10 Machine Learning Techniques for AI Development

Learning is defined as the adaptive intelligent change in response to experience. In humans, it refers to the continuous process of acquiring new skills, values, knowledge, attitude, and preferences based on past experiences. This lifelong exercise in humans is called cognitive learning.

The process of learning in machines is quite similar to humans. The machines review the existing data and learn new skills from it. This continuous learning cycle is known as Machine Learning (ML). 

Machine Learning is the branch of Artificial Intelligence (AI) and computer science that deals with the use of data and algorithms to imitate the learning process of humans. The ML technology is known for its predicting potential from a large set of data, which is beyond the processing power of humans. It is because of this ability of ML that almost every industry (healthcare, finance, retail, advertising, manufacture, transportation, etc.) is using Machine Learning as the underlying technology for most of their IT transformation roadmaps. 

Data set, algorithms, models, feature extraction, and training are 5 crucial components of Machine Learning. Depending upon the scenario, data type, input, etc., there are different ways an ML model learns. These are supervised and unsupervised learning methods. 

In supervised learning, the machine classifies objects, problems, and scenarios based on data that has been fed to them through data sets. On the other hand, in unsupervised learning, the machines learn on their own, discover information, create patterns, and then label the data. Know more about these machine learning categories through our blog that discusses supervised and unsupervised learning in detail. 

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There are certain techniques using which a model can be trained. Now that you’re familiar with the basics, you can move ahead and read the upcoming segment of this blog post that shares 10 Machine Learning techniques for AI development companies. Let’s get started. 

1. Regression (Supervised ML)

Regression analysis is a predictive modeling technique. It creates a relationship between the dependent (target) and independent (predictor) variable (s). The models created using this Machine Learning technique helps to understand how the value of dependent variables is changing corresponding to independent variables. For this reason, regression analysis is highly recommended for making predictions. For example, it can help determine blood pressure (dependent variable) of different ages, gender, and weights (independent factors). This can further help in health issues that happen due to fluctuations in blood pressure. 

There are 7 types of regression techniques: Linear, Logistic, Ridge, Lasso, Polynomial, and Bayesian. Amongst all these, Linear and Bayesian regression models are the most used and popular machine learning techniques. 

2. Classification (Supervised ML)

Classification is a technique where data is categorized into several classes. The process involves recognizing and grouping objects/ideas into categories. It uses pre-categorized training datasets to classify the given datasets into categories. One of the common examples of this machine learning technique is filtering emails into the ‘spam’ or ‘not spam’ category. 

There are seven ways to classify ML datasets: Logistic Regression, K-Nearest Neibhours, Random Forest, Decision Tree, Stochastic Gradient Descent, Naive Bayes, and Support Vector Machine. 

3. Transfer Learning (Supervised ML)

Transfer Learning refers to the process of reusing an already trained data set and using it to perform a new but similar task. Once a data set is trained for a task, a fraction of trained layers can be transferred and combined with the layers of a new dataset. This way, the ML algorithm can learn and adapt quickly to the new task. 

Transfer learning overcomes several challenges involved in training a dataset. Since this machine learning technique requires less amount of data to be trained, it proves less expensive in terms of computational resources. Moreover, with an already existing dataset, there is enough labeled data that can help in accomplishing the new task. 

4. Clustering (Unsupervised ML)

In the clustering learning technique, the goal is to group or cluster the observations that have similar characteristics. Unlike supervised training methods, clustering does not use the output to train the dataset and provide an output. Instead, it helps the algorithms to define the output.

Clustering involves the grouping of unlabeled data. That is why this machine learning technique comes under unsupervised learning. If the data would have been labeled, the technique would have been the classification.

Some of the common applications of clustering in the real world include market segmentation,  search result grouping, anomaly detection, medical imaging, social network analysis, image segmentation, etc. 

AlSO READ: Top 12 Clustering Algorithms in Machine Learning

5. Ensemble Methods (Supervised ML)

The Ensemble model ensures that you get the best of all worlds. It uses the idea of combining various predictive models to get precise predictions, which an individual model might fail to provide. 

The ensemble methods reduce the variance and bias of a single ML model. This is important; an ML model might work with precision under one circumstance and might fail under another. When there is another model available, it will help to preserve the accuracy. The prediction quality is balanced with multiple predictive models.

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6. Neural Networks & Deep Learning (Unsupervised ML)

Neural networks are a biologically-inspired programming paradigm that enables a machine to continuously learn from observational data. Deep learning involves a set of techniques that make the neural networks learn.

Deep learning technology helps the machine imitate the human brain. This machine learning technique has some amazing applications in areas of vision (image classification), text (text mining), audio (speech recognization), and video (computer vision). 

There are different types of neural networks that can be trained using this machine learning technique- Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). However, the major challenge associated with this learning method is it requires a lot of data and computational power (enhanced Graphical Processing Units). 

ALSO READ: CNN vs. RNN: What’s the Difference?

7. Dimensionality Reduction (Supervised & Unsupervised Learning)

Dimensionality reduction is a data presentation technique. It is the process of transforming the data from a high-dimensional space into a low-dimensional space. This helps the low dimensional representation retain significant properties of the original data which is quite near to its intrinsic dimension. 

High dimensional data means hundreds and thousands of inputs. When the data is reduced to a low dimensional structure, the number of inputs reduces significantly. This means fewer parameters or a simpler structure in the ML model. Also, fewer dimensions mean less computation time. 

The dimensionality reduction technique has its applications in noise reduction, cluster analysis, data visualization, etc. Some of the common techniques for dimensionality reduction include Missing Value Ratio, Backward Feature Elimination, Independent Component Analysis, Random Filter, etc. 

8. Word Embeddings (Unsupervised Learning)

A Word embedding is a learned representation of text. In this machine learning technique, the words with the same meaning have a similar representation. These words can also have approximate meanings. For example, these words are somehow related to one another- age, fitness, sports, etc. The benefit of word embeddings is it helps to reduce dimensionality, for word prediction, etc. 

9. Natural Language Processing (Unsupervised Learning)

Natural Language Processing is the technique of making machines understand the text and spoken words, just like humans do. NLP combines computational linguistics with statistical, ML, and deep learning models. 

Some of the common applications of NLP include machine translation, text prediction, sentiment analysis, text classification, autocomplete & autocorrect, name entity recognition, co-reference resolution, natural language generation, speech recognition, etc. 

To reduce the complexity involved in Natural Language Processing, there are several pre-trained NLP models available. Developers/researchers can fine-tune the existing models and make them do a better, complex task without having to build a new model from scratch.  

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10. Reinforcement Learning (Unsupervised Learning)

In Reinforcement learning, the machines find the best possible solution or way to act in a specific situation. It differs from supervised learning models in a way that there is a data set available with an answer key to give the correct answer. However, in the reinforcement model, the absence of a dataset binds the machine to learn from its own experience. 

The Wrap Up

Depending upon the scenario, the machine learning technique for AI development varies. Selecting the right technique is based on the type of project, data set availability and proficiency of the developer/researcher. At Daffodil, our AI development team takes care of these factors for development and ensures that the most suitable technique is adopted. 

Is there an AI development project in your business development roadmap this year?  Connect with our domain experts to know more about the right technology, approach, budget, and timelines for development. Schedule a free consultation session now!

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