Artificial Neural Networks (ANNs), popularly known as Neural Networks, are supervised Machine Learning algorithms capable of solving both classification and regression problems. Here we have discussed the analogy from human brain, terms used to define any neural network, what are the things present in a schematic diagram of ANN, advantages and disadvantages of ANN with respect to other machine learning algorithms and lastly what are the practical use-cases of ANN.

In order to fully grasp the concept of a Neural Network, it is crucial to understand the various components that make up a Neural Network. In this article, we delve into the key components of an Artificial Neural Network, including Neurons, Input Layers, Output Layers, Hidden Layers, Connections, Parameters, Activation Functions, Optimization Algorithms, and Cost Functions. These components work together to solve both classification and regression problems in Machine Learning.

Data Science, the application of scientific methods to extract valuable insights from data, is crucial for businesses to perform various analyses. In this paragraph, we discuss the importance of Data Science, its usage in businesses, the roles and responsibilities of a data scientist, the necessary skills for a career in Data Science, the distinctions between Data Science, Machine Learning, Data Engineering, and Business Analysis, and the challenges faced by data scientists.

Big Data, the stage of data that exceeds the capabilities of traditional storage, analytical and processing methods, requires specialized solutions like the Hadoop framework. In this article, we explore the characteristics, types, examples, advantages, and challenges of Big Data and how Hadoop supports its use-cases.

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.

Scikit-learn is a free machine learning framework available for Python, providing an interface for supervised and unsupervised learning. It is built over the SciPy library and provides every feature catering to every ML requirement. In this blog, we will learn the essential concepts, tools, and features related to Scikit-learn.

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 this article, we will learn about methods used for scaling different attributes present in our data. Normalization and Standardization are two most used techniques available for scaling features and bring them on same range.It avoids the cases of biases towards higher or lower magnitude features.

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.

Linear Regression is a supervised machine learning algorithm used to solve regression problems. In this blog, we have discussed: 1) What is linear regression? 2) Various types 3) The loss function for linear regression 4) Ordinary Least Squares (OLS) method 5) Polynomial regression 6) Python implementation of linear regression.

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.

Customer segmentation in machine learning is about splitting organization's customer base into smaller groups that reflect similarities in their behavior. It helps businesses develop customer-focused strategies, make segment-wise decisions, and maximize customer value. This blog explains cluster analysis for customer segmentation.

Logistic Regression is one of the most used machine learning algorithms. It is a supervised learning algorithm where target variables should be categorical, such as positive or negative, Type A, B, or C, etc. Although the name contains the term "regression", we can also say that it can only solve classification problems.

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.

Both classification and regression in machine learning deal with the problem of mapping a function from input to output. However, when it comes to classification problems, the output is a discrete (non-continuous) class label or categorical output. While on the other hand, when the problem is a regression problem, the output is continuous.

Pandas is a famous library package of Python used by data scientists and analysts for data understanding, data preprocessing, and much more. It provides us with numerous tools to do these manipulations and analysis efficiently. In this blog, We will cover installation and all the basic Pandas functions frequently used while building machine learning projects.

Python is the most preferred language for developing machine learning and data science applications. It has a large community support that can help debug the errors and resolve all the roadblocks appearing while developing any solution. In this blog, we have discussed various data types, expressions, variables and string operations in python.

We sometimes need to execute specific instructions only when some conditions are true. If not, then we will perform a different set of instructions. In this blog, we have discussed: 1) Various comparison operations in Python. 2) What are conditions in python? 3) What is branching? 3) How do we use logical operations to combine the two conditions? etc.

Numpy is considered one of the most used python libraries. In this blog, we have discussed: 1) What is NumPy? 2) Python lists vs. NumPy array 3) Shape, reshaping, squeezing, expanding, slicing and indexing of Numpy arrays 4) Concatenating, stacking, broadcasting of NumPy arrays 5) Mathematical operations on Numpy arrays.

Loops are the set of instructions that needs to be executed until a defined condition is satisfied. In this blog, we have discussed: 1) What is the range function in python? 2) How does the loop work? 3) for loop in python 4) while loop in python 4) How can we make conditional loops in python? 5) Use of Continue and Break statements in a loop.

Functions are a set of instructions grouped in a block and get executed only when it is called inside our program. In python programming, functions follow specific syntaxes to ensure their validity. In this blog, we have discussed: 1) What are functions in python? 2) How to create and call functions? 4) Various function arguments? 5)The anonymous function.

In Python, everything is an object which holds different properties and methods. Class is a blueprint that creates these objects. In this blog, we have explained fundamental oops concepts in python: 1) What are classes and objects? 2) How to use classes and objects? 3) Default classes examples in python 4) Abstraction, Inheritance and Polymorphism.

SVM, also known as support vector machines, is one of the most popular algorithms in machine learning and data science. Experts consider this one of the best "Out of box" classifiers. In this article, we will develop a thorough understanding of SVMs, relating it with SVC and Maximal Margin Classifier along with step-wise implementation.

Seaborn is an open-source library built over Matplotlib and makes plots more appealing and understandable. It works excellently with data frames and pandas libraries. In this blog, we have discussed: 1) Advantages of Seaborn over Matplotlib library, 2) Installation process of Seaborn in Python 3) Various Data Plots using the Seaborn library.

Matplotlib is one of Python's most effective visualization libraries for data visualization. It is an open-source library built over NumPy arrays. In this blog, we have discussed: 1) What is Matplotlib 2) Installation of Matplotlib using PIP 3) What is Pyplot in Matplotlib 4) The subplot in Matplotlib's pyplot module 5) Various plots using Matplotlib.

