Top 20 Algorithms Machine Learning Engineer should know

Kshitiz Sharan
10 min readSep 9, 2022

Introduction

Machine learning is one of the most exciting and popular fields in computer science today. It’s not just about technology, it’s also about applying advanced algorithms to solve real world problems. This article will be an introduction to artificial intelligence (AI) and machine learning using some of the most common algorithms that are used in this field.

Logistic Regression

Logistic regression is a supervised machine-learning algorithm that’s used for classification and regression problems. It can be used to predict the probability of an event, such as whether or not a patient will die within a given time period.

Logistic regression uses a logistic function to model the relationship between independent variables and dependent variable(s). This means that you use one set of parameters to determine how much influence each independent variable has on your dependent variable(s). Then, based on these values and other input data (such as what kind of illness this patient has), you can predict how likely it would be for him/her not only survive but also recover fully after being treated with medicine X.

The logistic regression algorithm is a type of discriminant analysis that’s used for classification. It can be used to predict the probability of an event, such as whether or not a patient will die within a given time period. Logistic regression uses a logit function to model the relationship between independent variables and dependent variable(s). This means that you use one set of parameters to determine how much influence each independent variable has on your dependent variable(s). Then, based on these values and other input data (such as what kind of illness this patient has), you can predict how likely it would be for him/her not only survive but also recover fully after being treated with medicine X.

Decision Trees and Random Forest

Decision trees and random forest algorithms are the next step up from decision trees. Both of these algorithms use a series of decisions to predict the outcome of an event, such as predicting whether a user will purchase something based on their interest in it.

Decision trees are best suited for large datasets that can be split into small subsets (also known as clusters). Random forest is especially good at making predictions with high accuracy when there are many variables involved in predicting outcomes based on previous experiences.

The following code example shows how you can use decision tree and random forest algorithms together:

Gradient Boosting Machines

Gradient Boosting Machines (GBM) is one of the most popular machine learning algorithms.

It is a supervised machine learning algorithm, meaning that you have to train it on some labeled data before using it for prediction or classification problems.

The idea behind GBM is to use gradient boosting as a way to improve performance of your model when there are many variables involved in your problem.

GBM is a great option for problems that have many variables and where it is difficult to find any useful structure in the data. It is also very good at dealing with highly correlated variables, which can be a problem for simpler models.

K-Means

K-Means is an unsupervised learning algorithm used for clustering data. It is an iterative algorithm, which means it begins with an initialized set of clusters and then iteratively expands these clusters by moving to new centers until no more moves are possible.

K-Means can be used in different situations, but one of its primary uses is dimensionality reduction or dimensionality reduction algorithms (DR). In this context, a DR algorithm divides your dataset into groups based on some similarity measure between each group’s members and your dataset as a whole. Then you use K-means to determine which groups best fit your original dataset; this will make it easier for you to find patterns within those groups

Singular Value Decomposition (SVD)

Singular Value Decomposition (SVD) is an eigenvalue decomposition of a matrix. It can be used to compute the principal components of a matrix and also compute the singular value decomposition (SVD) of a matrix.

Understanding PCA is an essential part of working with data. It’s used in countless applications, from finance and statistics to machine learning. In this article, we’ll discuss what PCA is, how it works, and how it can be used for dimensionality reduction.

Principal Component Analysis (PCA)

Principal Component Analysis (PCA) is a dimensionality reduction technique that finds the direction of maximum variance in data. It’s used to reduce the number of features in a dataset and find underlying structure in data by finding projection directions.

In this article, we’ll discuss how PCA works and why you should learn it as an algorithm engineer!

A fully connected layer, on the other hand, is made up of many neurons that take the output from a convolutional layer and combine them with weights. These weights are used to determine what features are most important for classification tasks.

Convolutional Neural Network (CNN)

Convolutional neural networks are a type of artificial neural network that can be used for image recognition, speech recognition and more. They consist of convolutional layers and fully connected layers.

