a particular disease. In finance, machine learning can be used to identify fraudulent transactions or to make recommendations for investment decisions. In e-commerce, machine learning can be used to personalize product recommendations for customers.

Overall, the importance of machine learning lies in its ability to enable us to solve complex problems and make better decisions using data. This can help improve the efficiency and accuracy of many different processes, and can provide valuable insights that would not be possible using traditional approaches.

How Does Machine Learning Work?

Machine learning algorithms are trained on a dataset, which is a collection of data examples that the algorithm uses to learn about the patterns and relationships in the data. The training process involves feeding the algorithm the data examples, and then adjusting the algorithm’s parameters to minimize the error between the predicted output and the actual output.

**Figure 1**: How Machine Learning Works

Once the algorithm is trained, it can be applied to new data to make predictions or take actions. For example, if the algorithm was trained on a dataset of images, it could be used to classify new images based on their content. Or, if the algorithm was trained on a dataset of customer data, it could be used to make recommendations for products or services.

History of Machine Learning

The field of machine learning has a long history, with roots dating back to the 1950s. Some key milestones in the development of machine learning include:

In 1950, Alan Turing published a paper titled “Computing Machinery and Intelligence,” in which he proposed the “Turing test” as a way to determine whether a machine has human-like intelligence.
In 1959, Arthur Samuel published a paper on “Self-learning programs,” which described the first program that could learn from data. The program was able to improve its performance at playing checkers by learning from its own mistakes.
In the 1960s and 1970s, researchers developed a number of algorithms for supervised learning, including the perceptron and the linear discriminant analysis.
In the 1980s, researchers began to focus on neural networks, which are inspired by the structure of the human brain.
In the 1990s, the field of machine learning experienced a resurgence with the development of new algorithms and techniques, such as support vector machines and decision trees.
In the 2000s, machine learning became more widely used in industry, and the development of new algorithms and techniques continued, including the emergence of deep learning, which uses multiple layers of neural networks.

Overall, the field of machine learning has a long history and has evolved significantly over the years, with many key developments and innovations along the way.

Figure 2: A Timeline of Machine Learning History

Types of Machine Learning

There are several different types of machine learning, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

Supervised learning: This is the most common type of machine learning, and it involves training the algorithm on a labeled dataset, where the correct output is provided for each example in the training set. The algorithm uses the labeled examples to learn the mapping between the input and the output, and can then use that knowledge to make predictions on new data.
Unsupervised learning: In unsupervised learning, the algorithm is not provided with labeled examples, and must learn to identify patterns and relationships in the data on its own. This type of learning is useful for tasks such as clustering, where the goal is to group similar data points together.
Semi-supervised learning: This type of learning is a combination of supervised and unsupervised learning, where the algorithm is trained on a dataset that contains both labeled and unlabeled examples. This can be useful in situations where it is difficult or expensive to label a large dataset, and the algorithm can use the labeled examples to guide its learning on the unlabeled data.
Reinforcement learning: In reinforcement learning, the algorithm learns by interacting with its environment and receiving feedback in the form of rewards or penalties. This type of learning is useful for tasks such as playing games or controlling robots, where the goal is to take actions that maximize the long-term reward.

Applications of Machine Learning

Machine learning is being used in many different fields and has the potential to transform many different industries. Some examples of the applications of machine learning include:

Healthcare: Machine learning can be used to process large amounts of medical data to identify patterns and trends, or to make predictions about the likelihood of a patient developing a particular disease. This can help doctors make more accurate diagnoses and provide more personalized treatment plans for their patients.
Finance: Machine learning can be used to identify fraudulent transactions or to make recommendations for investment decisions. This can help financial institutions reduce the risk of fraud and make more informed investment decisions.
E-commerce: Machine learning can be used to personalize product recommendations for customers. This can help e-commerce companies increase sales by showing customers products that are more likely to be of interest to them.
Social media: Machine learning can be used to identify and filter out fake news and malicious content on social media platforms. This can help protect users from being exposed to false or harmful information.
Transportation: Machine learning can be used to optimize routing and scheduling for transportation networks, such as for public transit or delivery services. This can help reduce congestion and improve the efficiency of these systems.

Thanks for reading this article. See you in my next post!

If you have any feedback feel free to share it in the comments section or contact me if you need more information.