Best 13 Machine Learning Methods And Techniques For Newbies

Machine learning methods are the techniques by which an ML engineer implements his projects. After getting a real-life problem the ML expert considers which method of machine learning should they follow. Supervised and Unsupervised machine learning are the major techniques of machine learning but there are many other techniques. In our today’s learning, we will discuss each of them with a relevant example for the newbies. 

What We Will Learn?

• Supervised machine learning and unsupervised machine learning are the major techniques or methods of machine learning. We will discuss in detail both ML methods.
• Semi-supervised and reinforcement machine learning is the second major methods of machine learning. We will also discuss those.
• We will discuss some other measures methods and techniques of machine learning.
• Will try to cover each of the topics with relevant example.
• At the end of the session, you will be able to implement the perfect method to required machine learning projects.

How Do Machines Learn in Machine Learning?

Machine learning learns by various methods or techniques. Which one is the best method depends on the requirement and the state of the problem? Sometimes the volume of data, configuration of hardware, classification of data set and many other relevant factors influencers on choosing the correct method. Moreover, it is the preference of the machine learning engineer to choose the machine learning methods.

Machine Learning Methods

Machine learning is basically to speak about two methods which are supervised learning and unsupervised learning. In our previous article, we already discussed supervised learning Vs unsupervised learning. In today’s article, we will brief in detail each part of the machine learning methods. The following figure will help you out to learn in detail.

1. Supervised Learning

Supervised learning is the method of machine learning which is used for label data. The task of leveling the data is called the train. It allows the machine to predict any situation based on the train.

Algorithm of Supervised Learning

Will make you understand the algorithm of machine learning by a graph. In this graph, we can see input is given as training data. The output is labeled with another set of data. The input is considered as X and output is considered as Y. The relationship between X and Y is a reverse engineer where the function is Y = f(X). Predict Y for the new instance X(new) using y = f(X).

Steps of Supervised Learning

Supervised learning follows several steps like:

• Data preparation for the method
• Dance training step
• Test or evaluate the step
• The development off the production

How to test the supervised learning algorithm?

The algorithm of supervised learning is tested in the following ways:

• When the training of algorithms is over test testing.
• If the algorithm is well trained that it can predict the new test data.
• But if the learning is not proper then there is the possibility of an underfitting situation. Then the algorithm will not work properly. In this situation, retiring may be needed.
• If it is too intensive on learning the training data then it will be overfitting. In this situation, the algorithm is not able to handle the new set of data. To solve this problem we follow a technique called regularization.

Some Real-life Example of Supervised Learning

• Supervised machine learning is used to recognize voice speech like t voice assistance. Apple Siri, Microsoft Cortana, Google Assistant and, Amazon Alexa are the best example of supervised machine learning.
• In the online selling services that chatbot performs the task of a sales assistant.
• Gmail features a new mail and sends it to an inbox or spam folder based on supervised learning.
• Many weather apps use a prediction system based on prior analysis and knowledge of weather. This is an example of supervised machine learning.

Types of Supervised Learning

There are two types of supervised learning which are as follow:

• Classification
• Regression

2. Classification Machine Learning Method

Classification means the output is classical. Two or more classes use to represent the output. In the classification method, the output fixed like true or false, black or white, yes or no, fit or unfit, etc. 

Classification represents on state based on a situation. For example, a person can be male or female. He can be a defaulter for loan or not. He can pay a loan or not. All data are predefined which known as data labeling. It is used usually for the financial sector.

The classification machine learning method is used for prediction.

The classification machine learning method is used for predicting or discrediting a class or label. It is involved in assigning a new input variable x. The X is used pain in the data who is known as data labeling. Repeating the processor when you get the same input then it works without any instruction is known as “learning”.

What You Can Do With Supervised ML?

After showing a picture it can identify is it a cat or a dog? If that is predefined earlier. To identify the spam email, predicting the probability of rain, the possibility of repaying of loan, selecting the positivity of negativity of a post what are the types of the movie.

Types of classification machine learning method

Classification machine learning method is also divided into three subcategories such as:

• Binary classification
• Multi-class or multi nominal classification
• Multi-label classification

Binary Classification

In the binary classification method, the input is divided into two groups. The example can be As such, is the picture indicates cats or dogs? If the email is spam or not? Most of the prediction is calculated based on a binary classification method.