A convolutional layer is made up of many small filters that process the data in an input layer before it’s passed on to another layer. The size of each filter depends on how much you want to learn from your machine learning model (e.g., whether you’re looking at text or images).

The output from each filter is then combined together with other outputs from previous layers through weighted summing operations called “pooling.” This helps reduce noise during training so that we can see better results later on when we’re trying to classify images into categories like “cat,” “dog,” etcetera!

Recurrent Neural Network (RNN)

Recurrent neural networks (RNNs) are a type of neural network that can remember previous events. RNNs are used in natural language processing, speech recognition, and machine translation. They’re also a type of deep learning model: LSTM networks are a type of RNN that can also remember previous events!

Applications of LSTM: natural language processing, speech recognition, and machine translation

This is a more advanced algorithm that can be used to compute the singular value decomposition (SVD) of a matrix. The algorithm proceeds by performing several iterations of QR decomposition, followed by an eigenvalue decomposition of the resulting matrix. LSTMs are one of the most popular types of RNNs. They’re used in many applications, from Google Translate to Siri and Alexa. LSTM networks are used for tasks like machine translation because they can learn to remember what came before (and predict what comes next).c

Long Short Term Memory Networks (LSTM)

LSTMs are a type of recurrent neural network (RNN) that can learn from the past and predict the future. They’re useful for sequence learning tasks like speech recognition, machine translation, and text classification.

In this article we will discuss how to use Long Short Term Memory Networks (LSTM) in Machine Learning with Python on Python 3.6+.

In this tutorial, we will show you how to train and evaluate a Long Short Term Memory Network (LSTM) in Python using Keras. The LSTM network is a type of recurrent neural network (RNN) that can learn from the past and predict the future. They’re useful for sequence learning tasks like speech recognition, machine translation, and text classification. In this article we will discuss how to use Long Short Term Memory Networks (LSTM) in Machine Learning with Python on Python 3.6+.

Markov Chain Monte Carlo Methods

Markov Chain Monte Carlo Methods are a class of algorithms used to approximate the probability of events in a stochastic system. The name comes from the fact that these methods work by simulating the behavior of complex systems that are difficult to model analytically.

Markov Chain Monte Carlo (MCMC) is an approach for computing posterior distributions based on sample data and prior knowledge about these parameters. It has been used extensively in Bayesian statistics, machine learning and computational finance (e.g., interest rate modeling).

In this post, we will build a simple HMM in PyTorch and then train it using Hard EM. In particular, MCMC has been used to estimate the posterior distributions of interest rate models. Prior information about the parameters is typically stored in a table, known as a “parameterization”. The algorithm then samples from this table using random numbers to create chains of states that are representative of the real-world system being studied (e.g., interest rates). Each state can either be observed or unobserved (i.e., latent).m

Hidden Markov Models

Hidden Markov Models (HMMs) are a class of Markov Model with the additional assumption that the underlying Markov chain is hidden.

There are two variants of EM: Soft-EM and Hard-EM. The difference between them is based on how you estimate your parameters, with Hard EM being more effective at producing good results in terms of predictive accuracy when compared with Soft EM or Gaussian Mixture Model which can be easily trained using gradient descent algorithms such as SGD or Adam optimizer.

Using a mixture model is great, but if your data has multiple components that can be separated and treated independently, then it’s better to use an HMM. An HMM is a generative model of a sequence of observations where the probability distribution over all possible observations given a sequence of hidden states.

Expectation Maximization Algorithm (EM)

Expectation Maximization (EM) is a probabilistic approach to finding a maximum likelihood estimation for a set of parameters. EM is used for both classification and regression problems, where it can be seen as the generalization of least squares regression. In other words, the Expectation Maximized (EM) model uses an iterative approach to find the most likely values for each parameter in order to fit data points accurately.

It’s important to note that EM isn’t just limited to linear models; it has also been applied successfully in nonlinear problems such as neural networks and non-parametric methods such as kernel functions and decision trees.

