Machine Learning tutorials
NumPy is the backbone of numerical computing in Python and is preferred in Data Science due to its efficiency, versatility, and extensive mathematical capabilities. If you want to know how to use NumPy for numerical computations, this article is forā¦
Hey there! Let’s chat about something that’s really shaking things up in healthcare: machine learning (ML). Think of it as a super-smart computer that’s learning how to help doctors diagnose and treat us better. It’s like having a brainy sidekickā¦
When preparing for Machine Learning interviews, it’s crucial to have a good understanding of performance evaluation metrics, as they are key to assessing and improving model performance. So, if you are looking for some challenging interview questions related to performanceā¦
Machine learning is an advanced technology that now has been used for developments of so many fields and industries. Aside from website and app building, some businesses have successfully implemented this technology to improve the quality of their products andā¦
Data science broadly encompasses data analysis, machine learning, data engineering, and statistical modelling. The path you take can vary based on the role you’re aiming for, such as a Data Analyst, Data Scientist, Machine Learning Engineer, or Data Engineer. So,ā¦
E-commerce, short for electronic commerce, refers to the buying and selling of goods and services over the Internet. In E-commerce, businesses set up online platforms (websites or mobile apps) to showcase their products or services. Customers browse through these platforms,ā¦
In a Data Science interview, candidates are often assessed on their knowledge of statistics, as statistics is a fundamental component of data analysis and modelling. If you want to know what kind of interview questions based on statistics you canā¦
In the Finance domain, there are various Data Science jobs for leveraging data-driven insights to make informed financial decisions, manage risks, detect fraud, and optimize investment strategies. If you are interested in the Finance domain and want to know theā¦
The retail domain refers to the sector of the economy that involves the sale of goods and services to consumers for their personal or household use. Retailers purchase products from manufacturers or wholesalers and then sell them to individual consumersā¦
Scikit-learn is a powerful library that provides a wide range of tools for data preprocessing and machine learning. It is built on top of other popular Python libraries like NumPy, SciPy, and Matplotlib, making it an integral part of theā¦
Time Series Forecasting is a statistical technique used to make predictions based on historical time-ordered data points. It’s valuable when dealing with data that changes over time, such as stock prices, sales figures, weather data, or economic indicators. ARIMA andā¦
There are various Data Science roles in the industry. Each role requires you to be an expert in some specific skills to get your first Data Science job. If you want to know what skills you should have for yourā¦
Deep Learning is a subset of Machine Learning that leverages deep neural networks to learn and represent complex patterns and features from data. If you are looking for a job specifically focused on Machine Learning, you should have at leastā¦
Regression metrics are quantitative measures used to evaluate the performance of regression models. They provide information about how well a regression model fits the data and how accurately it predicts the outcome variable. If you want to learn how toā¦
Neural network architectures refer to the structural and organizational designs of artificial neural networks (ANNs). These architectures determine how the network is organized, including the number of layers, the number of neurons in each layer, the connections between neurons, andā¦
Data Science means using data and computer science to solve business problems. There are various problems in Data Science that a Data professional deals with daily. If you want to know about the types of problems we deal with inā¦
Machine Learning means using data and algorithms to build intelligent systems. There are various Machine Learning algorithms used for training models that help in solving problems and building intelligent systems. While learning Machine Learning, many people are often confused aboutā¦
In Machine Learning, Feature Selection is the process of choosing a subset of relevant and significant features (variables or attributes) from features available in a dataset. The goal of selecting features is to improve the performance of a Machine Learningā¦
“Machine Learning Algorithms: Handbook” is a book based on a step by step guide to all Machine Learning algorithms with implementation using Python. In this article, I’ll give you a complete overview of the book so that you can understandā¦
Many people find solving a Data Science problem difficult even after mastering Machine Learning algorithms. The problem is that they focus more on algorithms than skills and the mindset of working with data. Data Science is not all about masteringā¦
Cloud platforms, also known as cloud computing platforms, are comprehensive services provided by technology companies like Amazon, Microsoft, Google, and IBM, among others. These platforms offer a wide range of computing resources, including servers, storage, databases, networking, analytics, Machine Learning,ā¦
Domain knowledge refers to expertise and understanding of a specific field or subject matter. It encompasses the concepts, terminology, practices, and challenges associated with that domain. In Data Science, domain knowledge means a deep understanding of the industry, business, orā¦
Feature Engineering is a critical step in solving a Data Science problem that involves creating or selecting the most relevant and informative features (input variables) from raw data. These features are then used to train machine learning models, so theā¦
In Data Science interviews, technical questions are designed to assess your fundamentals of working with data and your understanding of using Data Science concepts in a given problem. If you want to understand what types of questions are asked inā¦
In Machine Learning, Ridge and Lasso Regression are regularization techniques used in linear regression to prevent overfitting and improve the model’s generalization to unseen data. They work by adding a penalty term to the linear regression loss function. If youā¦
Everyone has their own way of approaching a Data Science problem. If you are a beginner in Data Science, then your way of approaching the problem will develop over time. But there are some steps you should follow to startā¦
Machine Learning means using data and algorithms to build intelligent systems. Machine Learning algorithms are computational procedures that enable machines to learn patterns and relationships from data without being explicitly programmed. These algorithms allow machines to make predictions, decisions, andā¦
