Machine learning is a subset of Artificial Intelligence which studies algorithms and statistical models. It aims to effectively perform certain tasks such as processing the natural language (NLP).
You could also say that Machine learning calculations utilize computational techniques to “learn” data specifically from information without depending on a predetermined condition as a model. The calculations adaptively improve their execution as the quantity of tests and data accessible for learning increments.
Why Machine Learning Matters
With the ascent in enormous amounts of data, machine learning has turned into key technique for solving problems in areas such as:
- Computer vision and image processing used for face recognition and object detection
- Natural language processing, for voice recognition
- Biological applications, for tumor detection and drug discovery
- Automotive and manufacturing, for predictive maintenance
- Computational finance used for algorithmic trading i.e. predicting stock prices
When Should You Use Machine Learning?
Machine learning is used when we are dealing with a large amount of data that is influenced by a lot of factors. It is mostly used when we want to build systems that have ability to automatically learn and improve from experience without being explicitly programmed.
How Machine Learning Works
Machine learning incorporates two types of techniques: Supervised and Unsupervised learning.
In supervised learning, sample inputs and outputs are presented to the model. It then trains on the data given to predict future outputs when given different inputs.
Types of Supervised Learning include:
- Artificial neural network
- Bayesian statistics
- Decision tree learning
- Gaussian process regression
- Genetic Programming
- Learning Automata
- Learning Classifier Systems
- Naive bayes classifier
- Support vector machines
- Random Forests
- Ordinal classification
- Data Pre-processing
- Handling imbalanced datasets
In supervised learning, a model makes predictions from a function that maps an input to an output based on example input-output pairs. In the same way, a supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.
A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data.
Supervised learning is used if you have known data for the output you are trying to predict. For instance, the correlation between the number of people who love football in a certain population. The data is then scaled to the larger population. In this case, the input is the people who love football and the output is the population. A function is generated for the ratio which you could scale to the larger population. This is the simplest type of supervised machine learning you can come by. This is because it can be easily plotted in a x-y graph to get the correlation which will be the gradient. Thus, a type of model called Stochastic Gradient Descent can be used here.
Supervised learning uses classification and regression techniques to develop predictive models.
The Classification Technique:
This is a predictive model that classifies input data to various categories. Not only is a classifier used to test spam mail, hate comments on social media, but also, it tests the stages of tumor cells or if an image is a dog or a cat. Applications of classification include speech recognition, credit scoring, sentiment analysis and image processing.
Use classification if your information can be labeled, arranged, or isolated into explicit classes. For instance, applications for recognition and computer vision.
Algorithms used for classification include:
- Linear Classifiers: Logistic Regression, Naive Bayes Classifier.
- Support Vector Machines.
- Decision Trees.
- Boosted Trees.
- Random Forest.
- Neural Networks.
- Nearest Neighbor.
The list is endless.
Regression techniques predict continuous responses. It is used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. The population sample is an example in which we try to find the relationship between the independent variable (football lovers and the dependent variable (population).
Common regression algorithms include:
- Linear Regression.
- Logistic Regression.
- Decision Tree.
- Naive Bayes.
- Random Forest.
Unsupervised learning finds concealed examples or intrinsic structures in information. Furthermore, it is used to draw derivations from datasets comprising of data that is not labeled.
Clustering is the most widely recognized unsupervised learning method. It is used for data analysis to discover hidden patterns in data. For example, can be used in market research, pattern recognition, data analysis, and image processing. Clustering can also help marketers discover distinct groups in their customer base.
For example, a smartphone that ships out its flagship models internationally can cluster and optimize its customer base. In this way, it sees which country is the best bet to buy the smartphone and ships there more models.
For instance, common algorithms for performing clustering include k-means and k-medoids, hierarchical clustering, Gaussian mixture models and hidden Markov models.
How Do You Decide Which Machine Learning Algorithm to Use?
Picking the correct algorithm can appear to be a daunting task. However, this process is easy. There are many supervised and unsupervised machine learning algorithms and each adopt an alternate strategy to learning.
There is no best technique for all tasks. Finding the correct algorithm is somewhat just trial and error – even with the most experienced data scientists. Additionally, algorithm choice relies upon the size and kind of information you’re working with. Also, into considerations are the bits of knowledge you need to get from the information and how those outputs will be used.
Here are a few tips when picking a machine learning model:
Choose supervised learning if you have to prepare a model to make an prediction.
However, choose unsupervised learning when searching for the function that links the inputs to outputs. Clustering, representation learning, and density estimation are example use-cases. As seen above, with unsupervised learning, we learn the inherent structure of our data without using explicitly-provided labels.
Finally, Thank you for reading this article. For more machine learning and data science related content, visit this blog.
- What Is Machine Learning? 3 things you need to know.
- What is Machine Learning? – Course by Cousera.
- Learn Machine Learning in 3 Months (with curriculum).
- Supervised Learning by Wikipedia.
- Unsupervised Learning by Wikipedia.
- The 5 Clustering Algorithms Data Scientists Need to Know.
- Cluster Analysis by Wikipedia.
- Supervised vs. Unsupervised Learning.
- 7 Types of Classification Algorithms.