Stochastic Gradient Descent (SGD) & Alternating Least Squares (ALS) Algorithms

Stochastic gradient descent (SGD) and alternating least squares (ALS) are simple, but effective algorithms for minimizing the loss function, maximizing the likelihood function and finding a local minimum in an iterative process.

These two algorithms are closely related because they both use gradient descent to solve these problems. In SGD we minimize the loss function while in ALS we maximize the likelihood function. In both cases we use gradient descent to do this; however, there is also some technical differences between these two approaches.

Naive Bayes Classifier Algorithm

Naive Bayes Classifier is a simple and effective algorithm for classification. It is based on the Bayes’ theorem, which is a theorem in probability theory. The algorithm relies on inductive reasoning rather than deductive reasoning to make predictions.

Naive Bayes Classifier Algorithm uses prior information about classes (e.g., whether an item belongs to group A or group B) before learning from new instances like text data or images.

The algorithm is used for classification, which is a task to predict the label or class of an instance. There are two types of classification problems: 1) binary classification and 2) multiclassification. In binary classification problem, we need to predict one label from two classes (e.g., predicting whether someone will buy your product or not).

In multiclassification problem, we need to predict multiple labels from a number of classes. For example, predicting whether someone is a customer or not (binary) or predicting whether someone will buy your product or not, but also whether they are likely to recommend it to their friends (multiclassification).

The Naive Bayes classifier algorithm is one of the most popular and simple machine learning algorithms. It is used in various applications such as spam filtering, document classification, and text mining etc.

Q-Learning Reinforcement Learning Algorithm

Q-learning is a reinforcement learning algorithm. It’s an off-policy learning method, which means that instead of starting with a model and then trying to optimize it, you start with an initial policy (the rules that describe what your model should do) and then use that as your starting point to improve over time.

Q-learning is also known as temporal difference learning because it uses temporal differences between observations in order to update its estimates of action values. This can be thought of as using experience points in games like World of Warcraft or Pokemon Go where players get stronger after spending time training their characters by doing things like fighting monsters or completing quests. For example if you were playing Final Fantasy VI on SNES you’d gain experience points from defeating enemies which would allow them become stronger if they were leveled up enough times before finishing off the final boss who was guarding some treasure chest somewhere deep within his lair underneath Castle Exdeath’s throne room where he usually keeps himself when not actively defending himself against invaders attempting to take over his kingdom!

k-Nearest Neighbors Algorithm (KNN) & Collaborative Filtering Algorithm

K-Nearest Neighbors (KNN) is an algorithm used for classification and regression. It uses the k-nearest neighbors to classify new data points, which then forms the basis of supervised learning. KNN is also known as a classification method based on frequent pattern matching or linear discriminative analysis.

The premise behind this algorithm is that there can be many different types of objects in our world and we need to find out which one fits best with our training set before making decisions about its class membership. This involves finding out how similar two objects are based on their relative distances from each other among all their individual features such as color, shape etc., then calculating their similarities using some mathematical formula like Euclidean distance between them. Once this information has been collected from various samples/samples within each class (which will be used later), An LDA model can be created using these values along with some other parameters such as threshold value for deciding whether an instance should be assigned into its corresponding class label or not

Takeaway:

Machine learning is a subfield of artificial intelligence that develops algorithms that enable computers to learn from data. It can be used to build predictive models, which can then be used to make predictions about future events.

Machine learning algorithms have been used in many fields, including finance (e.g., stock market), healthcare and law enforcement (e.g., facial recognition), robotics and autonomous vehicles (e.g., self-driving cars).

Conclusion

Machine learning is no longer just a tool for data analysts and developers. It’s also an essential skill for anyone looking to build an AI-based product or service. The best way to get started with machine learning is by understanding the basics of how algorithms work, so that when you run into a new algorithm on your path, you know what it does and how it works.

With this list of 20 algorithms that every ML engineer should know, we hope you can start building your own machine learning career!

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