Different Types of Machine Learning Algorithms

Having a clear understanding of the different types of machine learning algorithms is crucial for the success of a machine learning project. Each algorithm has its own strengths, weaknesses, and areas of applicability. Understanding these differences helps to select the most appropriate ML algorithm for a given problem and avoid common mistakes.

Gradient Descent Algorithm in Machine Learning

Gradient descent in machine learning is a basic cost function optimization algorithm. In this blog, we have discussed: 1) Limitations of computations in machine learning. 2) What are optimization algorithms, and why do we need them? 3) What is the problem with multiple minima in the cost function? 4) What is gradient descent and how does it work?

KNN Algorithm in Machine Learning

K-Nearest Neighbor is a supervised learning algorithm that can be used to solve classification and regression problems. This algorithm learns without explicitly mapping input variables to the target variables. It is probably the first machine learning algorithm, and due to its simplicity, it is still accepted in solving many industrial problems.

Feature Selection Techniques in ML

The best machine learninmodel would have the lowest number of features involved in the analysis keeping the performance high. Therefore, determining the relevant features for the model building phase is necessary. In this session, we will see some feature selection methods and discuss the pros and cons of each.

Regularization in Machine Learning

Regularization is the technique that is used to solve the problem of overfitting in machine learning. In this blog, we have discussed famous machine learning concepts like underfitting, overfitting, accurate fitting, regularization and how it cures overfitting. Mathematical logic behind regularization and difference between L1 and L2 regularization.

Evaluation Metrics for Classification Models in ML

Classification problems are among the most used problem statements in machine learning. We evaluate classification models using standard evaluation metrics like confusion matrix, accuracy, precision, recall, ROC and the AUC curves. In this article, we will discuss all these popular evaluation metrics to evaluate the classification models along with their inbuilt functions present in Scikit-learn.

Email Spam and Non-spam Detection using ML

In recent years, the quantity of spam emails has decreased significantly due to spam detection and filtering software. In this, We have covered these concepts: 1) Methods to segregate incoming emails into the spam or non-spam categories? 2) Steps to implement a spam classifier using the k-NN algorithm. 3) Real-life use case of Gmail, Outlook, and Yahoo.

Evaluation Metrics for Regression Models

We evaluate the performance of our regression models in Machine learning using standard metrics. In this article, we will be discussing all the standard evaluation metrics namely MAE, MAPE, MSE, RMSE, R-Squared and Adjusted R-Squared, for regression models used to compare two different models on the same dataset.

PUBG Cheater Detection Project Using ML

To detect whether player is genuine or false, BGMI (PUBG) uses a state-of-the-art machine learning approach to predict the presence of cheaters. It collects players' data, draws meaningful results, and categorizes cheaters into separate categories. They use a supervised learning approach to predict the occurrence of impossible events.

Wine Quality Prediction Using Machine Learning

Prediction of wine quality can be made easy with machine learning and data science techniques. In this blog, we have discussed: 1) Why do we need a machine learning model for wine quality assessment? 2) Factors that affect wine quality 3) Various ML models to predict wine quality 4) Implementation of predicting wine quality using k-NN regressor

Loss and Cost Function in Machine Learning

Optimization of error function is the respiratory process for machine learning algorithms. But this error function varies for classification and regression problems. In this blog, we have discussed: 1) Definition and importance of loss function 2) Loss functions used for regression 3) Loss functions used for binary classification 4) Loss functions used for multiple classification, etc.

Step by Step Guide to Get Started with Machine Learning

In this blog, we have discussed steps to master machine learning for implementing applications. Here are the steps: 1) Learn python 2) Learn math for ML 3) Learn concept of data, analysis, manipulation, filtering, and visualization and choose any open source dataset 5) Learn basics of ML and choose problem statement 6) Learn deep learning and neural networks.

Cost Function and Learning Process in Machine Learning

As humans, we learn through various methods such as practice, study, experiences, discussions, etc. On the other side, modern computers use machine learning to simulate human intelligence. So obvious curiosity for us to know how exactly a machine learns something. In this blog, we'll dive into the complete learning process of computers via machine learning.

t-SNE Algorithm in Machine Learning

t-SNE (t-distributed stochastic neighbor embedding) is an unsupervised non-linear dimensionality reduction algorithm used for exploring high-dimensional data. In this blog, we have discussed: What is t-SNE, difference between t-SNE and PCA in dimensionality reduction, step-wise working of t-SNE algorithm, t-SNE python implementation and Mathematical analysis of t-SNE algorithm.

XG-Boost Algorithm in Machine Learning

Boosting algorithms are popular in machine learning community. In this blog, we will discuss XGBoost, also known as extreme gradient boosting. This is a supervised learning technique that uses an ensemble approach based on the gradient boosting algorithm. It is a scalable end-to-end system widely used by data scientists.

Random Forest in Machine Learning

Random forest is a supervised learning algorithm in machine learning and belongs to the CART family (classification and Regression trees). It is popularly applied in data science projects and real-life applications to provide intuitive and heuristic solutions. This article will give you a good understanding of how Random Forest algorithm works.

