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

Miscellaneous

Top 5 programming languages and their libraries for Machine Learning Basics Vectors in Linear Algebra in ML Decision Tree Algorithm in Machine Learning Bias and Variances in Machine Learning Machine Learning Projects for the Final Year Students Top Machine Learning Jobs Machine Learning Engineer Salary in Different Organisation Best Python Libraries for Machine Learning Regularization in Machine Learning Some Innovative Project Ideas in Machine Learning What is Cross Compiler Decoding in Communication Process IPv4 vs IPv6 Supernetting in Network Layer TCP Ports TCP vs UDP TCP Working of ARP Hands-on Machine Learning with Scikit-Learn, TensorFlow, and Keras Kaggle Machine Learning Project Machine Learning Gesture Recognition Machine Learning IDE Pattern Recognition and Machine Learning a MATLAB Companion Chi-Square Test in Machine Learning Heart Disease Prediction Using Machine Learning Machine Learning and Neural Networks Machine Learning for Audio Classification Standardization in Machine Learning Student Performance Prediction Using Machine Learning

Machine Learning Classification Algorithm

Introduction to ML Classification Algorithm

The process of guessing a category or a class from a given set of observations is known as Classification. The output can be categorized into "Yes" or "No" or "Red" or "Black." It makes a conclusion based on observable values. It is a kind of supervised learning, so the target is always given along with the dataset. It has a wide variety of applications ranging from medicine to marketing.

It basically approximates the mapping function (f) from the input variables to the output variables. One of the best examples would be the spam mail detection, as the outcome will be either categorized into the spam or the non-spam.

For this, we will require a classifier and will need to train it. In the case of spam mails detection, we will need to train the classifier with spam and no-spam emails, which will act as the training data.   

Different types of learners in Classification

There are following types of learners in Classification:

Lazy learners: These are those learners who wait for a while for the test data to get available after the train data is stored. In this case, Classification is done after getting the test data. It takes much more time to make a prediction and spends very less time on training. Its examples are K-nearest neighbor and case-based reasoning.

Eager learners: These are the opposite of lazy learners as they do wait for the testing data to get appear after storing the train data. In fact, they spend more time training the data and less time on prediction. It includes Naïve Bayes, Artificial Neural Network (ANN), and Decision trees.

Types of ML Classification Algorithms

  • Logistic Regression
  • Support Vector Machine
  • Decision Tree
  • Naïve Bayes
  • Random Forest

All these topics will be further discussed in detail chapters wise.

Applications

Some of the applications of classification algorithms are given below:

  • Speech Recognition
  • Handwriting Recognition
  • Spam Email Classification
  • Biometric Identification
  • Emotion analysis
  • Recognition of Cancer tumor cells, etc.