Machine learning is one of AI use-cases that learn a machine to perform one or another function depending on the user request. Businesses use ML algorithms in multiple spheres: from recommendation algorithm development in video streaming to sales prediction in online retail. In a nutshell, such ML algorithms, known as models, represent the ways to apply data in the context of a business problem, and then, receive insight. They are two main strategies of teaching ML algorithm perform its functions:
- Supervised machine learning, i.e., you train an algorithm using an existing dataset
- Unsupervised machine learning, i.e., you need to find previously unknown patterns in data set without pre-existing labels If you consider entering as a data science field, you should be aware of the ten most popular ML methods that belong to both supervised and unsupervised techniques that data scientists currently use to manipulate data.
Naive Bayes or Classification
The classification method, based on Naïve Bayes theorem, is a probabilistic classifier that belongs to the supervised machine learning. Classification performs well to categorize the input data into predefined groups. You can apply the algorithm to assume that the presence of a particular feature in a class is unrelated to the presence of any other feature. You can use this algorithm for:
- Text classification, i.e., sentiment analysis
- Document categorization
- Spam filtering
- News classification
Regression
Regression in another ML method that belongs to supervised algorithms. The definition of regression is a data mining function to predict a number based on a set of previous data. These techniques could be used for simple linear regression to more complex regressions, such as polynomial, decision tree, and random forest regressions. Regression models have numerous use cases across the following industries:
-Financial forecasting
-Trend analysis
-Marketing
-Time-series prediction
-And even drug response modeling
Clustering
Clustering, which belongs to unsupervised machine learning methods, allows us to draw references from datasets that consist of input data without label responses. You can apply this algorithm for finding:
- Meaningful structure
- Generative features
- Explanatory underlying processes
- Or grouping inherent in a set of examples Clustering methods only apply visualization of elements to inspect the quality of the solution.
Dimensionality Reduction
Dimensionality Reduction is a method of unsupervised machine learning. In this context, ‘dimensionality’ means the number of features (i.e., input variables) in your dataset.
By using Dimensionality Reduction, you can reduce or remove the number of random variables we take into consideration by obtaining a set of principal variables. In other words, this method allows you to find a smaller set of new variables containing the same information as the input variables.
Ensemble Methods
Such methods leverage several supervised machine learning methods at once to receive a higher quality prediction compared to the forecast each model could provide on its own. Ensemble methods in machine learning are divided into two main groups:
- Sequential methods that exploit the dependence between the base learners
- Parallel methods that exploit independence between the base learners which reduce the number of errors by averaging The example of the ensemble method is Random Forest algorithms that apply the combination of several Decision Trees, previously trained using different samples of the data set. In this way, ensemble methods decrease variance (bagging), bias (boosting), or improve predictions (stacking).
Neural Networks and Deep Learning
Inspired by the human brain, neural networks include a set of algorithms developers that apply clustering raw input, labeling, and machine perception to recognize patterns. Such patterns could be in forms of numbers, vectors, images, sounds, and text. To achieve this, neural networks use different input, output, and even hidden layers of parameters to the model. Thanks to Neural Networks' flexible stature, you can add as many layers as necessary. Thus you can build well-known linear and logistic regression. You can use Neural Network for extracting features previously fed to other algorithms for classification and clustering. At the same time, Deep learning is a part of a neural net that includes numerous hidden layers and encapsulates a wide variety of architectures.
Transfer Learning
This machine learning method re-uses particular parts of previously trained neural net and then adapts them to different but related tasks. For example, after you trained a neural network using data for a task, you can use the same fraction of the trained layers combined with a few new layers that you can train for the new task. In this way, transfer learning significantly improve the sample efficiency of a reinforcement learning agent. You can apply transfer learning on your own predictive modeling problems using the following approaches:
Develop Model Approach
Pre-trained Model Approach
Reinforcement Learning
Reinforcement learning is a machine learning technique that enables an agent to learn from experience by trial and error, applying feedbacks from its own experience. We can compare this method with supervised machine learning since they both use the mapping between input and output. Still, unlike supervised learning, reinforcement learning uses rewards and punishments as signals of positive and negative behavior. You can apply reinforcement learning in case you have no historical data about an issue since RL does not need information in advance.
Natural Language Processing
While Natural Language Processing is not a machine learning technique, but rather a machine learning algorithm that applies artificial intelligence. This method allows understanding, analyzing, manipulating, and potentially generating human language using such texts as social media comments, online reviews, survey responses, even financial, medical, legal, and regulatory documents. In order to map the text in numerical representation, the NLP algorithm computes the frequency of each word within each text document.
Word Embeddings
Word Embedding is a way to improve the ability of networks to learn from text data. Word Embedding technique uses the setting of language modeling and feature learning techniques applied in Natural Language Processing, where words or phrases from the vocabulary are mapped to vectors of real numbers. This method is based on the idea of using a densely distributed representation for each word. Thus, word representations allow finding similarities between words by computing the cosine similarity between the vector representation of two words.
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