Top 10 Machine Learning Algorithms That Are Commonly Used

1. Linear Regression

One of the most foundational ML algorithms, it is used for predicting a continuous outcome based on one or more features. If you’re learning machine learning from scratch, then linear regression is often the first algorithm that is introduced because of its intuitive formulation: just fit a straight line through data to minimize error. Its strong points are interpretability, simplicity, and efficiency. Real-world applications include house price prediction, sales forecasting, and risk modeling.

2. Logistic Regression

Though named “regression,” logistic regression is actually used for classification. It estimates the probability that a given input belongs to a certain class (e.g., spam or not spam). This is one of the most used machine learning algorithms in business because it’s fast, easy to implement, and gives probabilistic outputs. When you start learning machine learning from scratch, logistic regression helps bridge the concept between regression and classification — showing how a linear function can be transformed into a probability using the logistic function, also known as the sigmoid.

3. Decision Trees

A decision tree is a flowchart-like model where internal nodes represent “decisions” on feature values and leaf nodes represent outcomes. Decision trees are powerful yet interpretable, hence very popular. As one of the top ML algorithms, they are used for classification and regression. They’re great when you want to visualize decision logic, handle categorical data, or explain model behavior to stakeholders.

4. Random Forests

Random Forest is an ensemble method that constructs a large number of decision trees and then combines their predictions through voting or averaging. As a result, it has become one of the most powerful machine learning algorithms in practice, overcoming overfitting and improving generalization. For a person learning machine learning from scratch, understanding random forests will give you an insight into how combining weak learners yields a robust and high-performing model. Applications include credit scoring, fraud detection, and medical diagnosis.

5. Support Vector Machines (SVM)

Support Vector Machines are powerful supervised learning algorithms that are used primarily for classification but, on occasion, for regression. SVMs find the optimal hyperplane that maximally separates classes. When learning machine learning from scratch, SVM is useful to understand kernel methods, margin optimization, and regularization. It is particularly handy in high-dimensional spaces–say for text classification – or when one needs a model that should handle nonlinearities via the kernel trick.

6. K-Nearest Neighbors (KNN)

KNN is a simple yet effective algorithm: to predict the class-or value-for a point, it looks at the ‘k’ closest training samples in feature space and uses them to decide. Due to the fact that it is one of the easiest ML algorithms to understand, it is ideal when you are learning machine learning from scratch. KNN does not have an explicit training phase, which makes it intuitive and helpful for classification tasks like image recognition, recommendation systems, and anomaly detection.

7. Naive Bayes

Naive Bayes is a family of probabilistic classifiers based on Bayes’ theorem with a naive assumption of independence regarding all features. This is a simplifying assumption that leads to very good practical results, particularly in text classification (like spam detection or sentiment analysis). When learning machine learning from scratch, naive Bayes teaches you about probabilistic modeling and conditional independence, powerful concepts in statistics and ML.

8. K-Means Clustering

K-Means is a type of unsupervised learning algorithm that aims to partition data into K clusters by minimizing the within-cluster sum of squares. Being one of the most well-known machine learning algorithms for clustering, it is extremely helpful when one intends to discover natural groupings in data without labels. For those beginners learning machine learning from scratch, K-Means is usually the first unsupervised algorithm they learn due to its intuitive geometric interpretation.

9. Principal Component Analysis (PCA) / Dimensionality Reduction

Dimensionality reduction algorithms such as PCA support projecting high-dimensional data into a lower-dimensional space while preserving as much variance as possible. The machine learning algorithms are very helpful in cases where many features are at play-for example, image data or text features-and makes your models faster and less prone to overfitting. When learning machine learning from scratch, PCA forms a good way to understand eigenvectors, explained variance, and how feature reduction can simplify your problem with no loss of critical information.

10. Gradient Boosting: e.g., XGBoost, AdaBoost)

Gradient boosting is a powerful ensemble technique where weak learners, usually decision trees, are built sequentially, each trying to correct the errors of its predecessor. It is one of the most effective ML algorithms that works equally well in both Kaggle competitions and real-world applications. If you are learning machine learning from scratch, gradient boosting will also introduce you to boosting concepts, loss optimization, and regularization. Variants like XGBoost (Extreme Gradient Boosting) are highly optimized for performance and scalability, which makes them very popular in practice.

• Start with Linear and Logistic Regression: Understand the basics of prediction and classification.
• Move to Decision Trees and Random Forests: Learn interpretability and ensembling.
• Explore SVM and KNN: Delve into margin-based models and distance-based learning.
• Learn Naive Bayes: Understand probabilistic thinking.
• Try unsupervised methods (K-Means, PCA): Practice clustering and dimensionality reduction on real data such as customer segmentation or image data.
• Finish with Boosting (Gradient Boosting): Apply robust methods for high-performance modeling. 

On your way to completion, do implement the algorithms listed here using tools like scikit-learn, TensorFlow, or PyTorch, implementing theory by practice.

• Overfitting: Models such as decision trees and boosting may overfit if not regularized.
• Computational Cost: Ensemble methods and dimensionality reduction can be computationally expensive. 
• Interpretability vs Performance: Very accurate algorithms, like gradient boosting, may be harder to interpret than simpler ones.
• Hyperparameter tuning: most ML algorithms have to be carefully tuned – for example, the number of trees or the learning rate – which can take a lot of time.
• Data Preprocessing: Feature scaling is required for algorithms such as SVM or KNN, while PCA requires data normalization.

• Finance: Logistic regression for forecasting the probability of loan defaults, random forests for credit scoring, and gradient boosting for risk modeling. 
• Marketing: Customer segmentation using K-Means clustering, predicting churn with decision trees or boosting algorithms. 
• Healthcare: Disease diagnosis using SVM or random forest, PCA for reducing the dimensionality of genetic data.
• Retail: Sales forecasting using linear regression, making personalized recommendations using KNN or clustering.
• Text Analytics: Spam email classification using Naive Bayes, sentiment analysis through logistic regression.

• Do Projects: Implement each algorithm on different types of data sets (regression, classification, clustering) to internalize them.
• Apply Open-source Libraries: Implement scikit-learn, TensorFlow, etc., to avoid reinventing the wheel.
• Visualize for decision trees or PCA, plot graphs to internalize how the algorithm works.
• Hyperparameter Tuning: Apply grid search, random search, or Bayesian optimization in order to improve performance.
• Read Theory: When learning machine learning from scratch, balance code with theory — understand loss functions, regularization, and model bias/variance.
• Stay Current: ML is fast-evolving, so keep reading blogs, research papers, and community resources to update your knowledge.

Mastering the top 10 machine learning algorithms is an important milestone when learning machine learning from scratch. These ml algorithms indeed provide a broad toolkit that helps in handling many types of real-world data problems-from simple linear regression to powerful boosting techniques. Whether you are building predictive models, clustering data, or reducing dimensions, these algorithms form the bedrock of practical machine learning knowledge.

This will help you understand when to use each of the various machine learning algorithms, how to optimize your models, and how to scale your solutions.

So go ahead, play around with each of these algorithms, and gain more confidence because once you master these, you will be well on your way to becoming a fine data scientist or an ML practitioner.