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.

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.

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?

It is critical to explore the complete process of machine learning and understand how exactly a machine learns something. In this blog, we will discuss: 1) How do we check the intelligence of any machine? 2) What is the cost function? 3) Steps involved in the learning process for machine learning algorithms? 3) How does a machine store its learnings? etc.

t-SNE (t-distributed stochastic neighbor embedding) is a non-linear dimensionality reduction algorithm used for exploring high-dimensional data. In this blog, we have discussed: 1) What is t-SNE? 2) t-SNE vs PCA 3) How t-SNE algorithm works? 4) Concept of similarity? 5) Python implementation of t-SNE 6) Mathematical analysis of t-SNE algorithm.

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 is a supervised learning algorithm in machine learning that can be used to solve both classifications and regression problems. 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 this algorithm works.

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.

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.

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.

Classification problems are among the most used problem statements in machine learning. We evaluate classification models with available models using standard evaluation metrics like confusion matrix, accuracy, precision, recall, ROC. In this article, we will discuss popular evaluation metrics used to evaluate the classification models.

When we build a solution for any regression problem, we compare its performance with the existing work using standard metrics, like measuring distance in meters, plot size in square feet, etc. Similarly, we need some standard evaluation metrics to evaluate two regression models. Some of them are MAE, MSE, RMSE, and R-Squared.

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-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.

The clustering technique is prevalent in many fields, so many algorithms exist to perform it. K-means is one of them! K-means is an unsupervised learning technique used to partition the data into predefined K distinct and non-overlapping partitions. These partitions are called clusters, and the value of K depends upon the user's choice.

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.

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.

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.

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 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.

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.

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.

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

Regularization is the technique that is used to solve the problem of overfitting in machine learning. In this blog, we have discussed: 1) What are underfitting, overfitting, and accurate fitting? 2) What is regularization? 3) How does regularization cure overfitting?4) Mathematical logic behind regularization 5)What are L1 and L2 regularization?

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.

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.

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.

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 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.

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.

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.