While learning Data Science, the decision between pursuing a degree or opting for courses and certifications can significantly impact your career trajectory. Your educational background plays a crucial role in determining the most suitable path for your journey in dataā¦
Hyperparameter tuning is an essential process in Machine Learning that involves optimizing the settings that guide the training of a Machine Learning model. But these settings themselves are not learned from the data. These settings, called hyperparameters, play a vitalā¦
A Web Data ETL (Extract, Transform, Load) pipeline is a systematic process used in data engineering to collect, transform, and load data from various sources on the internet into a structured and usable format for analysis and storage. So, ifā¦
In Data Science, time series refers to a sequence of data points recorded at specific time intervals. Time series data captures observations over time, where each data point corresponds to a particular moment or period. Time series analysis involves uncoveringā¦
In today’s competitive Data Science field, employers prefer candidates who can seamlessly bridge the gap between working with data and domain knowledge. By delving into projects that align with specific industries, you can demonstrate a deep understanding of the domain’sā¦
If you are looking for your first data science job, you need advanced and unique projects mentioned in your resume. A data science professional works with APIs to collect real-time data from company databases and third-party applications. Having at leastā¦
A Music Recommendation System is an application of Data Science that aims to assist users in discovering new and relevant musical content based on their preferences and listening behaviour. Personalized music recommendations have become an essential tool in the digitalā¦
Outliers are data points that deviate significantly from the rest of the data. These data points lie far away from the majority of the data points and can have a substantial impact on statistical analysis and modelling. If you wantā¦
Recommendation systems are one of the popular applications of Data Science in every domain. The recommendation system of Amazon works by using both collaborative filtering and content-based filtering techniques. Amazon uses collaborative filtering to analyze user behaviour and recommend productsā¦
Neural Networks are a type of computational model inspired by the structure and functioning of a human brain. They are composed of interconnected nodes called neurons, organized into layers. Each neuron receives inputs, processes them, and produces an output thatā¦
A Machine Learning Engineer uses data and algorithms to build intelligent systems. If you are new to machine learning, you need to work on beginner-level machine learning projects to understand how to use machine learning algorithms on datasets to solve problems. Ifā¦
Python and R are preferred languages for data science due to their rich ecosystem of libraries, extensive community support, and powerful tools for statistical analysis, data manipulation, machine learning, and visualization. You can choose either Python or R for Dataā¦
Data Engineering focuses on the foundational aspects of managing and processing data, providing the necessary infrastructure and pipelines to support Machine Learning and Analytics workflows. It ensures that data is reliable, accessible, and processed efficiently to enable data-driven decision-making andā¦
NumPy, which stands for Numerical Python, is a powerful library in Python providing support for large, multi-dimensional arrays and matrices, along with a vast collection of mathematical functions to operate on these arrays. It serves as a fundamental building blockā¦
There are various Data Science tools used in the industry. The usage of different data science tools across companies and professions stems from the diverse nature of their data, objectives, and roles within the industry. Companies, ranging from small startups to largeā¦
Data preprocessing is a critical step in data science tasks, ensuring that raw data is transformed into a clean, organized, and structured format suitable for analysis. A data preprocessing pipeline streamlines this complex process by automating a series of steps, enabling dataā¦
In Machine Learning, Scaling and Normalization are techniques used in data preprocessing to transform features or variables in a dataset. These techniques ensure that the data is in an appropriate range and distribution, which facilitates efficient training of Machine Learningā¦
In Machine Learning, Polynomial Regression is a regression algorithm used to model nonlinear relationships between input features and output labels. So, if you are new to Machine Learning and want to know how the Polynomial Regression algorithm works, this articleā¦
Support Vector Machine (SVM) is a popular supervised learning algorithm used for classification and regression problems in Machine Learning. The basic idea behind SVM is to find a decision boundary that separates data points of different classes with the maximumā¦
Content-based filtering and collaborative filtering are two common approaches used in recommendation systems. A Content-based recommendation system uses information about the recommended item, while a collaborative system uses user behaviour data. So, if you want to know how these approachesā¦
In Machine Learning, Decision Tree is an algorithm that works like a flowchart or tree structure to make decisions based on input data. It starts with a question or condition and then follows different branches based on the answers toā¦
As Data Science beginners, sometimes we need to find datasets to work on projects. And as Data Science professionals, we sometimes need to find synthetic data based on the problem we are working on. So, if you want to knowā¦
In Machine Learning, Naive Bayes is an algorithm that uses probabilities to make predictions. It is used for classification problems, where the goal is to predict the class an input belongs to. So, if you are new to Machine Learningā¦
Data-driven business models are the types of business models in which data is used for overall business operations. Most big tech product companies you hear about today have data-driven business models. So, if you want to understand what Data-Driven business modelsā¦
In Machine Learning, Logistic Regression is a statistical model used for binary classification problems. It is used to predict the probability of an outcome based on the input features. It uses a sigmoid function to map the input features toā¦
The first step to getting a job today is to create and upload your resume. The way you used to create a resume some years ago is very different from the way companies want you to. So, if you wantā¦