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.

Every machine learning project needs to go through these steps: Problem finalization, data gathering, data pre-processing, model selection, data splitting, performance evaluation, and model deployment. In this blog, we have discussed: 1) Steps of implementing machine learning projects 2) How to make ML models production-ready?, etc.

Machine learning is the science of getting computers to act without being explicitly programmed. Here computer takes Input Data and Output Data as its input parameters and tries to produce the best suitable function that maps Inputs to Outputs. The machine learns a mapping function that maps the input data to the output using existing experiences.

In python, sets and dictionaries are unordered data structures frequently used in machine learning applications. In this blog, we have explained these concepts: 1) What is set in python? 2) Various operations on sets 3) Conversion of lists into sets 4) What is dictionary python? 5) Various operations on dictionaries? 6) Comparison of sets and dictionaries.

Tuples and lists are popular python data structures. They are also called compound data types because they can store a mixture of primitive data types like strings, ints, and floats. Tuples are ordered sequences of the same or mixed data types enclosed in smaller parentheses. Lists store an ordered sequence of similar or different data type python objects.

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.

In this blog, we will focus on applications of regex by implementing it to some tedious tasks that wouldn’t be possible without regular expressions. Some standard applications of regular expressions in data science: 1) Web scraping and data collection 2) Text preprocessing (NLP) 3) Pattern detection for IDs, e-mails, names 4) Date-time manipulations

A regular expression is an expression that holds a defined search pattern to extract the pattern-specific strings. Today, regular expressions are available for almost every high-level programming language. As data scientists or machine learning engineers, we should know the fundamentals of regular expressions and when to use them.

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.

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.

In machine learning, anomaly detection is a process of finding samples behaving abnormally compared to the majority of samples present in the dataset. Anomaly detection algorithms have important use-cases in data analytics and data science fields. For example, fraud analysts use anomaly detection algorithms to detect fraud transactions.

There are various ways to make our computers machine learning enabled for implementing ML projects. In this blog, we will try one of the most preferred and easy-to-use methods, i.e., Python3 with Sublime Text 3. Python programming is the most preferred language for ML tasks, and sublime text 3 is the code editor to write ML codes.

These days, the support of libraries and frameworks is easily accessible in machine learning. But in this article, we will implement a basic machine learning project without using frameworks like Scikit-learn, Keras, or Pytorch. We will use two python libraries: NumPy library for numerical operations and Matplotlib library to visualize graphs.

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.

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.

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.

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.

Time series forecasting uses statistical models to predict future values using previously recorded observations. It is classified into two parts: 1) Univariate time series forecasting (Involves a single variable) 2) Multivariate time series forecasting (Involves multiple variables). Note: Time Series is a set of observations taken at a specific periodic time.

Time Series Preprocessing techniques have a significant influence on data modeling accuracy. In this blog, we have discussed: 1) Definition of time-series data and its importance. 2) Preprocessing steps for time series data 3) Structuring time-series data, finding the missing values, denoising the features, and finding the outliers present in the dataset.

When we talk about the machine learning model, one question comes to mind: What are the errors associated with that prediction? Bias and Variance are those error-causing elements, and ideas about these errors will help to diagnose the model. Bias, Variance and Bias-Variance tradeoffs are frequently asked questions in machine learning interviews.

Unlike humans, machines don’t understand words and their semantic context. So, we convert processed text into a format that the machine can understand using vector encoding. In this blog, we will learn: 1) Word embedding 2) Techniques to embed words (One-hot encoding, Word2Vec, TF-IDF, etc) 3) Implementation of all these embeddings.

We need to clean the text data before feeding it to machine learning algorithms. Fortunately, Python has excellent support for NLP libraries (NLTK, spaCyto) to ease text analysis. In this blog, we will learn: 1) Real-time working on the sentiment analysis dataset 2) Techniques for cleaning text data. 3) Exploratory analysis of text data.

Artificial intelligence and machine learning are the most famous buzzwords in the technical industries. We generally use them as synonyms, but these tech stacks are different, although machine learning is just a part of artificial intelligence. In this blog, we will discuss the basic comparison between artificial intelligence and machine learning.

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?

To learn a new subject, we should try to know how exactly that started. Every computer science field has a different history, reflecting the challenges that earlier researchers faced and making our journey easy. This article will discuss the 10 most interesting historical facts considered the turning points in AI and Machine Learning history.

Based on the nature of input that we provide to a machine learning algorithm, machine learning can be classified into four major categories: Supervised learning, Unsupervised learning, Semi-supervised learning, and Reinforcement learning. In this blog, we have discussed each of these terms, their relation, and popular real-life applications.

In this blog, we have discussed how exactly machines learn in machine learning. We have solved one common problem of finding the value of straight-line using two different approaches: Using traditional programming and using machine learning approach. We also looked at what information machines will store, which we say is machine learning.

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.

We can classify ML models into 5 categories: 1) Nature of input: supervised, unsupervised, semi-supervised, reinforcement learning 2) Nature of problem: classification, regression, clustering 3) Nature of algorithm: classical ML, neural networks, DL 4) Nature of solution: parametric, non-parametric 5) Nature of output: probabilistic, non-probabilistic.

Machine learning is the science of getting computers to act without being explicitly programmed. In this blog, we have answered these fundamental questions: 1) What is machine learning and how it works? 2) Why do we need machine learning? 3) When did it start? 4) Use cases of machine learning in industry 5) Machine learning vs. Artificial intelligence