This machine learning tutorial gives the basic and intermediate idea of machine learning. It is designed for beginners as well as professionals.

Machine learning is automated learning with small or no human intervention. It includes programming systems so that they can learn from available inputs. The main purpose of machine learning is to construct algorithms that can learn from erstwhile data and predict new input data. 

This machine learning tutorial is about an introduction to machine learning, how it works, various types, advantages, and disadvantages, and its applications.               

Contents of Machine Learning Tutorial:

  1. What is Machine Learning?
  2. Working on Machine Learning
  3. Need for Machine Learning
  4. Features of Machine Learning
  5. Classification of Machine Learning
  6. Supervised Learning
  7. Unsupervised Learning
  8. Reinforcement Learning
  9. Advantages and Disadvantages of Machine Learning
  10. Application of Machine Learning
  11. Prerequisites for Machine Learning
  12. Conclusion

What is Machine Learning?

Today, Machine learning is one of the most important cutting-edge technologies. It is all around us. From Google maps used for navigation to social media feeds, ML finds its applications everywhere. It is one of the most demanding technologies in every field.

The term Machine Learning was coined by Arthur Samuel in 1959. Therefore, It is defined as the branch of Artificial Intelligence, which gives the computer the ability to automatically learn, predict and improve from past experiences without explicitly programmed.

Working of Machine Learning:

Following are the steps which show the working of machine learning:

  1. Data Collection 

Firstly, data is grouped from different sources like databases, files, etc.

2. Data Preparation 

In this phase, data needs to clean as raw data cannot be directly used for building a model. The methods used for cleaning the dataset are removing the duplicates and normalizing the dataset.

3. Select Machine Learning Algorithm –

In this phase, we choose the best machine learning algorithms (such as supervised learning, unsupervised learning, and reinforcement learning). The selection of algorithms depends on the type of problem and type of data.

If data is labelled and the problem is to classify, then classification algorithms are used.

If data is labelled and the problem needs to perform the regression, then regression algorithms are used.

If data is unlabeled and the problem needs to create clusters, then cluster algorithms are used.

4.  Building Models – 

In this phase, the algorithms find a mapping between input and output and produce a model.

5. Evaluating Model

6.  Predictions

The need for Machine Learning

The need for Machine Learning is increasing with time. It has become essential because of its wide range of applications in today’s world and the incredible ability to solve complex problems efficiently, quickly, and effectively.

Presently, ML is used in face recognition, self-driving cars, recommendation system, language processing, stock market analysis, fraud detection, etc. Various top companies, i.e. FAANG, have built machine learning models to analyze the user interest and to recommend products.

Features of Machine Learning

The various features of Machine Learning are as follows:

  1. Automation is one of the most important features of Machine Learning. It can learn from erstwhile data and ameliorate automatically.
  2. Machine Learning is a data-driven technology.
  3. Accurate data analysis.
  4. Machine learning with IoT makes the efficiency level high.
  5. It is almost similar to Data-mining because it also involves a large volume of data.
  6. Machine Learning with Big data provides a high level of Business Intelligence. 

Classification of Machine Learning  

Machine Learning is typically classified into three types on the basis of nature of learning ‘signal’ or ‘feedback’ available to the learning system:

1.Supervised Learning

2.Unsupervised Learning

3.Reinforcement Learning

Supervised Learning 

This algorithm includes a target or dependent variable, which is predicted from a given set of predictor variables. Using this set of variables, we produce a method that maps input variables to the desired output variables. The aim of supervised learning is to understand a rule that maps inputs to outputs. 

Examples of supervised learning: Decision Tree, Random Forest, KNN, Regression, Logistic Regression, etc.

Further divided into two categories of algorithms:



Unsupervised Learning

This algorithm is just the opposite of supervised learning. In this algorithm, there is no target or dependent variable to predict.

Examples of unsupervised learning: K-means, Random Forests, Hierarchical clustering, and so on. 

Further divided into two categories of algorithms:



Reinforcement Learning

Using this algorithm, the machine is trained to make a specific decision. Unlike supervised learning, no data is provided to the machine. Here, the algorithm trains itself continually by using trial and error methods and feedback methods. The machine learns from past experience and tries to make accurate decisions.

Game Playing and Robot navigation are examples of Reinforcement learning.


Following are the advantages of Machine Learning:

  1. Rapid analysis prediction and processing
  2. No human intervention (Automation)
  3. Handling Big Data:Machine Learning algorithms help in handling and improving a large volume of data.
  4. Wide Applications:Machine Learning has become essential because of its wide range of applications in today’s world and the incredible ability to solve complex problems efficiently, quickly, and effectively.


Following are the disadvantages of Machine Learning:

1. Time-consuming 

Machine learning algorithms deal with a large volume of the dataset,so the time to learn from data and process also increases. 

2. Algorithm selection

 This is a disadvantage because it is a manual and hard task to run the models with different algorithms and to identify the most suitable algorithm on the basis of results.

3. High error-susceptibility 

Application of Machine Learning

The developed machine learning algorithms are used in various applications, which are as follows: 

1. Web Search Engine: Search Engines like Google, bing plays a very important role in today’s world. They work so well because the system has learned to rank the pages with complex machine learning algorithms.

2. Spam Detector: With the help of a supervised ML algorithm, the email filter out the spam mail from the mailbox.

3. Product Recommendations: On the basis of your old searches, e-commerce websites are able to give you the recommendation of products. All this is achieved by machine learning algorithms running at the backend.

4. Automation

5.Image and speech recognition

6. Online Fraud Detection

Some other applications of machine learning are as follows:

  • Game Playing
  • Healthcare
  • Natural Language Processing
  • Stock market analysis
  • Weather Forecasting
  • Software Engineering
  • Spark

Prerequisites for Machine Learning:

To learn Machine learning, one should have good knowledge of the following:

1.Basic understanding of python programming language or R.

2.Good understanding of Probability and Statistics.

3.Good knowledge of linear algebra and calculus.


Machine Learning with artificial intelligence (AI) has proved lucrative to us in every field i.e. medical, education, forecast, robotics, gaming, etc. It is present all around us, and it has made our life easier and profitable. It has made a new era in the field of computer science.