In Machine Learning, Linear Regression Algorithm is a statistical technique for calculating the value of a dependent variable based on the value of an independent variable. The goal of linear regression is to find the best-fit line that describes theā¦
Developing Data ETL pipelines is one of the most valuable skills for Data Engineers. Data ETL is a process where data is extracted from a place, then the data is transformed in some way, and then data is loaded intoā¦
There are many types of questions you are asked for a job interview to understand your behaviour, technical skills, domain expertise, and how you will use your skills in solving real-time business problems. If you are looking for some ofā¦
There is a huge competition in the Data Science field, making it hard to mention unique projects on your resume. An interviewer goes through hundreds of projects every day, so it’s essential to mention unique projects on your resume soā¦
Your LinkedIn profile is like an online resume. Today, companies do have a look at your LinkedIn profile before hiring you, especially in the tech industry. So, if you want to know how to create a LinkedIn profile to getā¦
A Data Engineer is a data professional who collects, transforms, and process data according to the need of the business. Data Engineering is one of the highest-paying jobs in Data Science. So, if you are looking for a roadmap toā¦
The decision Tree algorithm is a supervised Machine Learning algorithm used for both classification and regression problems. It is based on a sequential decision process. If you want to know everything about the Decision Tree algorithm, this article is forā¦
As a Data Science beginner, you need to work on various projects to get your desired job. Working on projects is valuable because your projects are the highlights of your resume when you are a fresher. Once you get experienced,ā¦
One of the reasons behind the success of many businesses is how they use the data generated by their business and customers in decision-making and improving their products and services. So, if you want to know how companies worldwide useā¦
In Machine Learning, we use data and algorithms to build intelligent systems. In the next ten years, you will explore many new high-paying jobs that require you to know about Machine Learning. So the time you will spend today learningā¦
Data Science is one of the highest-paying career options today. There are many jobs in the Data Science field you can prepare for as a fresher or an experienced professional. So, if you want to know about the highest-paying careerā¦
Like all other jobs, the salary of aĀ Machine LearningĀ Engineer depends on many factors like work experience, living standards in the country, and demand for Machine Learning Engineers in the country. Out of all factors, work experience is the most importantā¦
Grammarly is one of the best examples of applications that use Machine Learning for their business. Grammarly helps write a text for any purpose and uses Machine Learning to make that text more readable and engaging. So, if you wantā¦
Kaggle is a Data Science community owned by Google. You will find thousands of datasets and case studies on Kaggle to improve your Data Science skills. So, if you are looking for some case studies to practice your Data Scienceā¦
Data Scientists and Data Engineers are Data Science professionals with some skills in common and different responsibilities. If you don’t know how a Data Scientist differs from a Data Engineer, this article is for you. In this article, I willā¦
Data Scientists and Machine Learning Engineers are Data Science professionals with different skillset and responsibilities. So, if you are confused about how a Data Scientist differs from a Machine Learning Engineer, this article is for you. In this article, Iā¦
In Machine Learning, we use data and algorithms to build intelligent systems. If you are preparing for a job based on Machine Learning and confused about what kind of projects you should work on to mention in your resume, thisā¦
In Machine Learning, we use data and algorithms to build intelligent systems. It doesn’t matter if you want to become a Machine Learning Engineer, Data Scientist, or Data Analyst. If you want any job in the Data Science field, you needā¦
Regression analysis is a machine learning technique used to predict continuous values. Once you have trained a regression model to predict Continuous values based on the problem you are solving, it is valuable to evaluate the model’s performance. So, ifā¦
Data Science is one of the highest-paid career options today. If you want to learn Data Science, you don’t need to spend a lot as there are so many free resources on the internet to learn Data Science. So, ifā¦
In Machine Learning, we use data and algorithms to build intelligent systems. It doesn’t matter if you want to become a Machine Learning Engineer, Data Scientist, or Data Analyst. If you want any job in the Data Science field, youā¦
A Data Engineer is a Data Science professional who collects, transforms, and process data according to the need of the business. Data Engineering is one of the most in-demand Data Science jobs. So, if you are aiming for the positionā¦
Apple is one of the most loved smartphone brands worldwide. Apple’s iPhone is known for productivity, user experience, and privacy. There are a lot of areas where Apple uses Machine Learning to improve the overall user experience of an iPhone.ā¦
Deep Learning is a subset of Machine Learning where we need a large amount of data to train powerful models capable of finding relationships in the data to solve complex problems. So, if you are looking for courses that willā¦
In Machine Learning, the concept of model evaluation means determining the performance of a Machine Learning model. To evaluate the performance of a Machine Learning model, we need to use model evaluation metrics. So, if you want to learn classificationā¦
In Machine Learning, we use data and algorithms to build intelligent systems. There are a lot of free resources on the internet you can follow to learn Machine Learning. But some people like to learn from courses to learn everythingā¦
Time Series Forecasting is the process of analyzing and modeling time-series data. It helps in forecasting the future behaviour of the market, which is helpful in the decision-making for every business. Some of the applications of Time Series Forecasting areā¦
Salary is one of the most valuable factors in choosing a career. No one would like to prepare for a job that doesn’t pay well. So, if you are preparing for the role of a Data Science professional and wantā¦
Data science is one of the highest paying career options today. There are many jobs in this field, some of the most popular of which are Data Scientist, Data Analyst, Data Engineer, and Machine Learning Engineer. If you know youā¦
The new MacBooks with the M1 processor provide so many specifications that are enough for every Data Science and Machine Learning professional. But if you have just purchased a MacBook, you have to create a virtual environment to prepare itā¦
