How Machine Learning Algorithms Works : An Overview

Algorithms – The acquisition of knowledge by computers from previous experiences is made possible through the essential technique of artificial intelligence known as machine learning.

Machine learning involves the use of computational methods that enable algorithms to derive significant insights from data without being limited by pre-existing equations. The ML algorithms analyze data using statistical methods to uncover meaningful patterns and relationships. These insights can then be used to make predictions or decisions automatically, without requiring any specific programming instructions.

The Size of Machine Learning Ocean

Machine learning offers a number of algorithm options. Tools like TensorFlow, an open-source machine learning framework, are bringing machine learning to everyone, though. Your own experience, need, data quality, type of data, and the most important part, your own natural intelligence (NI), should be used for evaluation and determining suitability.

With the increase in the amount of information that can be obtained, the algorithms exhibit more efficient and prompt performance. Deep learning is a specialized subset of machine learning that is distinguished by its high level of specificity. Some of top machine learning algorithms are

• Support Vector Machines
• Linear Regression
• Decision Tree
• Logistic Regression
• Random Forest
• K-nearest Neighbors (KNN)
• K-Means
• Principal Component Analysis (PCA)
• Apriori

There may be various ways to reach the destination. Example of few outstanding, powerful algorithms are listed as above

Algorithms in Machine Learning

It provides a systematic approach to inspire computer systems with logic, scientific principles, and rational reasoning. Determining the optimal algorithm to employ in a given scenario entails a combination of expertise, cognizance, and exigency. It can be referred to as an alternative algorithm.

Algorithms in Machine Learning (ML) borrow principles from computer science. There are many algorithms, as no one algorithm works best for every problem,

• How does YouTube suggest videos?
• Which face has what name? How does Facebook know?
• How is Google Maps able to find the best and fastest route between the office and home?

All the above questions have one common answer: “Algorithms”. The different problems will have different algorithms (solutions). Google Maps uses its own routing algorithm, which is again a set of many small sub-algorithms.

Machine learning is a subset of artificial intelligence and a field of study that harnesses the principles of computer science and statistics. AILabPage members intuitively call statistics a graphical branch of mathematics. AILabPage defines machine learning in one simple line, as below.

A focal point where business, data, and experience meet emerging technology and decide to work together

For additional information regarding the chronological progression and historical background of machine learning, please read the article entitled “The Exciting Evolution of Machine Learning.“

Machine Learning – Background

Even though the term “machine learning” was coined in the 1950s, it remained largely confined to academia. Only recently has machine learning come to the limelight, but disappointingly, it is becoming a rocket science-type complex subject and more accessible to developers as their tool.

Different types of machine learning problems need different algorithms to solve them. Problems like the one below need easy answers with such tools.

• Implementation of different types of classification algorithms
• Organizing a predictive model project
• What are all the different elements of data that need to be used in predictive modeling?
• How deep convolutional neural networks leverage to convey images with high accuracy.
• How to scan audio in video messages by making use of natural language processing techniques and open source tool APIs

Open-source machine learning tools have the capability to solve a wider range of problems. These tools do have the strength and capabilities to build deep neural networks and run them across thousands of computers in data centers.

Machine learning has evolved from artificial intelligence’s subset to its own domain. It has reached an inflexion point, at least in terms of messaging. I remember in my school days, as part of statistics class, we were told something about AI and ML, and we laughed then, in the 1990s.

The need of today is a simple and easily accessible machine learning cloud service, i.e., “Machine Learning as a Service” (MLaaS), for everyone at a low cost.

Machine Learning Process Flow

ML instructs an algorithm to learn for itself by analyzing data. Algorithms here learn the mapping of input to output and the detection of patterns or rewards. The more data it processes, the smarter the algorithm gets.

In other words, machine learning algorithms “learn” from the observations. When exposed to more observations, the algorithm improves its predictive performance.

In the above diagram, of course, there are a lot of iterations between steps 5 to 4 and 3 and 5 to 3 and 2, which are not shown in the above diagram to keep it simple. Machine learning is classified into three categories at a high level, depending on the nature of the learning and the learning system.

1. Supervised learning: The machine gets labelled inputs and their desired outputs. The goal is to learn a general rule to map inputs to the output.
2. Unsupervised learning: Machine gets inputs without desired outputs, the goal is to find structure in inputs.
3. Reinforcement learning: In this algorithm interacts with a dynamic environment, and it must perform a certain goal without a guide or teacher.

