Uploaded on
02 Apr 2023
Skill-Lync
Machine Learning, we’ve all heard these buzzwords being used in nearly all tech products, but what is it exactly? Most companies these days make use of the insights brought along by big data. Companies have always had access to that data; however, manually analysing it is challenging due to the sheer volume of data. Machine learning is a subset of Artificial Intelligence capable of analysing vast amounts of data to find insight and connections in them. There are several machine learning algorithms. Some specialise in data analysis, while others specialise in pattern recognition. This blog will explore the various ML algorithms you need to know!
Machine Learning, or ML, focuses on building programs that are not explicitly programmed. Rather, it learns from data on its own. It is based on the working principle of the human brain.
ML is a field of study that uses algorithms to,
Using large amounts of data and applying ML algorithms can be used to make predictions and decisions that are more accurate than those made by humans.
ML algorithms are the building blocks of modern AI (Artificial Intelligence). ML algorithms are used in a variety of applications, such as,
Supervised learning algorithms use labelled data to make forecasts. These algorithms use the labelled data to learn how to map inputs to outputs. Labelled data helps the algorithm to learn and recognise patterns in the data and make predictions about new data.
Supervised learning algorithms are an important part of machine learning and can be used to predict new data.
Unsupervised learning algorithms are algorithms that work without any labelled data or output data. These algorithms explore and analyse data to discover patterns and insights.
Unsupervised learning algorithms are useful tools for exploring data, finding patterns, and understanding the structure of the data. They are also used for feature extraction and anomaly detection.
Semi-supervised learning algorithms use labelled and unlabeled data to train a model. These algorithms are useful when there is a limited amount of labelled data but a large amount of unlabeled data.
They classify data using labelled data to learn the structure. And uses unlabeled data to fill in the gaps.
Semi-supervised learning algorithms are becoming increasingly popular as they can learn from labelled and unlabeled data and can be used to improve the accuracy of machine learning models.
Unlike supervised learning algorithms, which use labelled data to make predictions, RL algorithms use rewards and punishments to learn how to take the best action in a given situation. RL algorithms are used in a wide range of applications,
RL algorithms are powerful tools for solving complex problems as they can learn from their mistakes and adapt to changing situations, making them ideal for dynamic decision-making applications.
Along with these algorithms, a variety of techniques are used in ML, such as,
These tools and techniques ensure that the machine learning algorithms are trained properly and produce accurate results.
Feature engineering is an essential part of the ML process, as it helps to identify patterns and relationships between variables that can be used to make predictions.
Preprocessing techniques help reduce the data's complexity, make it easier to analyse, and improve the model's accuracy.
Model evaluation measures the performance of models and determines which algorithms are best suited for data and objectives. It is important to evaluate a model before deploying it in production, as it can help identify any potential problems or areas of improvement.
The top model evaluation algorithms that you need to know for include the following:
TensorFlow is an open-source software library for machine learning developed by Google.
PyTorch is an open-source machine learning library developed by Facebook’s AI Research lab.
ML algorithms are an essential part of modern data science. Which can uncover hidden insights, automate tedious tasks, and make predictions with the right algorithms. While there are many machine learning algorithms, the ones mentioned in this blog are some of the most popular and widely used.
To learn about Machine Learning and Artificial intelligence, check our courses like Math behind Machine Learning & Artificial Intelligence using Python and Machine learning Basic. Skill-lync provides courses for engineering graduates to upskill their careers. Do talk with our experts for a free demo session!
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Navin Baskar
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