Machine Learning Algorithms – My aim is to address some fundamental questions, acknowledging that these queries have been addressed extensively before and are readily accessible on the internet. Nonetheless, by sharing insights derived from personal experience and practical application, we endeavor to offer a unique perspective that goes beyond the conventional explanations found in academic literature or scholarly texts authored by Ph.D. scholars. The primary objective of this post is to provide comprehensive descriptions and visual representations of various machine learning algorithms.
Points Covered in this Post:
This post is limited to the below-index items. The coverage of the points is from a layman’s perspective at a high level in simple English. Anyone looking for detailed explanations or codes should get in touch with me. This post is divided into 3 parts, as below:
- Machine Learning at a Glance
- Types of Machine Learning
- Algorithms in Machine Learning – MLAlgos
From a policy standpoint, we never share any code unless it’s open-source code or a link to the published work. Some of you might wonder why we need to know all of them or what the exact meaning of each term is. We have gathered all the information in one place, i.e., in this blog post.
MLAlgos (Machine Learning Algorithms): Describing and picturing machine learning algorithms is the main idea of this post. We will attempt to answer a few basic questions as well. Though these questions have been answered many times in the past and are widely available on the open internet, Answering them again here from my very own experience on the ground may make the difference compared to simply answering them from a PhD scholar’s material perspective.
Machine Learning at a Glance
Machine learning is a subset of Artificial Intelligence that borrows principles from computer science. It is not an AI, though; It is a focal point where business, data, and experience meet emerging technology and decide to work together. Machine learning is a way to achieve Machine learning models can find patterns in data to help prevent system breakdowns, persuade customers to buy more (e-commerce), or capitalize on a myriad of other business events.
AI and ML help us speed up and understand how Artificial Intelligence will impact global business. The role of machine learning and deep learning in healthcare, transportation, customer service, manufacturing, and financial services
The landscape of Artificial Intelligence as it stands today gravitates more towards machine learning and deep learning. The machine learning of today is helping the organization devise a strategy to move forward with a focus on the company’s most pressing points, i.e., its business and revenue growth. To date, supervised learning is the king or has a kind of monopoly in the machine learning domain, but with advancements in machine learning towards deep learning, the days are not far away when unsupervised learning will become far more important or the only success factor in the future.
Yet most of the expected value is never realized because organizations are unable to align all the data, teams, and processes required to get from proof of concept to production.
Machine Learning’s Business Impact
Machine Learning heavily relies on two main types of algorithms: learning style algorithms and symmetry and similarity algorithms. Presently, Machine Learning has gained immense popularity, making it one of the most sought-after subjects, and being a data scientist is widely considered the most desirable job of this era. However, despite its widespread recognition, the practical implementation of Machine Learning in real-life business scenarios has not always lived up to the hype.
The pressing need in today’s landscape is to showcase, elucidate, and extract tangible and substantial value from businesses that can be enjoyed by all stakeholders. Understanding why Machine Learning has become so influential today involves considering several factors, some of which are outlined below:
- The explosion of big data
- Hunger for new business and revenue streams in this business shrinking times
- Advancements in machine learning algorithms
- Development of extremely powerful machines with high capacity & faster computing ability
- Fast storage capacity available at extreme low cost
There are success stories where organizations have made remarkable progress and added value to their businesses with each type of learning. Making the right choice about which management techniques to use for a particular business problem requires experience and a thorough understanding.
Machine Learning – Strategic and Competitive Advantages
Each type of machine learning provides a strategic and competitive advantage, but the availability of quality data based on which technique is chosen is far more important. The types of machine learning algorithms and which one should be used when are extremely important to know. The goal of any machine learning task and all the things that are being done in the field that put you in a better position is to break down a real problem into design form for machine learning systems.
Until recent times, ML remained largely confined to academia, even though the foundation was laid in 1950. Only recently did it get the spotlight and attention of the industry. Machine learning use cases like face recognition, image captioning, voice and text processing, and self-driving cars are now something everyone talks about.
Machine learning, a subset of Artificial Intelligence, draws on computer science principles. While it is not AI in its entirety, machine learning serves as the pivotal juncture where business and practical experience converge with emerging technology, forming collaborative partnerships. Each type of machine learning offers strategic and competitive advantages, yet the crux lies in the availability of high-quality data, which determines the technique to be employed.
Understanding the various types of machine learning algorithms and knowing when to employ them is of utmost significance. The ultimate objective of any machine learning endeavor is to leverage data-driven insights to make informed decisions, optimize processes, and achieve desired outcomes.
