What is Machine Learning
Ever wondered how your instagram feed is always loaded with the content which matches your interests? well to understand we need to learn How Algorithm works for these Apps and how Machine Learning algorithms are implemented the backend!
In this article, we will briefly explore What Machine learning is all about. and also learn terms which are frequently used in machine learning field.
Machine Learning is integral part of the AI. The machines are usually trained by variety of data sets, user inputs, behavior etc. based on algorithm trained machine can select
Major sources of data for an algorithm to learn from can be in different formats like databases, Excel files, Images, text files, websites and so on…
Data Cleaning & Processing
The data on which the machine algorithm needs to be trained must be cleaned, preprocessed in order to accurately train the algorithm. In this process the data is checked for errors, missing values in tables etc.
Then below major steps are followed to train the machine.
- Organize data is specific format suitable for machine learning technique to be implemented e.g. supervised learning, unsupervised learning or there several other methods as well!
- Train the algorithm based on ML method selected.
- Next important step is to evaluate the performance of the algorithm where we gauge the accuracy in prediction of output, precision, recall and mean squared error.
- To ensure accuracy and efficiency hyperparameter tuning & optimization ** techniques are used. which includes grid search and cross validations.
Real life examples of Machine learning
Machine learning is embedded in our everyday life, from Spotify recommendations to Instagram filters. As machine learning systems and technologies gain widespread use, the need for skilled data professionals in this field continues to grow.
Let us know in comment box if you wish to deep dive any specific relevant topics…
Here are some definitions and reference books if you wish to read more about Machine Learning!
If you are interested learning about AI and its use case as Human augmentation which also uses different machine learning models which are discussed here pls navigate to the Link.
Definitions and Frequently used term
It is really important to learn how algorithm works if you are keen to understand how technology is evolving around the machine learning techniques, so here is brief introductory section for understanding the key terms used in machine learning domain and techniques implemented.
Machine Learning Techniques
Based on the nature of leaning and available data Machine Learning is classified as Supervised learning, Unsupervised learning, Semi-supervised learning and Reinforcement learning.
Supervised Learning
In supervised learning the machine learning algorithm is trained on a labeled dataset, where each input data point has a corresponding output value. The algorithm learns to map the input value to the output value and make predictions on new data. Classification and regression tasks are commonly completed by making use of supervised learning algorithms.
Unsupervised Learning
In unsupervised learning, the machine learning algorithm is trained on a unlabeled dataset where the data points do not have any corresponding output values. The algorithm learns how to find patterns and relationships in the data without any prior knowledge of data. Anomaly detection tasks and clustering like tasks are completed by making use of Unsupervised machine learning algorithms.
Semi-supervised learning
Semi-supervised learning combines the elements of both supervised and unsupervised learning. In this approach the machine learning algorithm is trained on both the datasets that contains labeled as well as unlabeled data. The algorithm uses labeled data to learn the basics of the task and then uses the unlabeled data to refine its knowledge and improve its predictions. Semi-supervised learning algorithms are commonly used for classification, regression tasks where there is limited labeled data available.
Reinforcement Learning
In Reinforcement learning the machine learning agent learns to interact with its environment and perform actions in order to maximize its rewards. The agent receives a reward or punishment for each action it takes and it learns to choose actions that lead to higher rewards overtime. Reinforcement learning algorithms are commonly used for robotics and game playing tasks.
Hyper parameter tuning and Optimization.
Where you divide your data into subsets and train your model on each subset to ensure it performs well on different data.
Refernces
some beginner friendly books to start your ML journey are Machine Learning for Absolute Beginners (by Oliver Theobald), The Hundred-Page Machine Learning Book (by Andriy Burkov) and Machine Learning for Dummies (by John Paul Mueller and Luca Massaron.
Learn here more about how AI is redefining the medical field with the help of ML
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