AdaBoost and Gradient Boosting in ML

In machine learning, Boosting is an approach where we sequentially ensemble the predictions made by multiple decision trees. In this blog, we have discussed: 1) What is Bagging and Boosting? 3) Pseudocode for boosting 4) Hyperparameters for Boosting algorithms 4) Variants of boosting algorithms like AdaBoost and Gradient Boost, etc.

Top Challenges Faced by Machine Learning Beginners

Many learners want to master machine learning and don’t know where to start. It seems like a formidable task, especially if one lacks a thorough background. This article will discuss some of the factors that can be obstacles in learning machine learning. Working around these obstacles can help us to develop a long-term interest in this subject.

Naive Bayes Classifier in Machine Learning

Naive Bayes is a popular supervised machine learning algorithm that predicts the categorical target variables. This algorithm makes some silly assumptions while making any predictions. But the most exciting thing is: It still performs better or is equivalent to the best algorithms. So let's learn about this algorithm in greater detail.

K-Means Clustering Algorithm in ML

Clustering is a powerful unsupervised learning technique that involves grouping similar data points together into subgroups or clusters. One of the most widely used clustering algorithms in machine learning is the k-means algorithm, which separates data into k distinct clusters based on pre-defined criteria. In this article, we provide a detailed, step-by-step explanation of how k-means works, and explore popular methods like the elbow method and average silhouette method for determining the optimal value of k in k-means. To illustrate our points, we also demonstrate how to implement k-means on the IRIS dataset using Python.

Univariate, Bivariate, and Multivariate Analysis

Exploratory data analysis can be classified as Univariate, Bivariate, and Multivariate analysis. Univariate refers to the analysis involving a single variable; Bivariate refers to the analysis between two variables, and Multivariate refers to the statistical procedure for analyzing the data involving more than two variables.

Data Processing in Machine Learning

Nowadays, data collection is one of the most common trends, and every company collects data for various uses. When they record any form of data, it comes with multiple impurities. So data preprocessing techniques are used to remove impurities from data and make it useful for training machine learning models.

Principal Component Analysis (PCA) in Machine Learning

Principle component analysis (PCA) is an unsupervised learning technique to reduce data dimensionality consisting of interrelated attributes. The PCA algorithm transforms data attributes into a newer set of attributes called principal components (PCs). In this blog, we will discuss the dimensionality reduction method and steps to implement the PCA algorithm.

Decision Tree Algorithm in Machine Learning

Decision tree algorithm in machine learning is a hierarchical breakdown of a dataset from root to leaf nodes based on attributes to solve a classification or regression problem. They are non-parametric supervised learning algorithms that predict a target variable's value. We have discussed various decision tree implementations with python.

Structured Data Pre-processing Hands-on

Companies are collecting tons of data, and the need for processed data is increasing. In this blog, we will do hands-on on several data preprocessing techniques in machine learning, like feature selection, feature quality assessment, feature sampling, and feature reduction. We will use different datasets for demonstrating data preprocessing methods.

Sentiment Analysis Using Machine Learning

In this blog, you will learn classification of movie reviews into positive and negative review categories using sentiment analysis. We have also discussed: 1) Use case of sentiment analysis? 2) Data analysis for the IMDB movie review dataset 3) Steps of text or data processing, including tokenization, lemmatization, word embedding, etc.

Top Misconceptions in Machine Learning

We sometimes overemphasize the capabilities of machine learning. Here are some popular misconceptions: 1) ML, DL, and AI are the same 2) Machine learning can predict the future 3) Machine learning can solve any problem 4) One machine learning algorithm will be sufficient 5) More features in the data, better will be the machine learning model, etc.

Life Expectancy Prediction Using Machine Learning

Using machine learning, we can predict the life expectancy of a person. In this blog, we will explore parameters affecting the lifespan of individuals living in different countries and learn how life span can be estimated with the help of machine learning models. We will also focus on the application of linear regression in predicting life expectancy.

Personality Prediction Project Using Machine Learning

Machine learning can predict personalities based on social media usage. This is highly used in dating apps and recommendation systems. In this blog, we have discussed: 1) How personality prediction is useful? 2) Big five personality trait model 3) How ML predicts personality based on social media behavior? 4) Steps to implement personality predictor.

Why DSA Important for Machine Learning?

In this blog, we have explained the top five reasons to learn data structures and algorithms for data science and machine learning. Some popular DSA concepts used in machine learning are array, vector, matrices, linked list, tree, graph, stack, queue, hashing, sets, dynamic programming, greedy algorithm, randomized algorithms, etc.

Recommender System using Machine Learning

Recommender System is a system that could predict the future preference for a user based on their previous behavior or by focusing on the behavior of similar users. In this blog, we have discussed: 1) What is a recommender system? 2) Their various types 3) Implementation of content-based recommender system 4) Various use cases in industry.

Machine Learning Glossary (A to Z Terms) in ML

This is a glossary of machine learning terms commonly used in the industry. Some popular machine learning terminologies: neural networks, supervised learning, unsupervised learning, reinforcement learning, regularization, classification, regression, clustering, optimizers, outliers, bias, variance, underfitting, overfitting, normalization, etc.

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