Multi-class/ multinomial Classification

In this method, the input is classified into more than two groups. For example, the news can be e property of several categories. The movie can be different types. A text can be classified into positive, negative or neutral.

Multi-label Classification

When we have more than one discrete classes we can call it multi-label classification. which is the generalization of multiple classes which is based on a single level problem. Categorize instances into preciously one or more classes.

Algorithms of the Classification Method

There are various algorithms to use classification methods to make your prediction. Some of these are:

Neural network: it is based on various cases. Google classifies people and places based on a neural network.

K- nearest neighbor: K-NN is used for the recommendation system. When you search for something it requirements for similar things.

Decision tree: which is used for both regression and classification machine learning method. It is used to solve problems visually could help of nodes and leaves.

Random forest: this machine learning model is also used for classification and regression methods. It develops multiple decision trees and merges them to get a more accurate prediction. In several circumstances, you can use this method for recommendation engine and feature selection.

Support vector machine: it is also used for regression and classification problems. It has lots of cases for handwriting recognition, face recognization, send image recognition.

Naive Bayes: it is very easy to implement an algorithm that is determined as a text document that can have one or more than one category or not. Classifying the news category is an example of Naive Bayes.

3. Regression Machine Learning Method

Regression is used for predicting continuous quantity output. The variable output is a real value such as floating integer decimal or any point value. 

Example of Regression

We have discussed classification is used for predicting a situation whether it will be rain or not. But the regression is the quantity of rain in that period.

In regression analysis on the variable is dependent and independent. For example, if you ask for a salary it depends on your experience. An example can be your weight is depending on your age. Here salary and age is the independent variable and others are the dependent variable.

Types of the Regression Method

• Simple linear regression
• Support vector
• Decision tree
• Random forest
• Polynomial

The Simple Linear Regression Method

Simple linear regression predicts the target variable y based on the input x. The equation is Y = a +bX. the relationship between the two variables is linear. Here we can represent Y as salary and X as experience.

Support Vector Regression Machine Learning Method

Support vector regression helps to identify a hyperplane with the maximum margin. The maximum number of data points are within that maximum margin.

Decision Tree Regression

a decision tree is used for both classification and regression. Where are all the values are split? The problem is solved with several nodes. Is nodes represent a question.

The Random Forest Regression Method

Random forest is used to prevent outfitting by a random subset of features. It is used to decide on the decision tree.

Polynomial Regression

In this regression method, the data scientist applies polynomial features into the original feature and fits with linear regression. If we consider the last example where Y = a +bX. It will be transformed into another equation like

Y = a+bX+cX^2

4. Unsupervised Learning Method

The unsupervised machine learning method is a subset of machine learning who is used for extracting inference from a data set. The dataset is consists of input data without any data labeling.

Unsupervised Learning Example

NASA is continuously discovering a new planet and astronomical object. Those objects never are seen by anyone even the machines also. But the question is how the computer understands is an astronomical object? Computer applied machine learning here which is without any labeled data.

Types of Unsupervised Learning Method

We have found three types of unsupervised learning who is are as follow:

Clustering Method

The clustering method is the most common unsupervised learning method. It is used to find the data cluster which is mostly close to matching data.

Example of the clustering method

Online news portal we can see much similar news it is not categories earlier. Sports, business news, technology, and various categories are the example of the clustering method.

Visualization Algorithm

Visualization algorithm for an unsupervised algorithm that is used to display data in 2D or 3D format. The age of understanding those data separated from the cluster.

Anomaly Detection

Without any previous training, anomalies are detected by the machine. The credit card fraud detection, criminal investigation are the example of anomaly detection.

5. Semi-supervised Learning

The semi-supervised machine learning method is a combination of supervised and unsupervised methods. It is the hybrid method where data can be labeled and some data can be unlabelled.

Example of Semi-supervised Learning

Google photos are identifying some people in the various photo. If it gets the name of the person in one photo automatically tag all the photos by that name.

6. Reinforcement Learning

Reinforcement machine learning allows the system to learn from the environment and set a benchmark for ideal behavior. It automatically collects data and makes a prediction set. If there is any deviation from that set it gives the signal.