Types of Machine Learning
Before we get into MLAlgos, let’s understand some basics here. The approach to developing ML includes learning from data inputs based on “What has happened”. Evaluating and optimizing different model results remains the focus here. As of today, Machine Learning is widely used in data analytics as a method to develop algorithms for making predictions on data. It is related to probability, statistics, and linear algebra.
Machine Learning is classified into four categories at a high level, depending on the nature of the learning and the learning system. Semi-supervised learning is actually the most interesting of them all.
- Supervised learning: Supervised learning gets labeled inputs and their desired outputs. The goal is to learn a general rule to map inputs to outputs.
- Unsupervised learning: The machine gets inputs without desired outputs; the goal is to find structure in the inputs.
- Reinforcement learning: This algorithm interacts with a dynamic environment and must perform a certain task without a guide or teacher.
- Semi-supervised Learning: This type of ML, i.e., semi-supervised algorithms, are the best candidates for model building in the absence of labels for some data. So if data is a mix of label and un-label, then this can be the answer. Typically, a small amount of labeled data and a large amount of unlabeled data are used here.
ML also has a very close relationship to statistics, which can be called a graphical branch (from a data representation point of view) of mathematics. It instructs an algorithm to learn for itself by analyzing data. The more data it processes, the smarter the algorithm gets.
Some of the popular Machine Learning Algorithms (MLAlgos)
- Linear Regression: In Simple Linear regression, there is only one independent variable. Multiple Linear regression refers to defining a relationship between independent and dependent variables.
- Logistic Regression: A super simple form of regression analysis in which the outcome variable is binary or dichotomous. Helps to estimate adjusted prevalence rates, adjusted for potential confounders (sociodemographic or clinical characteristics).
- Linear Discriminant Analysis: A generalization of Fisher’s linear discriminant, a method used in statistics, pattern recognition, and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events.
- Classification and Regression Trees: Decision trees are an important type of algorithm for predictive modeling in machine learning. A greedy algorithm based on the divide and conquer rule. Split the records based on an attribute test that optimizes a certain criterion. The real value is in determining how to split the records.
- Naive Bayes: Naive Bayes classifiers are a family of simple probabilistic classifiers based on Bayes’ theorem with strong (naive) independence assumptions between the features.
- K-Nearest Neighbors: The laziest algorithm, which is also a very simple algorithm that stores all available cases and predicts the numerical target based on a similarity measure, Since the beginning of the 1970s, as a non-parametric technique, KNN has been used in statistical estimation and pattern recognition.
- Learning Vector Quantization: It has an aim, i.e., the representation of large amounts of data by a few prototype vectors by identification and grouping in clusters of similar data.
- Support Vector Machines: A Support Vector Machine (SVM) is a supervised machine learning algorithm. This can be employed for both classification and regression purposes. SVMs are more commonly used in classification problems, and as such,
- Bagging and Random Forest: Bagging, in general, is an acronym that is a portmanteau of Bootstrap and aggregation. In general, by taking a bunch of bootstrapped samples from the original dataset, fit models will mainly be all BB model predictions; this is bootstrap aggregation, i.e., Bagging.
- The fundamental difference between bagging and the random forest is that in Random forests, only a subset of features are selected at random out of the total, and the best-split feature from the subset is used to split each node in a tree, unlike in bagging where all features are considered for splitting a node.” Does that mean that bagging is the same as random forest if only one explanatory variable (predictor) is used as input?
- Boosting and AdaBoost: Boosting is a machine learning ensemble meta-algorithm for primarily reducing bias and variance in supervised learning and a family of machine learning algorithms that convert weak learners to strong ones. Algorithms that achieve hypothesis boosting quickly became simply known as “boosting”.
- Q–learning – It’s a model-free reinforcement learning technique. It is able to compare the expected utility of the available actions (for a given state) without requiring a model of the environment.
It depends on how the math of the algorithm was translated into instructions for the computer you are using. And it depends on how much time you have. Only recently has machine learning gotten the spotlight and attention from the industry. Machine learning use cases like face recognition, image captioning, voice and text processing, and self-driving cars are now something everyone talks about.
Conclusion – It is crucial to acknowledge that Artificial Intelligence (AI) extends well beyond Machine Learning (ML). Personally, I believe that gaining insights into the various types of ML algorithms contributes to a more comprehensive understanding of AI as a whole. When faced with the question of selecting a specific machine learning algorithm, the response invariably becomes “It depends.” The choice is contingent upon several factors, such as the scale, quality, and characteristics of the data, as well as the intended application or purpose of the outcome.
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