Author: Samuel Ajala
Artificial Intelligence is a domain of technology that focuses on enabling computers to mimic human intelligence to perform tasks such as learning, reasoning, problem-solving, and decision-making. There are different ways through which this can be achieved, and these ways are grouped into subsets, some of which are Machine Learning, NLP (Natural Language Processing), and Computer Vision
In this blogpost, we will focus on Machine Learning the subset of Artificial Intelligence. Machine Learning is concerned with building systems that try to predict values or find patterns in data given to it, and these results can enable smarter decisions. This system is called a Model, it can be embedded in software as part of an application, or in a hardware system. What does a model do? A model takes in input, processes, and gives results.
Imagine a model designed to predict energy consumption based on three inputs:
- Number of people
- Season (e.g., summer, winter)
- Hour of the day
The model processes these inputs and predicts how much energy is likely to be consumed. This output can then guide energy providers or smart home systems to optimize energy distribution and usage.
A. Basic terms used in Machine Learning
- Dataset: This is the data being used to train the model, under dataset in Machine Learning there are two types:
1. Labeled data refers to data with its input features mapped to its output/target. During the training phase, the machine learning model learns from the labelled data by identifying patterns and relationships between the input features and the corresponding labels(output).
2. Unlabelled Data is a type of data without a target variable or output. Models built using this type of data are not used for predictions but can uncover insights and find patterns for making decisions.
Let’s say you were to build a model, the hello world of ML – a house price prediction model. To get started, you’ll need to gather data, as all AI systems cannot be built without data, you will need to train your model to find hidden patterns. This data would then be split into two, one called the training set and the other the test set. The training set, the training set is used to train the model, while the test set is used to measure how well your model has performed.
B. What are the types of Machine Learning?
There are three types or methods to achieve machine learning, and these methods are used to achieve different results:
- Supervised Machine Learning: This is a type of ML that depends on labelled data to find patterns or make predictions. Most of the ML models today use this type of learning. The two most common algorithms used for supervised learning are: –
- Regression: This algorithm is used to predict numerical values that are continuous, for example, predicting house prices. Regression aims to understand the relationship between the dependent and independent variables.
- Classification: This algorithm is what it sounds like, it’s used for predictions that involve a class. Given images of a dog or cat, a classification model can classify whether an image is of a dog or a cat. Classification algorithms are normally used for problems that require discrete outputs.
2. Unsupervised Learning: This type of ML, as you guessed, doesn’t require labelled data as input. Generally, unsupervised ML does not try to predict but rather look for patterns in given data. There are also two main types of unsupervised machine learning: –
- Clustering: This type of unsupervised machine learning creates clusters from data which can then be interpreted by the agent. This ML isn’t used to predict values but to find hidden patterns or trends in data examples of where this can be used are:- News classification(seeing related articles inna section) and a very common one, spam filtering which Gmail uses to identify and isolate spam emails.
- Anomaly Detection: is used to identify unusual patterns or events in a given dataset. It works by studying what’s normal or common in a dataset and then reacts to slight deviations or “anomalies”. It is commonly used in Fraud detection systems.
- Neural Networks: Neural networks are models that emulate how humans process information. They are made up of artificial neurons called nodes or units which have weights and are organized in layers. It is used in many areas, including computer vision, natural language processing, and robotics.
C. Is Machine Learning related to Data Science?
This has been an ongoing conversation; it will interest you to know that Data science is a field that uses mathematical and statistical methods to analyze and interpret data. Data science is considered a subset of machine learning because it uses machine learning techniques to analyze and interpret data. But machine learning is also considered a subset of data science, because it’s a specific way of analyzing data. It is safe to say that they are subsets of each other. Data science is like the parent category, and machine learning is a subcategory within it.
D. How do you get started?
Here are some of the best resources to get started with machine Learning:
- Machine Learning by Stanford and DeepLearning.AI
- Machine Learning with Python – Freecodecamp
- Intro to Machine Learning – Kaggle
If you have any questions, feel free to ask in the comments or through other platforms.