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author

Lucas White

Full Stack Engineer

“Google’s self-driving cars and robots get a lot of press, but the company’s real future is in machine learning, the technology that enables computers to get smarter and more personal.”

– Eric Schmidt (Google Chairman)

We all are living in a period of DEVELOPING. According to Eric Schmidt – “Machine Learning is the future of technology”. It is the major component of Artificial Intelligence. So, is it true that machine learning influences the performance of the business?

Machine Learning Algorithm


All your questions and doubts are answered in this article, you find three types of machine learning that useful to your business and the top 5 types of Machine learning Algorithms to make yourself more familiar with the concept of ML.

Introduction to Machine Learning


No doubt, machine learning has become a diverse business tool to enhance the numerous elements of business operations. Machine learning- “it is the method of data analysis which automates the analytical model.” As well as it is a branch of artificial intelligence based on the idea that the system can learn from data, identify the pattern and make decisions with minimal human interference.

Machine learning (ML) is the scientific study of algorithms and statistical models that the computer system used to perform a specific task without using explicit instructions, replying on pattern and inference instead. It is also a subset of artificial intelligence. -via Wikipedia

If you’re a beginner, machine learning can be confusing for you– how to choose which algorithms to use, from the apparently limitless options, and how to know which one will provide the right predictions (data outputs). The machine learning is a way for computers to run various algorithms without direct human oversight in order to learn from data.

Machine learning can include a variety of tasks in order for the machine to determine a high-probability result for different information, such as the functions between input and output or the hidden structures in unlabeled data.

So, just before starting with Machine learning algorithms, let’s have a look at types of Machine learning which clarify these algorithms.

Three Types of Machine Learning

Types of Machine Learning


There are three types of machine learning which help the developer to create something innovative.

1. Supervised Learning

  • Supervised learning is consist of a target variable (or dependent variable) which is to be divined from a given set of predictors (independent variables). Using these set of variables, that generates a function that map inputs to desired outputs.
  • The training process continues until the model achieves a desired level of accuracy on the training data. Supervised learning is the task of inferring a function from labeled training data.
  • Examples of Supervised Learning:


    i.) Regression, 
    ii.) Decision Tree, 
    iii.) Random Forest,
    iv.) KNN,
    v.) Logistic Regression, etc.

2. Unsupervised Learning

  • Unsupervised learning has less information about objects, in particular, the train sets unlabeled. What is your goal now? It’s possible to recognize some comparisons between groups of objects and include them in relevant clusters.
  • Some objects can differ hugely from all clusters, in this way you assume these objects to be excepted. This method allows you to significantly improve accuracy because we can use unlabeled data in the train set with a small amount of labeled data.
  • This category of machine learning is known as unsupervised because unlike supervised learning there is no teacher. Algorithms are left on their own to create and return the interesting structure in the data.
  • The goal of unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data.
  • Examples of Unsupervised Learning:


    i.) Apriori algorithm and
    ii.) K-means.

3. Reinforcement Learning

  • These methods allow the user to decide the best action, based on the current state and learned behaviors that maximize the rewards. This approach often used in robotics.
  • Using this learning, the machine trained to make specific decisions.

    It works like- The machine displayed to a setting where it trains itself continually using trial and error. This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions.
  • Example of Reinforcement Learning:


    i.) Markov Decision Process
Markov Decision Process

What is Machine Learning Algorithms?


Machine learning algorithms are programs that can learn from data and improve from experience, without human interference. Learning tasks may include learning the function that drafts the input to the output, learning the hidden structure in unlabeled data; or ‘instance-based learning’, where a class label is produced for a new instance by analyzing the new instance (row) to instances from the training data, which were stored in memory.

Machine learning algorithms


Machine Learning algorithm is an evolution of the regular algorithm. It makes your programs “smarter”, by providing them to automatically learn from the data you provide. The algorithm is mainly divided into:

  • Training Phase
  • Testing phase
Training and Testing Phase

Now, I am going to share the top five types of machine learning algorithms which improve business progress. These algorithms are user-friendly and encourage several goals. Besides, all of them are popular and utilized by thousands of enterprises.

Types of Machine Learning Algorithms for beginners.


There are top 5 machine learning algorithms for beginners offer a fine balance of ease, lower computational power, immediate, and accurate results.

1. Linear Regression


Linear Regression

  • Linear regression is a classification method, not a regression method. This predictive modeling strategy is very well understood, as statistics using this tool for decades before the invention of the modern computer.
  • The goal of linear regression is to make to most accurate predictions possible by finding the values for two coefficients that weight each input variable. These techniques can include linear algebra, gradient descent optimization, and more.
  • Employing linear regression is easy and usually provides accurate results. More skilled/experienced users know to remove variables from your training data set that is closely correlated and to remove as much noise (unrelated output variables) if possible.

2. Decision Tree

Decision Tree

  • Another popular and easy to understand an algorithm is decision trees. Their graphics help you see what you’re thinking and their engine requires a systematic, documented thought process.
  • The idea of this algorithm is quite simple. In every node, you choose the best split among all features and all possible split points. Each separation is selected in such a way as to maximize some functional. In classification trees, you use cross-entropy and Gini index.
  • In regression trees, you minimize the sum of a squared error between the predictive variable of the target values of the points that fall in that region and the one we assign to it.

3. Support vector Machine

Support Vector Machine

  • This algorithm also known as SVM, it analyzes the data set into classes. The main aim of support vector machine (SVM) is its helpful approach to future classifications. It used to find out the line that separates the training data to particular classes.


    One more thing that you should know is that it provides you the maximum margins to enter any future data into classes.
  • A beginner or experienced, who so work on this, it is the best for training data because nonlinear data can also be programmed in Support vector machine (SVM).

4. Apriori

Apriori Algorithm

  • Apriori learning used in a transactional database to work frequent itemsets and then generate association rules. It popularly used in market basket analysis, where one checks for combinations of products that frequently co-occur in the database.
  • The basic principle of Apriori used in market analysis. This algorithm checks for the positive and negative correlation between products after analyzing the A and B in data sets. It specially used by sales teams who keep an eye on the baskets of customers to find which products the customers will purchase with other products.

5. K-means clustering

K-means Clustering
  • Clustering used for group sample such as the objects within an identical cluster is more similar to each other than to the object from another group.
  • K- means clustering algorithms kinds of data sets through defined groups. It is an iterative process which also put out similar groups with input data attached.
  • Let’s take an example, If you use K- means algorithm for classifying web results for word civil, then it will show the results in the form of groups. And Accuracy is the main advantage of this algorithm. As well as, it has developed a reputation for providing the streamlined groupings in a short time as compared to other algorithms which give meaningful groups based on internal patterns. This algorithm helps marketers to identify target audience groups.

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About The Author

Lucas is a passionate software engineer with over fifteen years of experience developing software under both web and Windows environments. He specializes in Node.js and .NET frameworks, as well as React and AngularJS on the front-end. Lucas holds a Master’s degree in computer science and has worked with a variety of Agile teams. He communicates extremely well.

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