Tag Archives: Data Science

Amazon (AWS) Machine Learning / AI Services List

amazon machine learning services

Last updated: 30th Jan, 2024 Amazon Web Services (AWS) is a cloud computing platform that offers machine learning as one of its many services. AWS has been around for over 10 years and has helped data scientists leverage the Amazon AWS cloud to train machine learning models. AWS provides an easy-to-use interface that helps data scientists build, test, and deploy their machine learning models with ease. AWS also provides access to pre-trained machine learning models so you can start building your model without having to spend time training it first! You can get greater details on AWS machine learning services, data science use cases, and other aspects in this book – …

Continue reading

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

Machine Learning Lifecycle Example: From Data to Deployment

Machine Learning Lifecycle

Last updated: 27th Jan 2024 In this blog, we get an overview of the machine learning lifecycle, from initial data handling to the deployment and iterative improvement of ML models. You might want to check out this book for greater insights into machine learning (ML) concepts – Machine Learning Interviews. The following is the diagram representing the machine learning lifecycle while showcasing three key stages such as preparing data, ML development, and ML deployment. These three stages are explained later in this blog. Stage A: Preparing Data Preparing data for training machine learning models involves collecting data, constructing data pipelines for preprocessing, and refining the data to prepare it for …

Continue reading

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

Generalization Errors in Machine Learning: Python Examples

Generalization Errors in Machine Learning

Last updated: 21st Jan, 2024 Machine Learning (ML) models are designed to make predictions or decisions based on data. However, a common challenge, data scientists face when developing these models is ensuring that they generalize well to new, unseen data. Generalization refers to a model’s ability to perform accurately on new, unseen examples after being trained on a limited set of data. When models don’t generalize well, they commit errors. These errors are called generalization errors. In this blog, you will learn about different types of generalization errors, with examples, and walk through a simple Python demonstration to illustrate these concepts. Types of Generalization Errors Generalization errors in machine learning …

Continue reading

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

Demystifying Encoder Decoder Architecture & Neural Network

encoder decoder architecture

In the field of AI / machine learning, the encoder-decoder architecture is a widely-used framework for developing neural networks that can perform natural language processing (NLP) tasks such as language translation, text summarization, and question-answering systems, etc which require sequence-to-sequence modeling. This architecture involves a two-stage process where the input data is first encoded (using what is called an encoder) into a fixed-length numerical representation, which is then decoded (using a decoder) to produce an output that matches the desired format. In this blog, we will explore the inner workings of the encoder-decoder architecture, how it can be used to solve real-world problems, and some of the latest developments in …

Continue reading

Posted in Deep Learning, Generative AI, Machine Learning, NLP. Tagged with , , , .

Bag of Words in NLP & Machine Learning: Examples

Bag of words technique to convert to numerical feature vector

Last updated: 6th Jan, 2024 Most machine learning algorithms require numerical input for training the models. Bag of words (BoW) effectively converts text data into numerical feature vectors, making it compatible with a wide range of machine learning algorithms, from linear classifiers like logistic regression to complex ones like neural networks. In this post, you will learn about the concepts of bag-of-words model and how to train a text classification model using Python Sklearn. Some of the most common text classification problems includes sentiment analysis, spam filtering etc. In these problems, one can apply bag-of-words technique to train machine learning models for text classification. It will be good to understand the …

Continue reading

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

Natural Language Processing (NLP) Task Examples

natural language processing tasks examples

Last updated: 5th Jan, 2024 Have you ever wondered how your phone’s voice assistant understands your commands and responds appropriately? Or how search engines are able to provide relevant results for your queries? The answer lies in Natural Language Processing (NLP), a subfield of artificial intelligence (AI) that focuses on enabling machines to understand and process human language.  NLP is becoming increasingly important in today’s world as more and more businesses are adopting AI-powered solutions to improve customer experiences, automate manual tasks, and gain insights from large volumes of textual data. With recent advancements in AI technology, it is now possible to use pre-trained language models such as ChatGPT to …

Continue reading

Posted in Data Science, NLP. Tagged with , .

Cohen Kappa Score Explained: Formula, Example

Cohen Kappa Score Confusion Matrix

Last updated: 5th Jan, 2024 Cohen’s Kappa Score is a statistic used to measure the performance of machine learning classification models. In this blog post, we will discuss what Cohen’s Kappa Score is and Python code example representing how to calculate Kappa score using Python. We will also provide a code example so that you can see how it works! What is Cohen’s Kappa Score or Coefficient? Cohen’s Kappa Score, also known as the Kappa Coefficient, is a statistical measure of inter-rater agreement for categorical data. Cohen’s Kappa Coefficient is named after statistician Jacob Cohen, who developed the metric in 1960.   It is generally used in situations where there …

Continue reading

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

Validation Techniques for Machine Learning Models: Examples

Last updated: 4th Jan, 2024 In the realm of machine learning, the emphasis increasingly shifts towards solving real-world problems with high-quality models. Creating high performant models does not not just depend on raw computational power or theoretical knowledge, but crucially on the ability to systematically conduct and learn from a myriad of different models by trying with hypothesis and related experiments including different algorithms, datasets / features, hyperparameters, etc. This is where the importance of a robust validation strategy and related techniques becomes paramount. Validation techniques, in essence, are the methodologies employed to accurately assess a model’s errors and to gauge how its performance fluctuates with different experiments. The primary …

