Top Trending Machine Learning (ML) Algorithms To Learn In 2022

Artificial Intelligence is rapidly becoming the present and future of technology. Machine learning algorithms have been created to handle challenging real-world situations. These algorithms are highly efficient and self-modifying, as they improve over time with the addition of more data and minimal human involvement. Let’s go over the top machine learning algorithms you should be familiar with to keep up with the latest ML advancements.

1. Linear Regression: This supervised learning ML algorithm estimates values such as property prices and total sales using continuous variables. The algorithm depicts the relationship between two variables, one independent and the other dependent. When the independent variable is changed, it affects the dependent variable. We may establish a link between the independent and dependent variables by fitting the optimal line.

The regression line is the best fit line and is represented by the linear equation :

 Y= a+bX

Here Y is the output variable, X is the input variable, and a and b are the line’s intercept and slope respectively.

The purpose of linear regression is to determine the coefficients a and b’s values and find the best fit line for the data.

2. Logistic Regression: Unlike Linear Regression, Logistic Regression is used to predict discrete values. It is well suited to binary classification, in which an event is classified as 1 if it occurs and as 0 if it does not.

The result of logistic regression is a set of probabilities as the output, which is between 0 to 1. So, if we’re trying to predict if a candidate wins an election or not, where a win is given by 1, and a loss as 0, and our algorithm provides a candidate with a score of 0.95, it believes that candidate is very likely to win.

The y-value is obtained by using the logistic function h(x)= 1/ (1 + e-x) to log transform the x-value. The probability is then forced into a binary categorization by applying a threshold.

3. Decision Trees Algorithm: It’s a supervised learning algorithm commonly used to solve classification problems. It works for both categorical and continuous data. Using a tree branching methodology, all alternative outcomes of a decision are shown. Internal nodes represent tests on various qualities, branching represents test results, and leaf nodes represent the choice reached after computing all attributes.

4. Random Forest Algorithm: Random Forest is an ensemble of decision trees. The Random Forests Algorithm overcomes some of the drawbacks of the Decision Trees Algorithm, namely that as the number of decisions in the tree grows, the accuracy of the outcome diminishes. We have a collection of decision trees in Random Forest. Each tree categorizes a new object based on attributes, and we say the tree “votes” for that class, and the classification with the most votes is selected.

5. CART: CART (Classification and Regression Trees) implements Decision Trees. The root and internal nodes are non-terminal nodes in Classification and Regression Trees. The leaf nodes are the terminal nodes. The leaf nodes represent the output variable, whereas the non-terminal nodes represent a single input variable and a splitting point on that variable. To create predictions, the model is employed as follows: To get to a leaf node, walk the tree’s splits and output the value existing at the leaf node.

6. Support Vector Machine Algorithm: Support Vector Machine Algorithm can be utilized for classification or regression problems. By locating a particular line (hyperplane) that divides the data set into various classes, the data is separated into different classes. The Support Vector Machine Algorithm seeks out the hyperplane that maximizes the distance between the classes (margin maximization), increasing the likelihood of correctly categorizing the data. The SVM is two-dimensional at low feature levels, but it becomes three-dimensional as the number of groups or types detected increases.

7. Naïve Bayes Classifier Algorithm: It’s a classification method based on Bayes’ theorem and the assumption of predictor independence. A Naive Bayes classifier, in simple terms, posits that the existence of one feature in a class is independent of any other component. That is why it is regarded as Naive. Gmail uses this algorithm to determine whether an email is a spam or not.

8. K Nearest Neighbors(KNN) Algorithm: It can solve problems involving classification and regression. It is, however, more commonly utilized in categorization problems. The algorithm separates data points into different classes using a comparable measure like the distance function. The output variable is then summarized for these K occurrences. A forecast for a new data point is created by searching through the complete data set for the K most comparable examples (neighbors). This could be the mean of the outcomes in a regression problem or the mode in a classification problem. Note that this algorithm is computationally expensive and requires the normalization of the variables.

9. K Means Clustering Algorithm: It’s a type of iterative unsupervised Machine Learning algorithm that divides data into clusters based on similarity. It creates k cluster centroids and assigns a data point to the cluster whose centroid is closest to the data point. K-Means Clustering is a primary tool in consumer analysis.

10. Principal Component Analysis(PCA): By lowering the number of variables, Principal Component Analysis (PCA) makes data easier to analyze and visualize. This is accomplished by recording the highest variation in the data in a new coordinate system with axes referred to as principal components.

11. Dimensionality Reduction Algorithms: With so much data available, we’re presented with a plethora of options, which sounds great for constructing a robust model but presents obstacles such as identifying the most critical variables. In such circumstances, the dimensionality reduction approach, combined with other algorithms such as Decision Tree, Random Forest, PCA, missing value ratio, Factor Analysis, and others, can be beneficial.

12. Gradient Boosting Algorithms:

  • GBM: Boosting is a collection of learning techniques that combine the predictions of several different base estimators to increase resilience over a single estimator. It builds a strong predictor by combining many weak or mediocre predictors.GBM is a boosting algorithm utilized when there is a lot of data, and we need to create a prediction with high accuracy. 
  • XGBoost: The XGBoost has a tremendous predictive capacity, making it the ideal choice for various competitions. It supports multiple objective functions, including regression, classification, and ranking. It incorporates a linear model and a tree learning method, making the approach ten times faster than existing gradient booster techniques. With built-in cross-validation at each iteration of the boosting process, XGBoost may be coupled with Spark, Flink, and other cloud dataflow systems.
  • LightGBM: LightGBM uses tree-based learning algorithms for gradient boosting. The framework is based on decision tree algorithms that may be used for ranking, classification, and various other machine learning applications. It is prepared to handle massive amounts of data while also improving accuracy. Faster training speed and efficiency, lower memory consumption, parallel, and GPU learning capabilities are some of the benefits offered by LightGBM.
  • CatBoost: Yandex’s CatBoost is an open-source robust machine learning algorithm. It’s simple to integrate with deep learning frameworks like TensorFlow from Google and Core ML from Apple. The best part of CatBoost is that, unlike other ML models, it does not require substantial data training and can function with many data types.

13. Generative Adversarial Networks (GANs): GANs, or Generative Adversarial Networks, are a type of generative modeling that employs deep learning techniques such as convolutional neural networks. A Generator and a Discriminator make up a Generative Adversarial Network design, primarily used for image synthesis. The Generator iteratively attempts to rebuild tens of thousands of photos in a dataset. The Discriminator rates the Generator’s work after each effort and sends it back to try again, without realizing how the previous reconstruction went wrong. The Generator has a comprehensive map of relationships between points in the dataset by the end of the training.

14. Transformers: Transformer is a groundbreaking Natural Language Processing (NLP) technology that powers the autoregressive language model and AI poster-child GPT-3, among many others. It solves the problem of sequence transduction, often known as ‘transformation,’ which entails converting input sequences to output sequences. A transformer also receives and processes data in real-time rather than in batches, allowing for the persistence of memory that RNN architectures cannot achieve.

15. Apriori: In a transactional database, the Apriori algorithm is used to mine frequent itemsets and subsequently construct association rules. The algorithm uses the IF-THEN format to build association rules. This suggests that if event A occurs, then event B is likely to occur with a certain probability. Google auto-complete is an example of the Apriori Algorithm in action.