Working on a data science project is one of the best ways of showing your skills in working with data. If you are a data science student, your university will ask you to submit a report on your data science project inā¦
TensorFlow is one of the most powerful frameworks for Machine Learning. It was created by Google and used by companies like Airbnb, Spotify, and Google. So if you are looking for the best resources to learn TensorFlow, this article isā¦
Data Science is one of the highest-paid career options today. It is so popular that you can learn everything about data science without spending any money. But some people like to choose a paid course over free resources as weā¦
In Machine Learning, we use data and algorithms to build intelligent systems. If you are new to machine learning and looking for a certification or course to learn and get certified in machine learning, this article is for you. Inā¦
In Machine Learning, we use data and algorithms to build intelligent systems. If you are new to machine learning, you need to work on beginner-level machine learning projects to understand how to use machine learning algorithms on datasets to solveā¦
Data Science certifications are all about certifying whether a person knows how to work with data or not. Although if you have a good list of projects in your portfolio, you don’t need any data science certification. Having a certificationā¦
Many beginners in data science are often confused about what job role they should prepare for. People who are starting to learn data science have either an engineering background, a statistical background, or a non-computer background. Each person has theirā¦
Many data science beginners can solve any data science problem by following a tutorial, but they often struggle to solve a new data science problem. It’s not easy for beginners to start solving a problem when they see a newā¦
If you are a Data Science student, you must have found it hard to choose a topic for creating your first data science project as a beginner. Some universities give a list that you can choose from, while some universitiesā¦
In machine learning, we use data and algorithms to build intelligent systems. Companies use machine learning to make their systems and applications smart enough to give a better user experience to the customers. As machine learning is a popular skill,ā¦
Data science is one of the highest-paying career options in this data-driven world. Every company now makes decisions based on the data generated by its business. A company, therefore, needs skilled data science professionals to turn raw data into actionableā¦
Machine Learning is a branch of Artificial Intelligence where we use data and algorithms to build intelligent systems. It is one of the highest-paid skills in the industry today. If you want to learn machine learning, you should know thatā¦
If you are new to machine learning, you have to choose a programming language to learn machine learning and implement it on data. Python is one of the most preferred programming languages for machine learning. So if you want toā¦
Variance, covariance, and correlation are statistical measures for finding the relationship between data points in a dataset. If you are learning data science, you need to understand these terms. So if you want to learn more about variance, covariance, andā¦
Whenever you have seen a roadmap to learn data science, you must have found people often highlighting Python or R. Python and R are the most popular programming languages among the data science community. But just having the practical knowledgeā¦
Text classification is a machine learning technique used to classify texts based on predefined labels. For example, classifying languages based on a dataset containing different languages with predefined labels. Text classification is used in various problems of natural language processingā¦
You must have heard about some machine learning algorithms before, but most beginners often get confused about how many machine learning algorithms they need to learn when they see professionals using algorithms (specifically in Kaggle) that they havenāt heard aboutā¦
If you’ve never worked as a data science professional before, you must be wondering how to prepare for your first data science interview. So if you’ve learned data science, worked on projects, and think you’re ready for your first dataā¦
Natural Language Processing (NLP) is a subset of machine learning in which we aim to train computers to understand human languages. The chatbot you see in a banking app, Siri on iPhone, or Google translator are examples of natural languageā¦
Time series analysis is one of the most important topics in data science. Time series data is a sequence of data points collected and indexed based on an interval of time, and when we analyze such data to find patternsā¦
A neural network is a computational structure that connects an input layer to an output layer. This computational structure is used in training deep learning models that can easily outperform any classical machine learning algorithm. As a data science beginner,ā¦
A machine learning algorithm is used to find relationships between features and labels. Features are the independent variables that we feed into an algorithm to train a machine learning model, and labels are the dependent variables that we aim toā¦
The activation function is a function used in neural networks to calculate the weighted sum of inputs and biases, which is used to determine whether a neuron should be activated or not. You must have heard a lot about activationā¦
One of the mistakes data science newbies make while working on a machine learning project is that they train a machine learning model, check its accuracy score, and then complete the project. They do not test the performance of theā¦
A data scientist or machine learning engineer often needs the power of GPU to perform powerful calculations using deep neural network architectures. As a data science professional, you will have access to powerful laptops by your company itself, but asā¦
A data scientist has to spend a lot of time preparing a dataset for any data science task because the data we get has a lot of errors, and sometimes it is not labeled. Adding labels to a dataset isā¦
A glossary is a list of words with their meanings about a specific subject or topic. When learning machine learning, you go through a lot of terms that are not easy to remember, but if you have a machine learningā¦
Machine learning algorithms are a series of steps used to find the relationship between labels and features of a dataset. Machine learning algorithms play an important role in building applications and models based on machine learning. There are so manyā¦
There are many real-life use cases to create your unique machine learning projects. If you’re still struggling to work on an actual use case, find something practical and unique, like a machine learning project where you’ll show a comparison ofā¦
