on January 09, 2020

From the word go in the world of digital computing, Algorithms have been the smartest answer to complex questions. At their core, they are powerful programming models trained to process given datasets in order to produce predictive results of highest accuracy.

In this article, we are going to take a small trip through the 10 best **machine learning algorithms** essential for data scientists and their career. It includes types of algorithms, the purpose and methods of model training and their actual industrial applications.

Our goal in this blog is to help you gain a thorough and clear understanding of some widely used algorithms in machine learning domain.

Basically, all machine learning algorithms described below belong to any of three categories:

- Supervised learning
- Unsupervised learning
- Reinforcement learning

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**1. Apriori Machine Learning Algorithm**

Apriori algorithm follows an unsupervised machine learning type and is used for datasets working on association rules. In this principle of IF/THEN format, if an item A occurs, it implies the probability of item B. The principle simply goes like this:

- If a target item occurs frequently, its subsets also occur frequently.
- If the set items occur infrequently, so do all the supersets of the item.

For example, if someone on an ecommerce app buys an Android device, they are most likely to purchase supporting accessories like mobile cases or earphones. To make it work, the algorithm collects statistical data about the number of people opting for such accessories with their phone. This generates cognitive insight: 80% of people who bought a device also settled for a protection case.

**Applications**

**E-Retail purchase analysis**

Ecommerce retailers can use Apriori ML algorithm to pull insights on which products customers prefer to purchase together or which promotions they respond positively to. Like mobile devices, Apriori can also be applied to predict the purchase of food items, sports and fitness apparel, etc.

**Analyzing drug performance**

Apriori association analysis can also be useful in measuring dangerous side-effects of drugs based on patient characteristics, drugs consumed, experience of adverse reactions, diagnosis, etc. The association rule helps identify the combination of patient characteristics and drugs that induce adverse side effects.

**Auto-Complete search function**

The most popular example of Apriori algorithm is Google search engine’s auto-complete feature that automatically suggests the closest associated set of words after a user enters a specific word in search bar.

**2. K Means Clustering Algorithm**

K-means Clustering is another top unsupervised machine learning algorithm focused on non-deterministic and iterative methods for clustering. The algorithm is set to consider pre-defined numbers of clusters (k) to process and respond to given datasets. Based on the input as a group of clusters, the output determined at the end of the algorithm is necessary K clusters.

To exemplify, we can take into account the search engine Yahoo where you enter a specific term like ‘Amazon’ and you will see several pages containing the word Amazon. It can be details on Amazon forests, or latest updates on Amazon store or Amazon services (Kindle, Alexa, Echo). K Means clustering will thus group all the webpages in individual clusters of Amazon as forest, Amazon as online store or services and so on.

**Applications**

- K Means Clustering algorithm is mainly designed for leading search engines like Yahoo, Google, Ask or even Wikipedia to cluster web pages by similar concepts, topics or products, determining degrees of relevance among search results.
- It is most suitable when users need to reduce computational time taken by search engines.

**3. Naïve Bayes Classifier Algorithm**

Naïve Bayes Classifier machine learning algorithm sees similarities of input datasets for subjective analysis and works by the popular Bayes Theorem of Probability. It comes to your rescue if you intend to manually perform the rather cumbersome job of classifying web pages, emails, documents or other online textual note formats. The classifier function can help determine a most appropriate category by allocating its element value. For example, Important or Not Spam/Spam filters work as a classifier that picks labels like ‘Spam’ or ‘Important’ or ‘Primary’ to given emails. Similarly, it also helps design effective models for disease prediction.

**Applications**

- Google Mail is the perfect example of Naïve Bayes algorithm where it analyzes and determines Spam or Not Spam emails.
- Google uses document classification techniques for document categorization. It relies on Naïve Bayes to index documents and figure out PageRank which indicates relevance scores. It works its database to check pages marked as important.
- It is also used for human sentiment Analysis like Facebook where status updates are scrutinized for positive or negative emotions.
- Naïve Bayes Algorithm is also used for classifying news articles about Technology, Entertainment, Sports, Politics, etc.

**4. Support Vector Machine Learning Algorithm**

Support Vector Machine is a supervised type of machine learning algorithm that learns to classify new data into different classes or solves regression problems. It works its way by finding a line that separates training data from classes to be determined. Due to the presence of multiple linear hyperplanes, SVM for better results opts for margin maximization which maintains a fair distance between various classes. To generalize unseen data well, SVP should identify the line that maximizes the distance between the classes. Usually there are two major SVM algorithms:

- Linear SVM where classifier training data is separated by a hyperplane.
- Non-Linear SVM where hyperplane doesn’t help separating the training data.

**Applications of Support Vector Machine**

- SVM is best applied to stock market applications to forecast stock performance by financial setups.
- SVM can also be used to analyze and compare relative performance of certain stocks with other stocks of a similar sector to fuel investment decisions based on SVM algorithm’s classifications.

**5. Linear Regression Machine Learning Algorithm**

Linear Regression algorithm defines the relationship between any two variables, one dependent and one independent. It shows the impact of change in one independent variable on the other dependent one. Since the independent variables explain the factors likely to affect dependent ones, they are also called explanatory variables while dependent variable is referred to as the factor of interest. Linear Regression ML algorithm technique is widely used since it is easy-to-interpret, fast, and requires minimal efforts of tuning.

