A Beginner’s Guide to Machine Learning Algorithms

Who would have ever thought a machine would make human decisions in the third decade of the 21st century? Yes, that’s a reality now. And if you are here to learn about machine learning algorithms, welcome to this comprehensive post.

Machine learning is one of the fast-emerging fields of AI where machines take decisions like human beings. With ML approaches, customizing and analyzing user content & data have become a reality. In addition, it also decreases the requirement & app maintenance cost too.

But when a machine starts making human decisions, different machine learning algorithms run in the background. Now, what are ML algorithms, and how do they work?

The Purpose of Machine Learning Algorithms: Understanding the Types

ML is a branch of computer science allowing computers to infer patterns automatically from data. These inferences are based on algorithms that automatically examine the statistical properties of data. They create mathematical models that represent the connection between various quantities. Here’s presenting the types of machine learning algorithms:

Supervised Learning

Supervised Learning algorithms are the classes where an ML model comprises a set of explicit data labels for quantity. Semi-supervised learning utilizes an amalgamation of unlabeled and labeled data that train AI models. This type of algorithm is subdivided into classification and regression.

Unsupervised Learning

Data in unsupervised learning problems do not have any labels. Here, patterns are being looked for. Despite the absence of explicit and definitive data regarding someone’s interests, identifying a group of customers buying similar items will allow for making purchase recommendations. This depends on the type of people in the cluster who have purchased previously.

Reinforcement Learning

This is a class of machine learning algorithms where you assign the computer agent to perform tasks without much guidance. The computer makes the choices, and based on whetherthey lead to the best outcomes, one can assign rewards or penalties.

Deep Learning

Deep learning is the subsection of machine learning which breaks an issue problem down into different ‘layers’ of the neurons. Here, the artificial neural network, or ANN, contains several layers. That is why it is referred to as deep learning.

Semi-Supervised Learning

Semi-supervised learning algorithms use small amounts of labeled data along with large amounts of unlabeled data in their training phase. They sit between unsupervised learning, which uses only unlabeled data, and supervised learning, which uses only labeled data. 

They use the strong points of both these types and make models more accurate and perform better, especially in cases where labeling data can take up a lot of time or cost a lot. 

Common ML Algorithms

Common ML algorithms can be classified into several groups like the following:

• Regression algorithms predict common values.
• Classification algorithms sort data into predefined classes.
• Ensemble algorithms combine multiple models for greater accuracy.
• Clustering algorithms group similar unlabeled data points.
• Dimensionality reduction algorithms simplify complex datasets by reducing the features.
• Deep learning uses layered neural networks, learns patterns automatically, and drives image recognition, advanced predictions, and NLP.  

Functionalities of Some ML Algorithms

Narrated below are the top functionalities of ML algorithms:

Linear Regression

Linear Regression algorithms analyze data as well as predict outcomes with specific input variables that form a ‘visual slope’ for predictions. They comprise supervised algorithms and simplistic versions depending on equations:

• y = ax + b
• f(x,y,z) = w1x + w2y + w3z

Logistic Regression

It’s a supervised learning algorithm using predictive analysis to categorize issues and discover the right solutions. Businesses use it to predict the probability of any event. What it does is fit data to the logit function, thus, at times, referred to as logit model or regression.

Naive Bayes

This one is the fast-working supervised learning algorithm, and it assumes the occurrence of any feature to be independent of occurrences of other features. It also anticipates that the output value of any function might be calculated via the Bayes theorem.

K-NN or K-Nearest Neighbor

This algorithm evaluates similarities between a new case (data) and earlier cases. After analyzing the similarities, it puts new cases into a category (most similar to the available ones). Being an easy-to-use algorithm, it can resolve classification issues.

K-Means Clustering

The next comes a simple unsupervised ML algorithm that can cluster data depending on similarities (i.e. data points). It tries to analyze data patterns.

Now that you have learned about the basics of machine learning algorithms, it is time to choose one of the best machine learning courses online.

Why Machine Learning (ML) Algorithms Matter Today

The following are the main reasons why ML algorithms are so important these days:

• Automation of Complex Tasks
• Deriving Data-Driven Insights
• Powering Everyday Technology
• Predictive Power
• Personalization
• Solving Problems Which Could Not Be Solved Earlier
• Scalability and Continuous Improvement

In terms of powering regular technology, ML algorithms are extremely useful for the following:

• Recommendation Engines
• Natural Language Processing (NLP)
• Speech and Image Recognition

These benefits work in various ways. For example, ML algorithms can analyze massive datasets, make decisions, and identify patterns without human intervention. This helps automate error-prone and time-consuming tasks across industries.

How to Choose the Right ML Algorithm

You must focus on these factors to select the right ML algorithm:

• Defining the Desired Output and the Problem
• Understanding the Characteristics of Your Data
• Considering Practical Requirements and Constraints
• Iterating and Experimenting

All these steps can be broken down further into smaller steps. For example, in the case of defining the problem and the desired output, you can follow these steps:

• Predicting a Continuous Value (Regression)
• Categorizing Data into Classes (Classification)
• Finding Groupings or Patterns in Unlabeled Data (Clustering)
• Reducing the Number of Features (Dimensionality Reduction)
• Learning through Trial and Error (Reinforcement Learning) 

ML Workflow Every Beginner Should Know

As a beginner, you must be aware of the following ML workflow:

• Problem Definition
• Data Preparation and Collection
• Feature Selection and Engineering
• Model Training and Selection
• Model Tuning and Evaluation
• Monitoring and Deployment

Thus, this is evidently an iterative and systematic process that guides projects toward real-world application from conception.

Challenges and Limitations of ML Algorithms

The following are the biggest limitations and challenges of ML algorithms:

1. Technical Limitations and Challenges

• Data Dependency
• Underfitting and Overfitting
• Computational Expenses
• Model Drift
• Correlation vs. Causality
• Adversarial Attacks

2. Ethical and Operational Challenges

• Algorithmic Fairness and Bias
• Lack of Transparency or the Black Box Problem
• Data Security and Privacy
• Governance and Accountability
• Lack of Intuition and Common Sense
• Integration and Deployment Complexities

FAQs on Machine Learning Algorithms