Automation is key to many processes these days, so it is not surprising that machine learning gets so much attention. If you’re interested in taking a machine learning course, there are plenty of options to consider. Some are more accurate than others, but various computing tools and techniques have democratized access to all.
To date, all machine learning algorithms fall into four major categories, depending on how much supervision they need. Between supervised and unsupervised machine learning, one can also distinguish semi-supervised and reinforcement learning. In practice, the categorization falls into many other machine learning algorithms.
And Here are The Top Ten Machine Learning Algorithms to Consider.
- Linear Regression
- Logistic Regression
- Linear Discriminant Analysis
- Classification and Regression Trees
- Naive Bayes
- K-Nearest Neighbors
- Learning Vector Quantization
- Support Vector Machines
- Random Forest
- Gradient Boosting and AdaBoost
- Decision Trees Algorithm
- Artificial Neural Networks Algorithm
Machine learning algorithms are classified into 4 types
- Supervised and
- Unsupervised Learning
- Semi-supervised Learning
- Reinforcement Learning
The Top 10 Machine Learning Algorithms in Detail
1. Linear Regression
This algorithm is definitely the machine learning basics most developers can easily understand. It is a pretty simple machine learning algorithm, so it won’t write you a news update on the latest Firefly launch, but it has its applications.
Based on a predictive model, this machine learning algorithm aims to minimize model errors and to make the most accurate predictions possible. However, with this model, the explainability suffers. The basic principle of this machine learning method is to find the specific coefficients for input variables and draw the line between input and output variables.
The best techniques that can help understand linear regression in machine learning projects are linear algebra and gradient descent optimization.
One of the best things about this approach is that linear regression has stood the test of time, being around for over two centuries. The main trick is to avoid correlated, similar variables to remove noise from your data whenever possible. All in all, this is one of the top machine learning examples to try as a start.
2. Logistic Regression
This machine learning technique has been originally borrowed from statistics, so it works great for machine learning in finance as well. So far, this machine learning algorithm acts as the most effective method for dealing with binary, two-class problems.
Similar to linear regression, logistics regression aims to put coefficient values for input variables. However, the value will transform for output variables using logistics or non-linear function.
Logistic regression is one of those machine learning models that give a rationale for predictions. It identifies the probability of prediction instances based on which class, 0 or 1, they belong to.
Once again, this machine learning model works best if you remove similar and correlated variables. This is another fast and effective algorithm that can be found in most machine learning books for beginners.
3. Linear Discriminant Analysis
While logistics regression is the go-to method for binary problems, sometimes you just need more variables. That is where linear discriminant analysis (LDA) comes in. LDA includes statistical data properties and calculates variables for each class. Each input variable will consist of mean value and calculated variance.
LDA is one of those machine learning algorithms that make predictions by calculating discriminative values for each class and choosing the result with the largest value. It acts as a simple and effective method to classify predictive modeling problems that assume the data has a bell curve.
4. Classification and Regression Trees
Classification and Regression Trees (CART), aka Decision Trees, are important tools in machine learning datasets. CARTs are based on a binary model, where each node represents an input variable followed by a split point on numeric variables. Output variables will be located in the leaf nodes and will be used to make predictions by walking the tree splits until reaching a suitable output value.
One of the best things about CARTs, as a machine learning algorithm, is that they are fast to learn and have the potential to make predictions quickly. Another benefit of using CARTs in machine learning applications, according to the best tech news site thetechxplosion, is that they do not call for any data preparations and are quite accurate when solving a broad range of problems.
5. Naive Bayes
Naive Bayes is an essential algorithm in any machine learning tutorial because it is both simple and effective. This predictive model includes two types of probabilities — class probability and conditional probability for each class value. The actual predictions are calculated based on the Bayes Theorem. Assuming Gaussian, bell-curve distribution is the best way to estimate probabilities when working with real data.
The reason why Naive Bayes is branded ‘naive’ is this machine learning algorithm’s assumption that each input variable is independent. So, the biggest drawback of this machine learning algorithm is that it does not always apply when working with real data. Still, it does offer a range of complex solutions for theoretical problems, which is a nice start.
6. K-Nearest Neighbors
KNN is one the most simple and effective machine learning tools that make predictions by searching entire available datasets and choosing the most similar instances, aka neighbors. Once found, this machine learning algorithm summarizes K instances. Notably, found K instances will be different for regression and classifications problems. In the first case, it could be a mean input variable, while in the second one — the mode class value.
One of the biggest challenges of this machine learning classification algorithm is that determining similarities between instances is not always simple. Basing a prediction model on an instance scale could be one of the best ideas. In other words, we go back to the Euclidean distance because, this way, numbers can be easily calculated.
