Category Archives: Machine Learning

Digital Healthcare Technology & Innovations: Examples

digital health technology and innovations

Digital healthcare technology is making waves in the medical community. It has the potential to change the way we approach healthcare, and it is already starting to revolutionize the way patients are treated. In this blog post, we will explore some of the most exciting digital healthcare technologies including AI / machine learning & blockchain based applications, initiatives and innovations. We will also take a look at some real-world examples of how these technologies are being used to improve patient care. Digital health refers to the use of digital technology to improve the delivery of healthcare services. Connected health (also known as i-health) is a term that encompasses all digital …

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Posted in AI, BlockChain, digital transformation, Healthcare, Machine Learning. Tagged with , , .

Scatter plot Matplotlib Python Example

Scatter Plot representing Two Classes

If you’re a data scientist, data analyst or a Python programmer, data visualization is key part of your job. And what better way to visualize all that juicy data than with a scatter plot? Matplotlib is your trusty Python library for creating charts and graphs, and in this blog we’ll show you how to use it to create beautiful scatter plots using examples and with the help of Matplotlib library. So dig into your data set, get coding, and see what insights you can uncover!  What is a Scatter Plot? A scatter plot is a type of data visualization that is used to show the relationship between two variables. Scatter …

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Why AI & Machine Learning Projects Fail?

AI and machine learning solution approaches

AI / Machine Learning and data science projects are becoming increasingly popular for businesses of all sizes. Every organization is trying to leverage AI to further automate their business processes and gain competitive edge by delivering innovative solutions to their customers. However, many of these AI & machine learning projects fail due to various different reasons. In this blog post, we will discuss some of the reasons why AI / Machine Learning / Data Science projects fail, and how you can avoid them. The following are some of the reasons why AI / Machine learning projects fail: Lack of understanding of business problems / opportunities Ineffective solution design approaches Lack …

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Posted in AI, Data Science, Machine Learning.

Weight Decay in Machine Learning: Concepts

Weight decay machine learning neural networks

Weight decay is a popular technique in machine learning that helps to improve the accuracy of predictions. In this post, we’ll take a closer look at what weight decay is and how it works. We’ll also discuss some of the benefits of using weight decay and explore some possible applications. As data scientists, it is important to learn about concepts of weight decay as it helps in building machine learning models having higher generalization performance. Stay tuned! What is weight decay and how does it work? Weight decay is a regularization technique that is used to regularize the size of the weights of certain parameters in machine learning models. Weight …

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Posted in Data Science, Machine Learning. Tagged with , .

K-Fold Cross Validation – Python Example

K-Fold Cross Validation Concepts with Python and Sklearn Code Example

In this post, you will learn about K-fold Cross-Validation concepts with Python code examples. K-fold cross-validation is a data splitting technique that can be implemented with k > 1 folds. K-Fold Cross Validation is also known as k-cross, k-fold cross-validation, k-fold CV, and k-folds. The k-fold cross-validation technique can be implemented easily using Python with scikit learn (Sklearn) package which provides an easy way to calculate k-fold cross-validation models.  It is important to learn the concepts of cross-validation concepts in order to perform model tuning with the end goal to choose a model which has a high generalization performance. As a data scientist / machine learning Engineer, you must have a good …

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Model Complexity & Overfitting in Machine Learning

model complexity vs model overfitting vs model accuracy

In machine learning, model complexity and overfitting are related in a manner that the model overfitting is a problem that can occur when a model is too complex due to different reasons. This can cause the model to fit the noise in the data rather than the underlying pattern. As a result, the model will perform poorly when applied to new and unseen data. In this blog post, we will discuss what model complexity is and how you can avoid overfitting in your machine learning models by handling the model complexity. As data scientists, it is of utmost importance to understand the concepts related to model complexity and how it …

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Activation Functions in Neural Networks: Concepts

The activation functions are critical to understanding neural networks. It is important to use the activation function in order to train the neural network. There are many activation functions available for data scientists to choose from, so it can be difficult to choose which activation function will work best for their needs. In this blog post, we look at different activation functions and provide examples of when they should be used in different types of neural networks. If you are starting on deep learning and wanted to know about different types of activation functions, you may want to bookmark this page for quicker access in the future. What are activation …

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Posted in Deep Learning, Machine Learning. Tagged with , , .

