Machine Learning Techniques Every Data Scientist Should Know
The tech industry is investing its time and resources in exploring the vast concept of machine learning. Everyone is looking for new ways to make the machine more efficient and accurate, from individual developers to machine learning consulting firms. The field’s speed and complexity keep up with new innovations and it is the most overwhelming topic for beginners. And this is why ML is the largest segment of AI to experience breathtaking growth in the industry, the figures estimate the ML market growth to cross $126 billion by 2025. As the field grows, new machine-learning techniques emerge that derive great results. This blog covers the best methods that totally transform the machine learning workflow. But first, let us understand what a machine learning method is.
What is a Machine Learning Technique?
A machine learning technique is an algorithm, sometimes referred to as a model, representing data in the context of a problem. Each machine-learning method aims to generate useful insights into that problem that helps businesses to identify the problem’s cause and devise a possible solution for the relevant occurrence.
Mainly machine learning is categorised into two parts, supervised and unsupervised learning. Let’s differentiate between them before diving deeper into ML algorithms.
In this approach, the machine-learning model uses the existing piece of data to predict or explain the output from a new input. For example, supervised learning can help a business to predict the number of new users by estimating the previous data.
In an unsupervised model, the system relates groups of data without the use of a target variable. In other words, this technique clusters unrelated data sets to generate imaginative content from them. For example, it is useful for businesses to segment products with different characteristics.
Top 5 Methods for Machine Learning Approach
Here are the ten most useful algorithms every data scientist should know to move forward in the machine learning industry.
The regression algorithm falls in the supervised learning category. It involves predicting a continuous numerical output value based on input features. In regression, the model is trained to learn the relationship between the input and the output value, and then use that model to make predictions on the new data. Some common examples include predicting house prices by analysing the features such as location, size, and the number of bedrooms. The simplest method to utilise a linear regression model is using the mathematical equation of the line “(y = mx + b)” for modelling a dataset.
This is a type of supervised learning in machine learning, where the goal is to learn a function that maps input data to predefined output classes. The input data is typically a set of features, and the output classes are discrete labels or categories. The learned function can then be used to predict the class of new, unseen data.
An example of classification in machine learning is image classification, where the task is to assign a label or class to an input image. For instance, we can train a classification model to identify whether an image contains a cat or a dog. The input features could be the image’s pixel values, and the output classes could be “cat” and “dog”. After training the model, it can take an unseen image as input and predict the class label with a certain degree of confidence.
Moving on to the next machine learning technique, clustering falls in the unsupervised category of machine learning. The goal of clustering as the name suggests is to group observations that have similar characteristics into clusters. This method does not output training observations but lets the applied algorithm define the output from the clusters. The most popular example of the Clustering technique is the “K-Means” where K represents the number of clusters.
In contrast to the linear and logistic regressions which are mostly considered linear machine learning models, Deep learning or Neural Networks work to achieve the objective of non-linear patterns by adding multiple layers of parameters to the model. Neural networks are algorithms inspired by the human brain’s structure and function. In deep learning, neural networks are organised into layers of nodes, with each layer responsible for processing and transforming the input data to produce a desired output.
The term “deep” refers to the detail of the neural network, which is the number of layers it has. Deep learning models can have many layers and can learn complex patterns and relationships in the data they are trained on such as image and speech recognition, natural language processing, and autonomous driving.
Reinforcement learning is the next technique in our list of machine learning techniques. To understand it better, let’s take an example of a mouse in a maze, looking for hidden pieces of food. The more times it is exposed to the maze, the more the mouse knows the route. This is exactly what data scientists do in Reinforcement learning when training a system or a game. In RL, the machine learns by experience using a trial & error approach in a set environment. It can maximise the reward on multiple trials.
To sum it up, machine learning techniques have revolutionised the field of computer science and have significantly impacted many industries. They have enabled us to develop systems that can learn and improve from experience, and make predictions and decisions based on data.
From supervised learning to unsupervised learning and reinforcement learning, each technique has its own strengths and weaknesses and can be applied to a wide range of problems.
With the increasing availability of data and computing power, machine learning techniques are becoming more sophisticated and powerful, driving innovations in healthcare, finance, and transportation.
As research in the field continues, we can expect to see even more advanced techniques and applications of machine learning in the future.