If you find it difficult to work on machine learning projects as a beginner, it will be good for you to break the entire process of a machine learning project into small steps. This will help you focus on allā¦
There are so many business problems where a company needs good coders to solve their problems with their coding skills. To identify whether someone is a good coder or not, your projects are the only work that can show yourā¦
A machine learning model is a file that is trained to identify multiple relationships in a dataset. Usually, we train a model using a machine learning algorithm and use it for further predictions. Many powerful machine learning models are trainedā¦
Mathematics is one of the prerequisites that most data science enthusiasts fear to get into machine learning. Without learning math, it will be very difficult for you to create your algorithms although it is possible to learn machine learning ifā¦
There are so many applications in your smartphone that uses machine learning somewhere to provide a better user experience. Social Media applications are such applications that are found in every smartphone. So to understand the use of machine learning nothingā¦
Passive Aggressive Regression belongs to the category of online learning in machine learning. It is not one of the most commonly used machine learning algorithms, but it can nonetheless be used to achieve efficient results that solve regression-based problems. So,ā¦
Whenever we are looking for hotels for vacation or travel, we always prefer a hotel known for its services. The best way to find out whether a hotel is right for you or not is to find out what peopleā¦
In machine learning, accuracy is one of the most important performance evaluation metrics for a classification model. The mathematical formula for calculating the accuracy of a machine learning model is 1 – (Number of misclassified samples / Total number ofā¦
Fake news is one of the biggest problems with online social media and even some news sites. Most of the time, we see a lot of fake news about politics. So using machine learning for fake news detection is aā¦
Stochastic gradient descent is a machine learning algorithm that is used to minimize a cost function by iterating a weight update based on the gradients. If you’ve never used the SGD classification algorithm before, this article is for you. Inā¦
Detecting spam alerts in emails and messages is one of the main applications that every big tech company tries to improve for its customers. Apple’s official messaging app and Google’s Gmail are great examples of such applications where spam detectionā¦
One of the common tasks in natural language processing is sentiment analysis. If you want to work on a project based on natural language processing, sentiment analysis will be a good choice. Before you start a project on sentiment analysis,ā¦
In machine learning, variance is the difference between the actual samples of the dataset and the predictions made by the model. When working on a regression-based machine learning problem, it is very useful to know how much of the varianceā¦
There are so many courses available today based on machine learning because the use of machine learning in the industry is increasing rapidly. I’ve seen a lot of courses that are expensive but still don’t include all of the importantā¦
The R2 score is one of the performance evaluation measures for regression-based machine learning models. It is also known as the coefficient of determination. If you want to learn how to evaluate the performance of a machine learning model usingā¦
Microsoft is today one of the largest technology companies with more than 163,000 employees worldwide. It is known for the Windows operating system which is one of the most popular computer operating systems. If you want to learn how toā¦
It is very important to work on as many machine learning projects as possible to land your first job as a Data Scientist or Machine Learning Engineer. When you show up for your interview, you should have end-to-end machine learningā¦
Lazy Predict is a Python library designed to compare the performance of various machine learning models on a dataset. If you don’t know how to select an algorithm when training a machine learning model, this article is for you. Inā¦
Gender detection is one of the popular computer vision applications. When you use a camera to detect a person’s gender instead of detecting it on a picture, it can be said to be a real-time gender detection system. So, ifā¦
t-SNE is a very powerful machine learning algorithm that can be used to visualize a high-dimensional dataset also in two-dimensional figures. It stands for t-Distributed Stochastic Neighbor Embedding. If you want to learn more about t-SNE and how to visualizeā¦
Computer vision is a field of artificial intelligence that means giving a computer system the ability to see and analyze images just like humans. The FaceId in iPhone is one of the popular examples of computer vision applications. So ifā¦
The classification of social media ads is all about analyzing the ads for classifying whether your target audience will buy the product or not. It’s a great use case for data science in marketing. So, if you want to learnā¦
Working on data science projects will help you improve your problem-solving skills and a good collection of data science projects will strengthen your portfolio which will leave a strong positive impact on your profile as a data scientist. So ifā¦
When we want to train a machine learning model, it is always recommended to divide the dataset into training and test sets if your dataset is large. If you are interested in learning how to split a dataset into trainingā¦
Amazon Alexa is a cloud-based voice service developed by Amazon that allows customers to interact with technology. There are currently over 40 million Alexa users around the world, so analyzing user sentiments about Alexa will be a good data scienceā¦
In machine learning, we use data to predict the future course of action, but how does it add value to a company? If you are learning machine learning and don’t know how companies use machine learning, this article is forā¦
Independent Component Analysis (ICA) is one of the alternatives of PCA that is used to find the underlying factors or components from a multivariate statistical dataset. This is different from a standard PCA because it looks for components that areā¦
There are many concepts in machine learning in which a beginner should be perfect. One of these concepts is the training of a machine learning model. So if you’ve never trained a machine learning model before, this article is forā¦
Almost every business, big or small, today uses machine learning somewhere in their applications. Today, every business is in dire need of hiring machine learning experts, regardless of their educational background. So if you want to become an expert inā¦
