Machine Learning Tutorial

What is Machine Learning? Machine Learning Life Cycle Python Anaconda setup Difference between ML/ AI/ Deep Learning Understanding different types of Machine Learning Data Pre-processing Supervised Machine Learning

ML Regression Algorithm

Linear Regression

ML Classification Algorithm

Introduction to ML Classification Algorithm Logistic Regression Support Vector Machine Decision Tree Naïve Bayes Random Forest

ML Clustering Algorithm

Introduction to ML Clustering Algorithm K-means Clustering Hierarchical Clustering

ML Association Rule learning Algorithm

Introduction to association Rule Learning Algorithm


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How Many Types of Learning are available in Machine Learning?

Computer science's branch of machine learning allows machines to acquire knowledge from data without having to be explicitly programmed. It is a branch of artificial intelligence which makes use of statistical techniques to let robots learn from data and anticipate the future.

Machine learning encompasses a variety of learning methods, each having particular properties and uses. We shall examine the most prevalent learning modalities in machine learning in this post.

Supervised Education:

The most typical learning method used in machine learning is supervised learning. It entails using a labelled dataset to train a machine learning model, where each data item is labelled with an output that corresponds to it. On the basis of the patterns it discovers from the labelled dataset, the model subsequently learns to make forecasts on fresh, unlabelled data.

For problems involving classification and regression, supervised learning is frequently utilised. Although in regression, the model is trained to anticipate a continuous output variable, in classifications, the model learns it predict the class of a fresh data point.

Unsupervised Education:

An unlabelled dataset is used to train the machine learning algorithm in unsupervised learning. Finding patterns and structure inside the data without even any prior knowledge of output variable is the aim of unsupervised learning.

Unsupervised learning is frequently used for clustering and dimension reduction. While dimensionality reduction of characteristics in the dataset, clustering entails gathering together comparable data points.

Learning That Is Semi-Supervised:

Learning that incorporates both supervised as well as unsupervised learning is known as semi-supervised learning. The machine learning algorithm is taught on both labelled and unlabelled data in semi-supervised learning, where the labelled data is used to direct the learning experience and the two datasets is used to understand the underlying data structure.

When getting labelled data is expensive and time-consuming yet there is a lot of unlabelled data available, semi-supervised learning is frequently used.

Reward-Based Learning:

By interaction with the environment and feedback inside the way of rewards or penalties, the machine learning algorithm learns through reinforcement learning. Learning a policy that maximises the total reward over time is the aim of reinforcement learning.

Applications where the agent should acquire the ability to make choices in a constantly changing environment include gaming, robotics, and autonomous driving. These applications frequently use reinforcement learning.

Adaptive Learning:

Ensemble learning is a sort of learning in which a computer learning model that has been trained solely on a single task is used as the foundation for another task that is unrelated but nonetheless important. Transfer learning is the process of using what you learned from one assignment to help you do better on another.

When there is a great amount of labelled available data for the initial task but little labelled data available again for second task, transfer learning is frequently used.


In summary, machine learning provides an array of learning modalities, each with distinctive properties and uses. Unsupervised, semi-supervised, reinforcement, and transfer learning are the next most popular learning methods after supervised learning.

To choose the best algorithm for a specific task and achieve the best performance, it is crucial to understand the many forms of learning in machine learning.

Here are some illustrations of each sort of machine learning learning:

Supervised Education:

  • Image classification: Cat and dog images are categorised.
  • Voice recognition is the process of typing spoken words.
  • Determine the sentiment (positive or negative) of a text using sentiment analysis.
  • Fraud detection is the process of identifying phoney transactions using historical information.

Unsupervised instruction:

  • Clustering is the process of combining related data items, such as when customers are grouped according to their purchasing patterns.
  • Finding uncommon or odd data points, including such spotting credit card theft, is known as anomaly detection.
  • Dimensionality reduction is the method used to decrease the amount of features inside a dataset while maintaining the dataset's key information, for as by decreasing the amount of pixels in an image.
  • Teaching a system to play the games like Go or chess using reinforcement learning.
  • Robotics: The study of programming a machine to move around and carry out activities like collecting things or putting together parts.
  • Somewhat guided learning
  • Utilizing a combination of labelled and unlabelled to train a system for machine translation is known as language translation.
  • With a minimal quantity of labelled data, an object detection model is trained to find things in pictures or videos.
  • Transfer learning in image recognition is utilising a model that has already been trained to categorise photos that are different from those it was first trained on, for instance employing a model is trained on animal images to categorise photographs of cars.
  • Natural language processing is the use of a language model that has already been trained to carry out a new task, like sentiment analysis, machine translation.