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

Understanding different types of Machine Learning

A machine learns from a trained data set to create a model. Whenever there is a new input to the algorithm, it predicts on the basis of the model. The evaluation is made in terms of accuracy, and the algorithm is deployed only if the accuracy is accepted by the algorithm of machine learning, else the model is trained repeatedly with a large data set. The machine learning algorithm can be broadly classified into 3 main categories;

  1. Supervised learning
  2. Unsupervised learning
  3. Reinforcement learning 
understanding- different-types-of-machine- learning

Supervised Learning

Supervised learning is the one done under the supervision of a teacher or a supervisor. Basically, the model is trained over a labeled dataset. A labeled database is one which contains both inputs as well as the output parameters. Here the trained dataset act a teacher, and its primary role is to train the model. The prediction is made once the model is built. It works in the same manner as the student learns under the supervision of the teacher. 

understanding- different-types-of-machine- learning-1

In the supervised learning, the input, as well as the output, is provided to the model aiming to find a mapping function that maps an input variable (P) to the output variable (Q). The supervised learning is used for fraud detection, risk assessment, spam filtering, etc.

 Types of Supervised Learning:-

understanding- different-types-of-machine- learning-2

Classification: - Classification based algorithms are used whenever there is some certain (discrete value) output variable, i.e., either Red or Blue, Yes-No, Male-Female, 0 or 1, etc. The real-world example would be spam filter. 

Random Forest

  1. Naïve Bayes
  2. Decision Trees
  3. Logistic regression
  4. Support Vector Machine(SVM)

Regression: - The regression-based classification method is used for the prediction of continuous values, especially in the case when the input and output variables are related to each other. It focuses on calculating a value closer to the output value. It is employed in weather forecasting etc.

Types of Regression-based algorithm:: -

  1. Linear Regression
  2. Regression Trees

Unsupervised Learning: -

It is a kind of learning in which the output target is not given to the model while performing the training. It only has the input variables. The model has to lean itself. The trained data that is fed to the system can be unlabeled as well as unstructured in nature.

The unstructured data is the one where the noise or some irrelevant information is present, whereas in case of unlabeled data it does not contain any target value other than the input data and is easy to collect as compared to labeled one in the supervised learning.

understanding- different-types-of-machine- learning-3

Types of Unsupervised Learning:-

understanding- different-types-of-machine- learning-4

Clustering is a technique in which the model groups the data based on different patterns, whereas in the case of association; it is a rule-based technique. It is used to find out the relevant relationship between the parameters of a dataset.

Types of Unsupervised based learning algorithms: -

  1. DBSCAN
  2. Hierarchical Clustering
  3. K-means clustering

Reinforcement based Learning: -

In this type of learning, the agent connects with the environment and searches for the best outcome. It is a hit and trial method. Based on the result, the author may be either rewarded or penalized for every wrong and correct answer. The more the positive rewards points gained, the more the model can train itself. And the prediction is made after getting trained entirely. 

understanding- different-types-of-machine- learning-5