How Machine Learning Algorithms Work?

In one line, the answer is “In machine learning, algorithms work by learning strategies to map input to output”.

Machine learning can be defined as a realm rooted in mathematical principles or characterized by the manipulation and analysis of numerical data. Algorithms function by comprehending the underlying principles of the game that pervade all algorithms. Algorithms do not possess any discernible taste or flavour, as they function solely as distinct methodologies for accomplishing identical tasks. The initial step typically involves acquiring knowledge of a designated function.

Target function (f) that best maps input variables to an output variable. For example, for our input variable “a” we need to predict the value of “y.”. In this case, our function becomes y=f(a).

Can you predict the value of the house (“Y”) in a location (“LOCATION”) with input variables like the dimensions of the land in square feet, the construction cost per square foot, the annual rate of growth in a price change, etc.?

After the function has been learned, it will be easy to predict the value of “y” for every new value of “a.”. It does not get over here; there is always a need to keep the error margin, or so-called algorithm performance indicator (b). As it is independent of the input variable “a.”. So our function looks like y = f(a) + b.

Why Learning a Function is Important

Learning a function has one sole purpose, and that is to make the best possible predictions. Predictive modeling is mapping y = f(a), i.e., predicting Y for every new value of “a.”.

Functions are unknown for the model, and as mentioned above, their taste, flavor, shape, or color are really of no use or importance as long as they give possible accurate predictions. Functions are unknown before algorithms learn them, hence their importance. Otherwise, there would be no use in wasting time and learning anything.

In supervised learning, algorithm jobs are to take some data with a known relationship. From the above example for predicting house value, where the price of land, etc. is known, creating a model of those relationships is easy.

This is a regression problem as the output will be numerical, but if we were to find expensive or not expensive, then this would have fallen into the category of the classification problem.

Learning a Function – Approach

When to use which algorithm is a complex question to answer. It entirely depends on the problem and data at hand. Applying at least 3–5 algorithms to find the best results or answer is better.

It is so important to try 3-5 or sometimes even more different algorithms (this is not a law or any rule) on a given problem. Otherwise, knowing the best outcome beforehand is impossible.

Remember, it’s all about estimations, possibilities, prediction, and forecasting, so it is always best to make use of machine learning algorithm suites for approximations.

Different depictions of the methods used make different assumptions about the function that is about to be learned or is being learned. So whether it is linear or nonlinear, we need to be careful.

Algorithm Performance Measure

Different machine learning types have different reasons and methods for their algorithms. How well your algorithm has performed can be measured with error margins. For example.

• In case of supervised learning problems you can measure
  • Classification Error for classification problems – how many errors are made
  • Prediction error for regression problems – How far from real value
• In the case of unsupervised learning problems you can measure
  • Clustering – How loose or tight
  • Association – Confidence level of associations

In order to enhance the efficacy of algorithms, it is imperative to curtail the utilization of resources. However, it is imperative to note that diverse resources, including time, computing power, accuracy, and space complexity, cannot be directly juxtaposed resources, including time, computing power, accuracy, and space complexity, cannot be directly juxtaposed.

The determination of algorithm superiority is contingent upon variables including but not limited to dataset properties, objectives, and the prioritization of specific efficiency metrics.

Books & Other Material Referred

• Open Internet & AILabPage (a group of self-taught engineers) members hands-on lab work.

Points to Note:

When to use which algorithm is a complex question to answer. It entirely depends on the problem at hand to be solved. It’s better to apply at least three to find the best results and the best answer. All credits, if any, remain with the original contributor only. In the next post, I will talk about recurrent neural networks in detail.

Feedback & Further Question

Do you have any questions about deep learning or machine learning? Leave a comment or ask your question via email. I will try my best to answer it.

Conclusion –  We have seen in the post above that machine learning algorithms are all about learning the target function. It works to estimate the mapping function (f) of output variables (y) given input variables (a), or Y=f(a). Google, Match.com, and Facebook matching algorithms are examined to see more clearly how they work mathematically.

We have also learned in this post that different machine-learning algorithms make different assumptions. When to use which algorithm is a complex question to answer. So it is always best to make use of machine learning algorithm suites for approximations.

============================ About the Author =======================

Read about Author at : About Me

Thank you all, for spending your time reading this post. Please share your opinion / comments / critics / agreements or disagreement. Remark for more details about posts, subjects and relevance please read the disclaimer.

FacebookPage    ContactMe      Twitter

====================================================================