Still, machine learning project ideas based on distance can mean multiple input variables, negatively affecting algorithm precision. To keep KNN predictions accurate, updating and curating all learning instances is the wisest approach. If by now you think that KNN will require a lot of memory data, you’re absolutely right. On the bright side, this machine learning algorithm only performs calculations when necessary. Still, to make this method more functional, you should use only those input variables that are necessary for calculating output variables.
7. Learning Vector Quantization
We come to a point when it’s necessary to compare artificial intelligence vs machine learning. Learning Vector Quantization (LQV) combines a little bit of both, which gives it a competitive edge over KNN machine learning algorithm that relies on your entire training dataset. In contrast to that, LQV relies on an artificial neural network that allows defining the number of necessary instances and can quickly learn what those instances are.
LQV acts as a collection of codebook vectors. At first, vectors are selected randomly but are later adjusted to best suit the training course. Upon learning, these same vectors are used to make predictions pretty much like KNN machine learning algorithm. LQV calculates the most similar instances using distances between codebook vectors and new data instances. It then returns predictions using code values for best matching instances. To ensure ultimate results, it is important to rescale all data in one format.
LQV is a good example of machine learning software that makes accurate predictions, just like KNN, but requires less memory for training dataset storage.
8. Support Vector Machines
Today, Support Vector Machines (SVMs) are a hot, widely discussed topic, and for a good reason. This machine-learning algorithm splits input variable space with a hyperplane. The goal of a hyperplane in SVMs is to best separate points between 0 and 1 classes. To understand this machine learning algorithm, one should imagine a two-dimensional line and multiple dots. Dots act like classes, and a line is a hyperbole separating as many of those as possible. This machine-learning algorithm studies coefficients to properly separate classes.
With SVM, the distance between data points and hyperbole is called a margin. Lines with the greatest margins are most effective for separating classes, so a merging is a primary indicator of a hyperplane’s effectiveness. Support vectors are points used to construct classifiers. Technically, support vectors should define hyperplanes, but in practice, one needs an optimization algorithm to optimize both margins and coefficients.
To date, SVM acts as one of the top, most optimal methods that rely on some out-of-the-box approaches to classifying datasets, which is exactly why SVL gets so much attention these days.
9. Random Forest
Random Forest is simply a collection of decision trees. With this machine learning algorithm, it is important to classify each tree based on its attributes, and each tree will cast votes of its own. More specifically, the Random Forest algorithm is based on an ensemble machine called Bagging or Bootstrap Aggregation.
Bootstrap is now a top method for estimating data sample quantity. It takes all available samples, calculates means, and returns average values that are very realistic. Bagging uses a very similar approach, but instead of samples, it estimates entire models —that is, trees. Together, they take multiple samples of training data and make predictions both on trees and separate samples. The output is the averaged result of both.
10. Gradient Boosting and AdaBoost
This is a good machine learning Python example that is most effective with R Codes. Boosting machine learning algorithms is necessary when you need accurate predictions on massive loads of data. In a nutshell, these machine learning algorithms use ensemble techniques and apply several prediction estimators to achieve the best results.
The main idea is to use multiple-way classifiers to create a strong one. First, this machine learning algorithm builds a model based on training data and then attempts to fix mistakes in the first model. The process goes on until the predictions reach maximum accuracy.
11. Decision Trees Algorithm
AdaBoost is a very similar machine learning software but is used for binary classification. To figure out boosting, it is probably better to start with AdaBoost, mainly used with short decision trees. After creating the first tree, the system thinks over how much attention the next tree will require and so on. Predictions are made for new data after all trees are built.
As you can see, there are plenty of machine learning courses to choose from, but if you’re really interested in a machine learning career, you should probably start fast. The field is developing rapidly, and it looks like the machine learning industry has potential in decades to come.
|1||Linear Regression||Linear regression is well-known algorithms in machine learning|
|2||Logistic Regression||Logistic regression is technique borrowed by machine learning.|
|3||Linear Discriminant Analysis||For more than two classes Linear Discriminant Analysis algorithm is preferred L classification technique|
|4||Classification and Regression Trees|
|5||Naive Bayes||Naive Bayes is a simple but surprisingly powerful algorithm for predictive modeling.|
|6||K-Nearest Neighbors (KNN)||The KNN algorithm is very simple and very effective|
|7||Learning Vector Quantization (LVQ)|
|8||Support Vector Machines (SVM)|
|9||Random Forest||It is a type of ensemble machine learning algorithm called Bootstrap Aggregation or bagging|
|11||AdaBoost||AdaBoost was the first really successful boosting algorithm developed for binary classification|
1. Linear regression
2. Logistic regression
3. Decision tree
4. SVM algorithm
5. Naive Bayes algorithm
6. KNN algorithm
8. Random forest algorithm
9. Dimensionality reduction algorithms
10. Gradient boosting algorithm and AdaBoosting algorithm
1. Linear Regression
2. Logistic Regression
3. Decision Tree
4. SVM (Support Vector Machine) Algorithm
5. Naive Bayes Algorithm