How to Identify AI / Machine Learning Use Cases

identify ai and machine learning use cases

As AI and machine learning solutions and technologies continue to evolve, more and more businesses are looking for ways to incorporate them into their operations to realize a greater business impact. But with so many potential applications, it can be difficult to know where to start. In this blog post, we’ll outline some tips for identifying AI and machine learning use cases. We’ll also provide a few examples of how AI & machine learning can be used in business settings. So if you’re thinking about adding AI or machine learning to your toolkit, read on! This blog post will be appropriate for product managers, business analysts, data science architects, data …

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Softmax Regression Explained with Python Example

In this post, you will learn about the concepts of what is Softmax regression/function with Python code examples and why do we need them? As data scientist/machine learning enthusiasts, it is very important to understand the concepts of Softmax regression as it helps in understanding the algorithms such as neural networks, multinomial logistic regression, etc in a better manner. Note that the Softmax function is used in various multiclass classification machine learning algorithms such as multinomial logistic regression (thus, also called softmax regression), neural networks, etc. Before getting into the concepts of softmax regression, let’s understand what is softmax function. What’s Softmax function? Simply speaking, the Softmax function converts raw …

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Information Theory, Machine Learning & Cross-Entropy Loss

information theory - machine learning

 What is information theory? How is information theory related to machine learning? These are some of the questions that we will answer in this blog post. Information theory is the study of how much information is present in the signals or data we receive from our environment. AI / Machine learning (ML) is about extracting interesting representations/information from data which are then used for building the models. Thus, information theory fundamentals are key to processing information while building machine learning models. In this blog post, we will provide examples of information theory concepts and entropy concepts so that you can better understand them. We will also discuss how concepts of …

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Posted in Information Theory, Machine Learning. Tagged with , .

Cross Entropy Loss Explained with Python Examples

In this post, you will learn the concepts related to the cross-entropy loss function along with Python code examples and which machine learning algorithms use the cross-entropy loss function as an objective function for training the models. Cross-entropy loss is used as a loss function for models which predict the probability value as output (probability distribution as output). Logistic regression is one such algorithm whose output is a probability distribution. You may want to check out the details on how cross-entropy loss is related to information theory and entropy concepts – Information theory & machine learning: Concepts What’s Cross-Entropy Loss? The cross-entropy loss function is an optimization function that is …

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Linear Regression Explained with Python Examples

SSR, SSE and SST Representation in relation to Linear Regression

In this post, you will learn about concepts of linear regression along with Python Sklearn examples for training linear regression models. Linear regression belongs to class of parametric models and used to train supervised models.  The following topics are covered in this post: Introduction to linear regression Linear regression concepts / terminologies Linear regression python code example Introduction to Linear Regression Linear regression is a machine learning algorithm used to predict the value of continuous response variables. The predictive analytics problems that are solved using linear regression models are called supervised learning problems as it requires that the value of response/target variables must be present and used for training the models. Also, recall that …

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Posted in Data Science, Machine Learning, Python. Tagged with , , , .

Mean Squared Error or R-Squared – Which one to use?

Mean Squared Error Representation

In this post, you will learn about the concepts of the mean-squared error (MSE) and R-squared, the difference between them, and which one to use when evaluating the linear regression models. You also learn Python examples to understand the concepts in a better manner What is Mean Squared Error (MSE)? The Mean squared error (MSE) represents the error of the estimator or predictive model created based on the given set of observations in the sample. Intuitively, the MSE is used to measure the quality of the model based on the predictions made on the entire training dataset vis-a-vis the true label/output value. In other words, it can be used to …

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Linear Regression Explained with Real Life Example

Multiple linear regression example

In this post, the linear regression concept in machine learning is explained with multiple real-life examples. Both types of regression models (simple/univariate and multiple/multivariate linear regression) are taken up for sighting examples. In case you are a machine learning or data science beginner, you may find this post helpful enough. You may also want to check a detailed post on what is machine learning – What is Machine Learning? Concepts & Examples. What is Linear Regression? Linear regression is a machine learning concept that is used to build or train the models (mathematical models or equations)  for solving supervised learning problems related to predicting continuous numerical value. Supervised learning problems …

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Posted in AI, Data Science, Machine Learning. Tagged with , , , .

Tensor Broadcasting Explained with Examples

In this post, you will learn about the concepts of Tensor Broadcasting with the help of Python Numpy examples. Recall that Tensor is defined as the container of data (primarily numerical) most fundamental data structure used in Keras and Tensorflow. You may want to check out a related article on Tensor – Tensor explained with Python Numpy examples. Broadcasting of tensor is borrowed from Numpy broadcasting. Broadcasting is a technique used for performing arithmetic operations between Numpy arrays / Tensors having different shapes. In this technique, the following is done: As a first step, expand one or both arrays by copying elements appropriately so that after this transformation, the two tensors have the …

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Posted in Data Science, Deep Learning, Machine Learning, Python. Tagged with , , , , .

Regularization in Machine Learning: Concepts & Examples

In machine learning, regularization is a technique used to avoid overfitting. This occurs when a model learns the training data too well and therefore performs poorly on new data. Regularization helps to reduce overfitting by adding constraints to the model-building process. As data scientists, it is of utmost importance that we learn thoroughly about the regularization concepts to build better machine learning models. In this blog post, we will discuss the concept of regularization and provide examples of how it can be used in practice. What is regularization and how does it work? Regularization in machine learning represents strategies that are used to reduce the generalization or test error of …

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Posted in Data Science, Deep Learning, Machine Learning. Tagged with , .