We need to convert an image to an array to use it for any kind of data science task where we need to understand the features of an image. Converting images to an array is as easy as converting textā¦
You must have used a neural network for training a model on your data. There are so many types of architectures of neural networks that you can use to train a model, but have you ever visualized the architecture ofā¦
WhatsApp is a great source of data to analyze many patterns and relationships between two or more people chatting personally or even in groups. If you want to know how we can analyze the sentiments of a WhatsApp chat, thisā¦
Non-Negative Matrix Factorization (NNMF) is a group of machine learning algorithms used in multivariate analysis and linear algebra. It is used in place of PCA when the dataset is composed of non-negative items. If you don’t know anything about theā¦
You’ll find plenty of machine learning projects and tutorials on the internet, but only a few of them focus on end-to-end model deployment. So, if you want to learn how to build an end-to-end machine learning model, then this articleā¦
AutoKeras is a Keras-based machine learning framework. It was developed by DATA Lab at Texas A&M University to provide deep learning for everyone. Simply put, it is an automatic machine learning framework for deep learning. If you want to learnā¦
A lot of people are often confused between machine learning and automation. These two areas are very different from each other. If you’re one of those people who doesn’t know how machine learning and automation are different, this article isā¦
Sentiment analysis is the classification of a customer’s reviews or comments as positive, negative, and sometimes neutral also. Most businesses analyze their customers’ feelings about their products or services to find out what their customers want from them. Google playā¦
When working on a data science task, sometimes many missing values act as a hindrance while getting the correct information from a dataset. We can easily remove the missing values, but sometimes we need to fill these values depending onā¦
Most beginners don’t know how to predict values using a machine learning model. What they do is train a model and check its accuracy and they end the task. But before you complete it, you should test how the modelā¦
Video game sales analysis is a popular problem statement on Kaggle. You can work on this problem to analyze the sales of more than 16,500 games or you can also train a machine learning model for forecasting video game sales.ā¦
Working on machine learning projects can make you an expert in machine learning. As a beginner, you only work on projects that are very common in the data science community. But if you want to become an expert, you haveā¦
While solving problems based on classification with machine learning, we mostly find datasets made up of categorical labels that cannot be processed by all machine learning algorithms. So, if you want to learn how to handle categorical data in machineā¦
Dogecoin is the reason for the recent drop in bitcoin prices. The price of Dogecoin is currently very cheap compared to bitcoin, but some financial experts, including Tesla’s CEO Elon Musk, claiming that we will see a rise in theā¦
Perceptron is one of the simplest architecture of Artificial Neural Networks in Machine Learning. It was invented by Frank Rosenblatt in 1957. In this article, I will take you through an introduction to Perceptron in Machine Learning and its implementationā¦
Currency exchange is one of the biggest financial markets. Currently, 1 United States dollar is equivalent to 73.02 Indian rupees. Many factors affect exchange rates such as economic, political and even psychological factors. The prediction of the currency exchange rateā¦
A neural network is a subset of machine learning that mimics the workings of a human brain while solving a complex problem based on deep learning. Neural networks are inspired by neurons found in the human brain. In this article,ā¦
Forecasting sales is a difficult problem for every type of business, but it helps determine where a business should spend more on advertising and where it should cut spending. In this article, I will walk you through the task ofā¦
When working on regression-based machine learning problems, sometimes we find that several independent features correlate not only with the dependent features but also with each other. This is what multicollinearity means. In this article, I’ll take you through an introductionā¦
Kernel PCA is one of the variations of principal component analysis in machine learning that uses kernel methods to perform the initially linear operations of principal component analysis. In this article, I will take you through an introduction to Kernelā¦
Gender Detection is based on the applications of computer vision. There are so many computer vision libraries in Python that we can use for the task of recognizing the gender of a human being. In this article, I will takeā¦
Whenever we want to solve the same problem on a new dataset with the same features that we solved before then we can use the same machine learning model that we trained earlier. But to use the same model againā¦
We usually train a machine learning model to produce a single value or a single label as the output. But when the number of output classes is greater than one, this is the problem of multiclass classification. One-vs-all and One-vs-oneā¦
The performance of a machine learning algorithm on a particular dataset often depends on whether the features of the dataset satisfies the assumptions of that machine learning algorithm. Not all machine learning algorithms have assumptions this is why all algorithmsā¦
An end-to-end machine learning project means building a machine learning application that takes input at the start and provides a solution at the end based on the user input. In this article, I’m going to introduce you to some ofā¦
Principal Component Analysis (PCA) is a dimensionality reduction algorithm used to reduce the dimensionality of a dataset. Sparse PCA is one variation of PCA that can exploit the natural sparsity of data while extracting the principal components. In this article,ā¦
In unsupervised machine learning, the training data is not labelled and here you have to find clusters to detect the similarities between different data points. In this article, I’m going to introduce you to all the unsupervised machine learning algorithmsā¦
Apple’s new M1 chipset offers a powerful processor for every task. It has the most advanced neural engine which offers up to 11 times better performance for machine learning compared to the older MacBooks. So, is the new MacBook M1ā¦
TextBlob is a Python library that can be used to process textual data. Some of the tasks where it is good to use are sentiment analysis, tokenization, spelling correction, and many other natural language processing tasks. In this article, I’llā¦
