Category Archives: Machine Learning
NLP: Huggingface Transformers Code Examples
Do you want to build cutting-edge NLP models? Have you heard of Huggingface Transformers? Huggingface Transformers is a popular open-source library for NLP, which provides pre-trained machine learning models and tools to build custom NLP models. These models are based on Transformers architecture, which has revolutionized the field of NLP by enabling state-of-the-art performance on a range of NLP tasks. In this blog post, I will provide Python code examples for using Huggingface Transformers for various NLP tasks such as text classification (sentiment analysis), named entity recognition, question answering, text summarization, and text generation. I used Google Colab for testing my code. Before getting started, get set up with transformers …
Sklearn Algorithms Cheat Sheet with Examples
The Sklearn library, short for Scikit-learn, is one of the most popular and widely-used libraries for machine learning in Python. It offers a comprehensive set of tools for data analysis, preprocessing, model selection, and evaluation. As a beginner data scientist, it can be overwhelming to navigate the various algorithms and functions within Sklearn. This is where the Sklearn Algorithms Cheat Sheet comes in handy. This cheat sheet provides a quick reference guide for beginners to easily understand and select the appropriate algorithm for their specific task. In this cheat sheet, I have compiled a list of common supervised and unsupervised learning algorithms, along with their Sklearn classes and example use …
Supervised & Unsupervised Learning Difference
Supervised and unsupervised learning are two different common types of machine learning tasks that are used to solve many different types of business problems. Supervised learning uses training data with labels to create supervised models, which can be used to predict outcomes for future datasets. Unsupervised learning is a type of machine learning task where the training data is not labeled or categorized in any way. For beginner data scientists, it is very important to get a good understanding of the difference between supervised and unsupervised learning. In this post, we will discuss how supervised and unsupervised algorithms work and what is difference between them. You may want to check …
Machine Learning: Inference & Prediction Difference
In machine learning, prediction and inference are two different concepts. Prediction is the process of using a model to make a prediction about something that is yet to happen. The inference is the process of evaluating the relationship between the predictor and response variables. In this blog post, you will learn about the differences between prediction and inference with the help of examples. Before getting into the details related to inference & prediction, let’s quickly recall the machine learning basic concepts. What is machine learning and how is it related with inference & prediction? Machine learning is about learning an approximate function that can be used to predict the value …
Sklearn Neural Network Example – MLPRegressor
Are you interested in using neural networks to solve complex regression problems, but not sure where to start? Sklearn’s MLPRegressor can help you get started with building neural network models for regression tasks. While the packages from Keras, Tensorflow or PyTorch are powerful and widely used in deep learning, Sklearn’s MLPRegressor is still an excellent choice for building neural network models for regression tasks when you are starting on. Recall that Python Sklearn library is one of the most popular machine learning libraries, and it provides a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more. In this blog post, we will be focusing on training a …
Neural Networks Interview Questions – Quiz #45
Are you preparing for a job interview in the field of deep learning or neural networks? If so, you’re likely aware of how complex and technical these topics can be. In order to help you prepare, we’ve put together a list of common neural network interview questions and answers in form of multiple-choice quiz. The quiz in this blog post covers basic concepts related to neural network layers, perceptron, multilayer perceptron, activation functions, feedforward networks, backpropagation, and more. We’ve included 15 multiple-choice questions, as well as 5 additional questions specifically focused on the backpropagation algorithm. I will be posting many more quizzes on the neural networks in time to come, …
Google’s Free Machine Learning Courses: Learn from the Best
Machine learning has become a fundamental part of almost every industry today. With the increasing demand for data scientists and machine learning engineers, it has become imperative for professionals to keep themselves updated with the latest tools and techniques. Fortunately, Google offers a range of free machine learning courses that cater to professionals of all expertise levels. In this blog, we will explore the top Google machine learning courses that will help learners enhance their skills and stay ahead of the game. List of Free Machine Learning Courses by Google The following is a list of free machine learning courses from Google which you can take online. These courses can …
Lung Disease Prediction using Machine Learning
Lung diseases, including chronic obstructive pulmonary disease (COPD), are a leading cause of death worldwide. Early detection and treatment are critical for improving patient outcomes, but diagnosing lung diseases can be challenging. Machine learning (ML) models are transforming the field of pulmonology by enabling faster and more accurate prediction of lung diseases including COPD. In this blog, we’ll discuss the challenges of detecting / predicting lung diseases using machine learning, the clinical dataset used in research, supervised learning method used for building machine learning models. Challenges in Detecting Lung Diseases with Machine Learning Detecting and predicting lung diseases using machine learning can be challenging due to a lack of labeled …
KMeans Silhouette Score Python Example
If you’re building machine learning models for solving different prediction problems, you’ve probably heard of clustering. Clustering is a popular unsupervised learning technique used to group data points with similar features into distinct clusters. One of the most widely used clustering algorithms is KMeans, which is popular due to its simplicity and efficiency. However, one major challenge in clustering is determining the optimal number of clusters that should be used to group the data points. This is where the Silhouette Score comes into play, as it helps us measure the quality of clustering and determine the optimal number of clusters. Silhouette score helps us get further clarity for the following …
Why & When to use Eigenvalues & Eigenvectors?