Some Features of Reinforcement Learning

• Reinforcement learning learns from the environment.
• It observes, select and takes certain action and gets the rewards or penalties.
• To maximize the rewards and minimize the penalties the agent learns the strategy or policy.

Example of Reinforcement Learning

• The news of cyclone, tornado, earthquake is the best example of reinforcement learning.
• Many manufacturing system robots use a deep reinforcement learning method. 

Some Other Machine Learning Methods

Up to this time we have covered the two major machine learning methods. There are many methods other than the two methods. We will brief in detail those methods.

7. Dimensionality Reduction

Dimensionality reduction is a comparison of a file. That means it kicks out the not required information. Buy this it reduces the complexity of data and makes the data more useful and relevant.

Example of the Dimensionality Reduction Method

An example of dimensionality reduction can be the images/video size reduction. Where the required part keeps and unnecessary part is deleted.

8. Ensemble Machine Learning

Suppose you need a bicycle which must be fashionable. The available bicycle from online or store is not according to your choice. What you can do?

You can collect several parts of a bicycle and assemble according to your own. Ensemble Machine learning is the same type of bicycle assembling. It connects the decision tree and predicts it on its own. 

Ensemble Machine learning is better than any other machine learning method because of it’s connecting capability. A single Method can be biased but Ensemble is the combination of decision tree so it does not follow bias functions.

Example of Ensemble Machine learning techniques

The ensemble is used for Kaggle competitions. This method is also used for XGBoost and lightGBM.

9. Neural Network

A neural network is the set of algorithms that are designed by the human brain. It can recognize the pattern that helps to cluster and classify. It is inspired by a biological neural network that is applied to very complex mapping.

Examples of Neural Network

• The neural network is applied to identify the voice expression, speech recognization, fiscal expression, and pattern recognition.
• To find out any anomalies neural network is widely used.
• To protect the credit card fraudulent transaction, and anomalies in the nuclear power plant neural network are widely used.
• It is also used for prediction of future stock prices and the preference of movies for a particular person.

10. Deep Learning Method

Deep learning is a subset of machine learning which implement through a neural network. The function and structure of deep learning are like a brain that is called an artificial intelligence network. 

When there is lots of leveled data, sophisticated algorithm and high-performance GPU then we use deep learning methods. in deep learning, there is no requirement to import the data manually. Deep learning is highly expensive and it requires a higher configuration computer.

Example of Deep Learning Method

If in the search engine you search a particular topic and get a result on the first page. You go through up to the end and on the first page, you found nothing relevant to your searches. Then you again go to 2, 3 and 4 pages. If a similar thing will happen for that topic then the machine automatically learns through The deep learning about your preference.

11. Transfer Learning

The transfer machine learning method is used by the data scientist. After preparing a single module you can transfer the model to another model. When you develop a model train a model in a month then the new model you can transfer the knowledge. By adding the new layer of neuron network you can adopt the new task.

Example of Transfer Learning

You have developed a model by a month program. The model is based on designing a t-shirt, Polo and shorts. Now if your boss order you to develop a new model design of jeans, Polo and t-shirt. So, we can just transfer your knowledge by the transfer learning method.

12. Natural Language Processing Methods

The natural language processing machine learning method is using since a long time ago. When you type something on your mobile or computer it shows the misspelled words. There is a chance of an auto-correction of your mistake. It has been trained for a long time. So the language becomes natural.

Example of a Natural Language Processing Method

Machine does not understand the language of humans. But it can suggest if you type any wrong. typing on a mobile phone or computer and showing the misspelled word are the example of a natural language processing method.

13. Word Embedding

In a text document, TFM and TFIDF are numerical presentations that represent frequency and weight. It can quantify the similarity of words to allow aromatic similarity between words.

Example of Word Embedding

The method based neural network word2vec that allows words in a corp to a neutral vector.

Final Words

After a long review of machine learning methods, we find two major techniques of machine learning which are supervised and unsupervised methods. Semi-supervised and reinforcement learning is also a popular machine learning technique for engineers. There are some uncategorized machine learning methods like natural language processing, transfer learning, Word Embedding, Ensemble Machine learning and deep learning which are also popular to ML project developers. We hope this article will clarify all of your doubts to become a machine learning expert.