In machine learning, customer segmentation is based on the problem of clustering which means finding clusters in a dataset with the same features. Customer segmentation can help a business focus on marketing strategies to increase profits and overall customer satisfaction.ā¦
The use of big datasets in machine learning has grown exponentially since the first release of Apache Hadoop. Big Data has played a major role in the use of machine learning in applications such as mass clustering and collaborative filtering.ā¦
Polynomial regression is a machine learning algorithm that is used to train a linear model on non-linear data. Sometimes your data is much more complex than a straight line, in such cases, it is not a good option to trainā¦
In Machine Learning, binary classification consists of distinguishing two classes. For example, classifying messages as spam or not as spam. We have a bunch of machine learning algorithms for the binary classification task, so to help you choose the bestā¦
A company should always set a goal that should be achievable, otherwise, employees will not be able to work to their best potential if they find that the goal set by the company is unachievable. The task of profit predictionā¦
Unsupervised learning is based on problems where there is no supervisor to explain to you about the data unlike supervised learning, i.e. when the data is not labelled. When the problems are based on how you can group items basedā¦
In machine learning, supervised learning is characterized by the concept of a supervisor whose goal is to provide an accurate measure of error that is directly comparable to the output values. If you don’t know in what kinds of problemsā¦
A lot of computer science students have C++ as their main programming language but when they want to start with machine learning they learn languages like Python or R. So is C++ not a good language for machine learning? Yes,ā¦
When using a machine learning algorithm, it is very important to train the model on a dataset with almost the same number of samples. This is known as a balanced class. We need to have balanced classes to train aā¦
Working on some machine learning projects is one of the most useful strategies you can use as a beginner to learn machine learning. As a beginner, you are out of ideas about how to start with a project or whichā¦
There are so many machine learning algorithms for all machine learning problems like classification, regression, and clustering. You may be familiar with more than 3-4 algorithms for each task, but the problem every newbie faces is how to select aā¦
Classification means categorizing something from a group based on some features. For example, spam detection is one of the most popular tasks of classification in the machine learning community, here we need to classify emails as spam or not spam.ā¦
AutoTS is an automatic machine learning library in Python which is developed for the task of automatic time series forecasting. You can use this library for any task of time series forecasting such as predicting the stock prices for theā¦
In graduate studies, many students find it difficult to achieve good grades because they do not get much support in higher education courses compared to the support that students receive in schools. We can use machine learning for the studentā¦
A recommendation system is one such data science application that is used by almost all companies based on products and services on their website and software applications. In this article, I will introduce you to 4 data science projects onā¦
A lot of people often get confused between the job roles of Data Scientists and Machine Learning Engineers. Both these professions are concerned with working with data and machine learning algorithms, so whatās the difference between them? In this article,ā¦
There are so many resources on the internet to learn machine learning in the form of courses, videos, blogs, and books. Choosing the best resources to learn machine learning from the available resources is a difficult task for any newbie.ā¦
In Machine Learning, the AUC and ROC curve is used to measure the performance of a classification model by plotting the rate of true positives and the rate of false positives. In this article, I will walk you through aā¦
Real-time machine learning projects are the type of projects where a user can interact and get real-time output based on user actions rather than running the program over and over after each action. In this article, I will introduce youā¦
Scikit-learn is one of the most useful Python libraries for machine learning. All the concepts that we study about machine learning theoretically can be implemented by using the Scikit-learn library in Python. In this article, I will take you throughā¦
When a machine learning model performs very well on training data, but poorly on test data, it is referred to as overfitting. Likewise, if the model is performing poorly in both training and testing datasets, it is referred to asā¦
Ted talks are a good source to learn and take inspiration from. These days every platform is having a recommendation system to provide a better user experience. Most of the applications collect data to recommend similar content according to theā¦
In Machine Learning, we sometimes use models trained and developed by other developers for some complex tasks. These models are known as pre-trained model. It is not a bad idea to use a pre-trained model for solving a similar problemā¦
Tesla is an amAmericanlectric vehicle company whose aim is to accelerate the world’s transition towards sustainable energy. A few days back the rise in the stock prices of Tesla made Elon Musk the richest person in the world. Predicting stockā¦
The demand for data scientists and machine learning engineers has led to great competition for your first job in data science. It is believed that someone with a very good knowledge of the fundamentals of data science is more likelyā¦
Recommendation Systems are one of the widely used applications of Data Science in most companies based on products and online services. Amazon is a great example of such companies. Being an online shopping website Amazon needs to generate personalised recommendationsā¦
Natural Language Processing or NLP is the field of Artificial Intelligence that empowers a computer system to read and understand human languages. In this article, I will take you through a complete roadmap on how to learn Natural Language Processing.ā¦
An End to End machine learning model follows the complete lifecycle of a machine learning model which means to first collect data, then prepare the data according to the patterns found in the dataset, then train and evaluate the modelā¦
Streamlit is an open-source Python framework used to deploy machine learning models in beautiful web applications in just a few lines of code. You don’t need any knowledge of web development to deploy machine learning models with this framework. Inā¦
