1. Introduction in Machine Learning

Machine Learning: Machine Learning is a method of teaching machines/computers to make a prediction based on some data and experien...

2. Data Preprocessing

Data Preprocessing: Data Prepossessing is the first stage of building a machine learning model. It involves transforming raw data...

3. Regression

Regression: Basically, Regression is a statistical approach to find the correlations between variables(dependent and independent)....

4. Simple Linear Regression

Simple Linear Regression: It is a linear regression model that uses two-dimensional sample points with one independent and one dep...

5. Multiple Linear Regression

In this tutorial, we are going to understand the Multiple Linear Regression Algorithm and implement the algorithm with Python.Mult...

6. Polynomial Regression

Polynomial Regression: Polynomial regression is a form of regression analysis in which the relationship between the independent va...

7. Support Vector Regression

Support Vector Regression: Support Vector Regression(SVR) is quite different than other Regression models. It uses the Support Vec...

8. Decision Tree Regression

Decision Tree Regression: This Regression is based on the decision tree structure. A decision tree is a form of a tree or hierarch...

9. Random Forest Regression

Random Forest Regression: The basic idea behind Random Forest is that it combines multiple decision trees to determine the final o...

10. Evaluating Regression Models performance

The performance of a regression model can be understood by knowing the error rate of the predictions made by the model. You can al...

11. Classification

Classification: Classification is machine learning task of predicting the value of a categorical variable(target or class). This i...

12. Logistic Regression

In this article, I will go you through the logistic regression, a simple classification algorithm. Then we will implement the algo...

13. K-Nearest Neighbor

In this tutorial, I am going to explain to you the K-Nearest Neighbor(KNN) algorithm and how to implement this algorithm in Python...

14. Support Vector Machine

In this tutorial, we will learn the Support Vector Machine algorithm and implement it in Python. Support Vector Machine: Support...

15. Kernel SVM

In this tutorial, we are going to introduce to the Kernel Support Vector Machine and how to implement in Python. Kernel SVM Intui...

16. Naive Bayes Classification

In this tutorial, we are going to learn the intuition behind the Naive Bayes classification algorithm and implement it in Python....

17. Decision Tree Classification

In this article, we are going to understand the concept of Decision Tree algorithm for classification and then we will implement i...

18. Random forest Classification

Random Forest is an ensemble learning technique. It builds a number of decision trees on the randomly selected data sample. Then i...

19. Evaluating Classification Model performance

Through this post, you are going to understand different metrics for the evaluation of classification models. The Basics: False P...

20. K-means Clustering

Clustering: Clustering is an unsupervised learning algorithm. A cluster refers to groups of aggregated data points because of cert...

21. Hierarchical Clustering

Clustering: Clustering is an unsupervised learning algorithm. A cluster refers to groups of aggregated data points because of cert...

22. Association Rule Learning | Apriori

In this tutorial, we are going to understand the association rule learning and implement the Apriori algorithm in Python.Associati...

23. Eclat Intuition

Eclat Intuition: Today, we are talking about the Eclat model. It is similar to the a priori algorithm. Here, we actually talking a...

24. Reinforcement Learning

Reinforcement Learning: Reinforcement Learning is a branch of Machine Learning, also called Online Learning. It is used to solve i...

25. Upper Confidence Bound (UCB)

In this tutorial, I will explain to you the application of Upper Confidence Bound(UCB) algorithm to solve the Multi Bandit problem...

26. Thompson Sampling Intuition

In this article, we will talk about the Thompson Sampling Algorithm for solving the multi-armed bandit problem and implement the a...

27. Natural Language Processing

Natural Language Processing: Natural language processing (NLP) is a field of artificial intelligence concerned with the interactio...

28. Deep Learning

Deep Learning: Deep Learning is an artificial intelligence function that imitates the workings of the human brain in processing da...

29. Artificial Neural Networks

In this article, we are going to learn and implement an Artificial Neural Network(ANN) in Python.Artificial Neural Network: An art...

30. Principal Component Analysis

Principal Component Analysis (PCA): Principle Component Analysis or PCA is a popular dimensionality reduction technique that...

31. Linear Discriminant Analysis (LDA)

Linear Discriminant Analysis (LDA): Linear Discriminant Analysis(LDA) is a dimensionality reduction technique, that separates the...

32. Kernel PCA

Kernel Principal Component Analysis(Kernel PCA): Principal component analysis (PCA) is a popular tool for dimensionality reduction...

33. Model Selection & Boosting

Model Selection & Boosting: Model Selection is the undertaking of choosing a statistical model from an arrangement of candidat...

34. K-fold Cross Validation

In this tutorial, we are going to learn the K-fold cross-validation technique and implement it in Python. While building machine l...

35. XGBoost

XGBoost in Python Step 1: First of all, we have to install the XGBoost. Now, we need to implement the classification problem. In t...

36. Convolution Neural Network

Convolution Neural Network: A Convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural ne...

37. Dimensionality Reduction