Continue reading

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

K-Fold Cross Validation in Machine Learning – Python Example

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

Last updated: 3rd Jan, 2024 In this post, you will learn about K-fold Cross-Validation concepts used while training machine learning models with the help of 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 implement training of k-fold cross-validation models.  It is important to learn the concepts of k-fold cross-validation concepts in order to perform model tuning with the end goal to choose a model which has …

Continue reading

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

Machine Learning Definition, Examples, Method, Types

Machine Learning Modeling Workflow

Last updated: 3rd Jan, 2024 Machine learning is a machine’s ability to learn from data. It has been around for decades, but machine learning is now being applied in nearly every industry and job function. In this blog post, we’ll cover a detailed introduction to what is machine learning (ML) including different definitions. We will also learn about different types of machine learning tasks, algorithms, etc along with real-world examples. What is machine learning & how does it work? Definition 1: Simply speaking, machine learning can be defined as an approach to model our beliefs about real-world events. For example, let’s say a person came to a doctor with a …

Continue reading

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

Machine Learning Models Solution Design: Examples

Solution Design for Machine Learning Models - Examples

This blog is crafted for data scientists, machine learning (ML) and software engineers, business analysts / product managers, and anyone involved in the ML project lifecycle, aiming to create a reliable solution design and development strategy / plan for successful AI / machine learning project implementation and value realization. The blog revolves around a series of critical solution design questions, meticulously curated to guide teams from the initial conception of a project to its final deployment and beyond. By addressing each of these solution design questions, teams can ensure that they are not only building a model that is technically proficient but also one that aligns seamlessly with business objectives, …

Continue reading

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

Micro-average, Macro-average, Weighting: Precision, Recall, F1-Score

Last updated: 30th Dec, 2023 In this post, you will learn about how to use micro-averaging and macro-averaging methods for evaluating scoring metrics (precision, recall, f1-score) for multi-class classification machine learning problem. You will also learn about weighting method used as one of the other averaging choices of metrics such as precision, recall and f1-score for multi-class classification problem. The concepts will be explained with Python code examples.  What & Why of Micro, Macro-averaging and Weighting metrics? Micro and macro-averaging methods are used in the evaluation of classification models, to compute performance metrics like precision, recall, and F1-score. These methods are especially relevant in scenarios involving multi-class or multi-label classification. In case of multi-class classification, …

Continue reading

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

ROC Curve & AUC Explained with Python Examples

Last updated: 29th Dec, 2023 Confusion among data scientists regarding ROC Curve and AUC often stems from misunderstanding their relationship. The ROC Curve visualizes true positive vs false positive rates at various thresholds, while AUC quantifies the overall ability of a model to discriminate between classes, with higher values indicating better performance. In this post, you will learn about ROC Curve and AUC concepts along with related concepts such as True positive and false positive rate with the help of Python examples. It is very important to learn ROC, AUC and related concepts as it helps in selecting the most appropriate machine learning classification models based on the model performance.  What is …

Continue reading

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

Accuracy, Precision, Recall & F1-Score – Python Examples

Last updated: 29th Dec, 2023 Classification models are used in classification problems to predict the target class of the data sample. The classification machine learning models predicts the probability that each instance belongs to one class or another. It is important to evaluate the performance of the classifications model in order to reliably use these models in production for solving real-world problems. The performance metrics include accuracy, precision, recall, and F1-score. Because it helps us understand the strengths and limitations of these models when making predictions in new situations, model performance is essential for machine learning. The most common question asked is what is accuracy, precision, recall and f1 score? In …

Continue reading

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

MSE vs RMSE vs MAE vs MAPE vs R-Squared: When to Use?

Regression models evaluation metrics MSE RMSE MAE MAPE R-Squared

Last updated: 29th Dec, 2023 As data scientists, we navigate a sea of metrics to evaluate the performance of our regression models. Understanding these metrics – Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-Squared – is crucial for robust model evaluation and selection. In this blog, we delve into the intricacies of these different metrics while learning them based on clear definitions, formulas, and guidance on when to use which of these metrics. Different Types of Regression Models Evaluation Metrics The following are different types of regression models evaluation metrics including MSE, RMSE, MAE, MAPE, R-squared and Adjusted …

Continue reading

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

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

Mean Squared Error Representation

Last updated: 29th Dec, 2023 As you embark on your journey to understand and evaluate the performance of regression models, it’s crucial to know when to use each of these metrics and what they reveal about your model’s accuracy. In this post, you will learn about the concepts of the mean-squared error (MSE) and R-squared (R2), the difference between them, and which one to use when evaluating the linear regression models. Note that MSE is very closely related to root mean squared error (RMSE) which is also discussed in this blog. You also learn Python examples to understand the concepts in a better manner. For learning the differences between other …

Continue reading

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