After training and evaluating a machine learning model, the next step is to deploy the model. As a Data Scientist, you don’t need to build a complete software or web application to deploy your machine learning models, but you stillā¦
Natural Language Processing (NLP) is the field of Artificial Intelligence which means to train a computer system or any machine to understand human languages. NLTK and spaCy are two of the most used Python packages for the tasks of NLP.ā¦
The way birds inspired humans to create an aeroplane the same way a human’s brain inspired humans to build intelligent machines. Neural networks were inspired by biological neurons found in the brain of a human. In this article, I willā¦
The BIRCH is a Clustering algorithm in machine learning. It stands for Balanced Reducing and Clustering using Hierarchies. In this article, I will take you through the concept of BIRCH Clustering in Machine Learning and its implementation using Python. BIRCHā¦
In Machine Learning, classification means predicting the class of data points. In simple words, when we are predicting the category of the target label using machine learning algorithms then this is known as classification. There are mainly 3 types ofā¦
Bankruptcy is a state of insolvency when a business or legal person cannot repay debts to creditors. Bankruptcy is primarily imposed by court order when initiated by the debtor. In this article, I will walk you through how to trainā¦
Sentiment Analysis is an application of natural language processing that is used to understand people’s opinions. Today, many companies use real-time sentiment analysis by asking users about their service. In this article, I’ll walk you through real-time sentiment analysis usingā¦
Semi-supervised learning is a combination of supervised and unsupervised machine learning. The issues here are based on datasets where the dataset will contain many unlabeled instances and a few labelled instances. In this article, I will take you through whatā¦
PyCaret is an open-source machine learning library that helps automate the entire process of training a machine learning model. From model selection to training and testing, PyCaret is a great tool that can be used in machine learning. In thisā¦
The price of a product is the most important attribute of marketing that product. One of those products where price matters a lot is a smartphone because it comes with a lot of features so that a company thinks aā¦
Linear and logistic regression are excellent statistical techniques used as machine learning algorithms to understand the relationship between features and labels in machine learning. In this article, I will take you through the difference between linear regression and logistic regressionā¦
Feature Scaling means resizing features so that no feature dominates other features. In machine learning, we use the concept of feature scaling to make sure that all the features we use to train a machine learning model are at aā¦
Cosine similarity is a method used in building machine learning applications such as recommender systems. It is a technique to find the similarities between the two documents. In this article, I’ll give you an introduction to Cosine Similarity in Machineā¦
In Machine Learning, clustering involves identifying similar instances and then assigning them to similar clusters or groups of instances. It is an unsupervised machine learning technique. In this article, I’ll give you an introduction to the most popular clustering algorithmsā¦
In machine learning, the mean squared error (MSE) is used to evaluate the performance of a regression model. In regression models, the RMSE is used as a metric to measure model performance and the MSE score is used to evaluateā¦
Machine learning is one of those areas that every business invests in today. The complete lifecycle of a machine learning application begins with data collection and ends with model deployment. In this article, I’ll walk you through what model deploymentā¦
Machine learning helps a business solve complex problems where we don’t program an application but train it so that it can learn to react in different kinds of situations. In this article, I’m going to introduce you to some machineā¦
Clustering is used to divide data into subsets, and classification is used to create a predictive model that can be used to categorize the values of future data points. In this article, I’ll walk you through the difference between clusteringā¦
The principal component analysis (PCA) is a dimensionality reduction algorithm. This is one of the easiest and most intuitive ways to reduce the dimensions of a dataset. In this article, I will walk you through the Principal Component Analysis inā¦
A human can express his emotions in any form, such as face, gestures, speech and text. The detection of text emotions is a content-based classification problem. In this article, I will take you through how to solve the problem ofā¦
Dimensionality reduction is used to reduce the dimensions of a data set to speed up a subsequent machine learning algorithm. It removes noise and redundant features, which improves the performance of the algorithm. In this article, I will introduce youā¦
The K-Means Clustering is a clustering algorithm capable of clustering an unlabeled dataset quickly and efficiently in just a very few iterations. In this article, I will take you through the K-Means clustering in machine learning using Python. K-Means Clusteringā¦
When first starting with machine learning, a beginner faces most of the problems associated with choosing a programming language. As a computer science student, we learn languages like C++ and Java which are the best for obtaining placements. But forā¦
Machine Learning Pipelines helps in automating the process of the lifecycle of a machine learning model. It automates the lifecycle of data validation, preprocessing, training and deployment on a new dataset. In this article, I will take you through Machineā¦
Collaborative filtering is a recommendation system method that is formed by the collaboration of multiple users. The idea behind it is to recommend products or services to a user that their peers have appreciated. In this article, I will introduceā¦
Gradient Descent is an optimization algorithm used to train a machine learning model differently. It is best suited for problems where there are a large number of features and too many samples to fit in the memory of a machineā¦
Passive Aggressive Classifier belongs to the category of online learning algorithms in machine learning. It works by responding as passive for correct classifications and responding as aggressive for any miscalculation. In this article, I will walk you through what Passiveā¦
Most recommendation systems use content-based filtering and collaborative filtering to show recommendations to the user to provide a better user experience. Content-based filtering generates recommendations based on a user’s behaviour. In this article, I will walk you through what content-basedā¦