Eigenvalues and eigenvectors are important concepts in linear algebra that have numerous applications in data science. They provide a way to analyze the structure of linear transformations and matrices, and are used extensively in many areas of machine learning, including feature extraction, dimensionality reduction, and clustering. In simple terms, eigenvalues and eigenvectors are the building blocks of linear transformations. Eigenvalues represent the scaling factor by which a vector is transformed when a linear transformation is applied, while eigenvectors represent the directions in which the transformation occurs. In this post, you will learn about why and when you need to use Eigenvalues and Eigenvectors? As a data scientist/machine learning Engineer, one must …
Amazon Bedrock to Democratize Generative AI
Amazon Web Services (AWS) has announced the launch of Amazon Bedrock and Amazon Titan foundational models (FMs), making it easier for customers to build and scale generative AI applications with foundation models. According to AWS, they received feedback from their select customers that there are a few big things standing in their way today in relation to different AI use cases. First, they need a straightforward way to find and access high-performing FMs that give outstanding results and are best-suited for their purposes. Second, customers want integration into applications to be seamless, without having to manage huge clusters of infrastructure or incur large costs. Finally, customers want it to be …
SVM RBF Kernel Parameters: Python Examples
Support vector machines (SVM) are a popular and powerful machine learning technique for classification and regression tasks. SVM models are based on the concept of finding the optimal hyperplane that separates the data into different classes. One of the key features of SVMs is the ability to use different kernel functions to model non-linear relationships between the input variables and the output variable. One such kernel is the radial basis function (RBF) kernel, which is a popular choice for SVMs due to its flexibility and ability to capture complex relationships between the input and output variables. The RBF kernel has two important parameters: gamma and C (also called regularization parameter). …
Data Science Careers: India’s Job Market & AI Growth
Aspiring data scientists and AI enthusiasts in India have a plethora of opportunities in store, thanks to the country’s booming AI, machine learning (ML), and big data analytics industry. According to a recent report by NASSCOM, India boasts the second-largest talent pool globally in these fields, with a remarkable AI skill penetration score of 3.09 [1]. The nation’s rapid growth in AI talent concentration and scientific publications underscores the immense potential for individuals looking to build a successful data science career in India. As the demand for skilled professionals surges, multiple factors contribute to the thriving industry. The higher-than-average compensation and growth prospects in the field make it an attractive …
Generative AI: Scaling Techniques for LLM Models
In the rapidly evolving world of artificial intelligence, large language models (LLMs) have emerged as a game-changing force, revolutionizing the way we interact with technology and transforming countless industries. These powerful models can perform a vast array of tasks, from text generation and translation to question-answering and summarization. However, unlocking the full potential of these LLMs requires a deep understanding of how to effectively scale these LLMs, ensuring optimal performance and capabilities. In this blog post, we will delve into the crucial concept of scaling techniques for LLM models and explore why mastering this aspect is essential for anyone working in the AI domain. As the complexity and size of …
Meta Unveils SAM and Massive SA-1B Dataset to Advance Computer Vision Research
Meta Researchers have, yesterday, unveiled a groundbreaking new model, namely Segment Anything Model (SAM), alongside an immense dataset, the Segment Anything Dataset (SA-1B), which together promise to revolutionize the field of computer vision. SAM’s unique architecture and design make it efficient and effective, while the SA-1B dataset provides a powerful resource to fuel future research and applications. The Segment Anything Model is an innovative approach to promptable segmentation that combines an image encoder, a flexible prompt encoder, and a fast mask decoder. Its design allows for real-time, interactive prompting in a web browser on a CPU, opening up new possibilities for computer vision applications. One of the key challenges SAM …
Autoencoder vs Variational Autoencoder (VAE): Differences
In the world of generative AI models, autoencoders (AE) and variational autoencoders (VAEs) have emerged as powerful unsupervised learning techniques for data representation, compression, and generation. While they share some similarities, these algorithms have unique properties and applications that distinguish them from each other. This blog post aims to help machine learning / deep learning enthusiasts gain a deeper understanding of these two methods, their key differences, and how they can be utilized in various data-driven tasks. We will learn about autoencoders and VAEs, understanding their core components, working mechanisms, and common use-cases. We will also try and understand their differences in terms of architecture, objectives, and outcomes. What are …
Nice question to help us