**Applications**

**Sales estimation**

Linear Regression can be effectively used in estimating business sales from future perspectives based on the existing trends. It is especially useful if a company with steady monthly sales increases wishes to forecast sales in upcoming months.

**Risk Assessment**

When it comes to assessing risk involvement in finance and insurance domain, Linear regression analysis delivers great results. Insurance companies can run this algorithm on each customer of a certain age for the number of claims they own. Thus, the assessment results help derive a complete insight on claims-related risk status on young vs old customers and take sensible business decisions.

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**6. Decision Tree Machine Learning algorithm**

Decision tree ML algorithm, as the name implies, helps make better decisions through a graphical representation that relies on branching methodology. Based on specific conditions, users will interpret all probabilities and combinations of possible outcomes of a decision. Each internal node of a decision tree is a test on the given attribute and each branch of the tree shows the outcome of the test. The final decision made (shown as class label) after considering all the attributes is represented by the leaf node. The path from root to leaf contains the classification rules.

**Types of Decision Trees**

- Classification Trees
- Regression Trees

**Applications of Decision Tree Machine Learning Algorithm**

- This popular machine learning algorithm finds its place in applications for finance for option pricing.
- Decision trees are also useful for Remote sensing and pattern recognition.
- A baby product company Gerber products uses decision tree to decide about the use of PVC in their products
- Banks also use Decision tree systems to classify loan applicants based on their defaulting payments.
- Decision tree machine learning algorithm to decide whether they should continue using the plastic PVC (Poly Vinyl Chloride) in their products.
- Medical settings can also leverage decision tree machine learning algorithms to identify patients at high risk and forecast disease trends.

**7. Random Forest Machine Learning Algorithm**

Random Forest is the go to machine learning algorithm that seems like an extension of the decision tree concept. It uses a bagging approach, and based on random data subsets, it creates a bunch of decision trees. To achieve good prediction accuracy from random forest algorithms, a model is trained with random samples of datasets several times. The final prediction is made based on the combined output of all decision trees — which is often a prediction that appears frequently among decision trees — in this ensemble learning method.

**Applications**

- Automobile industry where it plays a good role in predicting the damage and failure of mechanical parts.
- Banking domain can use Random Forest algorithms to qualify loan applicants or predict whether they are worth a risk.
- Healthcare industry can use Random Forest to predict if a patient is at risk of developing a chronic disease.
- The algorithm is also applied to predict the performance scores and probability of number of social media shares.
- Random forest algorithm can also be innovatively used to classify images or texts or measure performance of speech recognition software.

**8. Logistic Regression**

Logistic Regression machine learning algorithm is used for classification tasks and has nothing to do with ‘Regression’ jobs as possibly inferred by the name. It follows a linear model where logistic function is applied to features of a linear nature to predict the outcome of dependent variables. Here explanatory variables are represented by the odds and probabilities describing the outcome of a single trial. Using the predictor variables, Logistic Regression algorithm determines the probability of reaching a specific level of the categorical dependent variable.

Logistic regression is classified into 3 categories:

- Binary Logistic Regression
- Multinomial Logistic Regression
- Ordinal Logistic Regression

**Applications of Logistic Regression**

- The field of epidemiology can use Logistic regression to plan preventive measures by identifying possible risk factors associated with diseases.
- Classification of specific types of words like nouns, adjectives, pronouns, verbs, etc.
- Applied in risk potential assessment system like credit scores to predict account defaulting
- Used to forecast the probability of weather conditions such as rainfall in a given region.
- To make general predictions in political elections such as candidate’s win or lose or voters’ vote for specific candidates

**9. KNN**

The K-Nearest Neighbors algorithm is a quite simple non-parametric kind of Machine learning algorithm. Since it treats the entire data set as the training model dataset instead of dividing it into separated training set and test set, it requires no explicit training phase for classification. It works on a database where data points are divided into several classes. This is to predict the classification of a new sample point. Though KNN needs huge data storage space, it still performs learning or computing only when a prediction is required. KNN training instances can also be updated and curated to improve prediction accuracy.

**Applications**

- As KNN uses Euclidean distance and Hamming distance methods to calculate similarity of instances, KNN algorithm is widely used in search applications to find similar items.
- Known as K-nearest neighbor search, it will help with the task of finding items similar to a specific item.

**10. Boosting with AdaBoost**

Boosting with AdaBoost are successful boosting algorithms mostly used for boosting classification. They are usually applied when there is a need to handle ample loads of data to attain high prediction accuracy. Boosting algorithms are considered powerful, interpretable and impressively scalable. Its ensemble technique helps create a robust classifier from given number of weak or average predictors by building a model from the training data which is then followed by a second best model that is kept to correct the errors of the first one. Thus, an array of models is formed until the training datasets look perfect.

**Applications**

- Boosted algorithms come handy when you have piles of data to be handled and you seek high predictive power.
- It is used in supervised learning to reduce bias and variance.
- They are ideal for data science competitions like Kaggle, AV Hackathon, CrowdAnalytix.

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**Conclusion**

Here we just described 10 most important **machine learning algorithms** that you can use to build various strong data models that can be used in applications intended for several industries. In conclusion, we can firmly say that machine learning is a vast, expansive and rapidly evolving field where the machine learning algorithms discussed above cover a handful of essential ones. The application of each algorithm depends on the target business problem, expected outcome and the scope of the project on hand. In the end, learning is key to gaining the anticipated results.