A Short Introduction to Machine Learning
In computing, a program takes in data and then processes it to produce an output. This data processing is done by the algorithm of the program/software.
The algorithm is basically the set of sequential data processing steps that are used to process the data input into the desired data output.
If this algorithm can learn from previous iterations how to better solve a problem using the data provided (the data input), then the algorithm is said to possess artificial intelligence.
Artificial intelligence thus has 2 components – learning and adaptive problem-solving. This learning domain forms the core of machine learning.
This is the branch of artificial intelligence that allows an algorithm to improve its problem-solving capacity without being programmed explicitly to increase this capacity, but instead uses the multiple iterations of processing data (input) to produce an output and then comparing how this output is close to the ideal output.
Basically, the algorithm automatically works out – after many iterations – how best to use the available data input to perform its task so as to produce a more perfect output.
As a field of computational science, machine learning (abbreviated as ML) is described as the study of algorithms that automatically improve their problem-solving capacity through running iterations of data analytics and data processing tasks.
Equally, it can be said that machine learning is the study of how an algorithm uses its data processing and analysis operations to improve its predictive accuracy or decision-making capabilities without being programmed to do so by a human programmer.
In the field of data science, ML is used to build programs that can learn from their data input how best to produce the desired output.
Machine learning is used to build applications such as Shazam that can listen to a song and then identify its name and singer based on its voice recognition algorithms and natural language processing capabilities.
It is also used in computational finance for algorithmic trading and credit scoring, and in computer vision for image processing, face detection, object detection, and motion detection.
It is also used for load forecasting in energy production and predictive maintenance by utility companies, as well as a medical diagnosis and drug discovery in computational biology.
Moreover, it is used in robot vacuum cleaners among other smart devices that are used in the home and office. Additionally, ML is critical to the operations of self-driving cars.
Basically, ML is a subset of data science that improves the work efficiency of applications that compute large sets of data.
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In Machine Learning, the algorithm needs to be trained to identify features and patterns in large data sets that can then be used to improve the way this data is processed, or optimize its predictive accuracy and decision-making capacity.
This training requires the algorithm to repeatedly process varied data sets and assess how well it adapts to the provided data in terms of producing the desired output.
Algorithm training requires one to build an ML model based on ML algorithms. This model is to be fed training data.
Training data is a set of data that has been randomly selected from the input data so that it can be processed by the ML model.
The training data is meant to be the representative data of the data pool, and it can be classified, labeled, or tagged based on its attributes or properties.
This type of training data is called labeled data. After the ML model works on the labeled data, it can then be fed unlabeled data and assessed on how best it extracts the attributes that can be used to assign classifications of the unlabeled data.
Basically, after working on labeled data, the ML model is expected to be able to take unlabeled data and classify and tag its data so as to process it into labeled data.
The labeled data is called the training subset, while the unlabeled data used to test how well the ML model works is called the evaluation subset.
The main ML algorithms used in creating ML models are instance-based algorithm, decision tree, or regression algorithm (including support vector machine for difficult-to-classify labeled data) for processing the labeled.
Meanwhile, the ML algorithms used for processing unlabeled data can be the clustering algorithm, neural networks (including a deep neural network that has multiple hidden layers of calculation), and association algorithm.
The ML model is an algorithm built using an ML algorithm. If this model produces output that is close to the perfect (desired) output after algorithm training, then it is said to be an accurate, trained algorithm that can be used to develop a data science application or software.
The accurate, trained algorithm is sometimes simply designated as the working ML model so as to differentiate it from the untrained ML model.
The working ML model can be fed with new data so as to improve the effectiveness of its trained algorithm. This allows the working ML model to be improved before it enters the marketplace and starts working with real-world data.
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Machine Learning Methods
There are 3 primary machine learning methods that are used to develop accurate, trained algorithms.
Supervised Machine Learning
The ML model uses labeled training subset data for algorithm training. The process of labeling and tagging the training subset data can be expensive and can be over-fitted with very many labels and tags.
Unsupervised Machine Learning
The ML model uses unlabeled training subset data and then works out how to identify features and patterns that can be used to sort, label, and classify the data. Unsupervised ML is useful in identifying patterns in big data that humans can easily miss, and this makes it useful in applications such as spam detection for mail filtering.
Semi-supervised Machine Learning
This ML uses a small volume of labeled training subset data for algorithm training and then uses the learned feature extraction and classification capabilities to process large sets of unlabeled data so that it can label and classify it.
Reinforcement Learning and Deep Learning
Reinforcement ML is a type of behavioral ML learning method that uses trial and error during processing of unlabeled training data subset to determine successful outcomes and then prioritizing the algorithms that created these outcomes.
These algorithms are then chosen for use and improvement upon processing more data, that is (i.e), the algorithms are reinforced in the ML model. It is considered an atypical form of supervised ML.
Deep Learning is a form of ML learning method that uses an artificial neural network that applies deep neural network that uses layers of calculations to process large volumes of data, with each layer of calculation assigning biases and weight to the data.
It is considered an atypical form of semi-supervised and unsupervised ML.
So, where can one learn about ML? One of the best education technology platforms that offer ML courses is Udemy. In this platform, one will find lots of ML courses, 10 of the best we have recently reviewed.