Tag Archives: machine learning
Ridge Regression Concepts & Python example
Ridge regression is a type of linear regression that penalizes ridge coefficients. This technique can be used to reduce the effects of multicollinearity in ridge regression, which may result from high correlations among predictors or between predictors and independent variables. In this tutorial, we will explain ridge regression with a Python example. What is Ridge Regression? Ridge regression is a powerful technique in machine learning that addresses the issue of overfitting in linear models. In linear regression, we aim to model the relationship between a response variable and one or more predictor variables. However, when there are multiple variables that are highly correlated, the model can become too complex and …
Microsoft Designer: Transforming Design with AI
Are you looking for a hassle-free and smart way to make impressive designs without any graphic design expertise? Then Microsoft’s newly launched tool, Microsoft Designer, may just be the answer. The realm of digital design welcomes a new entrant that promises to revolutionize the way we create – Microsoft’s latest offering, Microsoft Designer. Drawing parallels with the popular tool Canva, Microsoft Designer is an innovative platform that brings effortless design capabilities to your fingertips. This user-friendly, AI-powered design platform caters to an extensive array of users, from entrepreneurs and marketers to educators and students, and even to social media enthusiasts. Whether you are a seasoned professional looking to expedite your …
Bayesian Machine Learning Applications Examples
Have you ever wondered how machines can make decisions with uncertainty? What if there was an approach in machine learning that not only learned from data but also quantified and managed uncertainty in a principled way? Enter the realm of Bayesian machine learning. Bayesian machine learning is one of the most powerful modeling technique in predictive analytics. It marries the probabilistic reasoning with machine learning algorithms. Bayes’ theorem, which was first introduced by Reverend Thomas Bayes in 1763, provides a way to infer probabilities from observations. Bayesian machine learning has become increasingly popular because it can be used for real-world applications such as spam filtering (NLP), credit card fraud detection, …
Azure Machine Learning Studio: Getting Started
Azure Machine Learning Studio is a powerful cloud-based platform that brings the world of machine learning to your fingertips. Whether you’re a data scientist, a developer, or a business professional, Azure Machine Learning Studio provides a user-friendly and collaborative environment to build, train, and deploy machine learning models with ease. This blog post serves as a quick tutorial to help you get started with Azure Machine Learning Studio. From setting up your workspace to exploring key features and best practices, we will walk you through the essential steps to embark on your machine learning journey. Azure ML Studio – Machine Learning Pipeline Before we can proceed with the tasks in …
Machine Learning NPTEL Online Courses List 2023
Machine learning is a rapidly evolving field that has gained immense popularity in recent years. As technology continues to advance, the demand for professionals with expertise in machine learning continues to soar. If you’re someone who is interested in diving deep into the world of machine learning or looking to enhance your existing knowledge, the NPTel courses are an excellent avenue to explore. The National Programme on Technology Enhanced Learning (NPTel) is a joint initiative by the Indian Institutes of Technology (IITs) and the Indian Institute of Science (IISc). It offers a wide range of online courses across various disciplines, including computer science and engineering. In this blog, we will …
How to Access GPT-4 using OpenAI Playground?
How good it would be if we could access GPT-4 using the OpenAI Playground and harness the groundbreaking advancements OpenAI has made in generating human-like text? OpenAI has revolutionized the field of natural language processing (NLP) with its large language models (such as different versions of GPT-3.5), and the release of GPT-4 has further pushed the boundaries of what’s achievable. In this blog post, we will guide you through a step-by-step process to access GPT-4 model using the OpenAI playground. Step 1: Visit the OpenAI Playground To get started, open your web browser and navigate to the OpenAI Playground website. The URL for the OpenAI Playground is https://playground.openai.com/. Step 2: …
Online US Degree Courses & Programs in AI / Machine Learning
Data Science & AI / Machine learning has emerged as a transformative field, revolutionizing industries and shaping the future of technology. As the demand for professionals skilled in machine learning continues to rise, top universities in the United States (USA) have recognized the need to offer online degree courses and programs in this dynamic field. Through these online offerings, students can now access world-class education and earn prestigious degrees from the comfort of their own homes, while benefiting from the expertise of renowned faculty members. In this blog post, we present a curated list of leading US universities that provide online degree courses and programs in machine learning. Whether you …
Azure OpenAI Service Details & Pricing Info
Azure OpenAI, an offering from Microsoft Azure, empowers developers, researchers, and enterprises with the transformative capabilities of Artificial Intelligence (AI). In this blog post, we explore Azure OpenAI’s service details and pricing information, providing you with insights to harness the immense power of AI. Azure OpenAI Services Information Azure OpenAI service provides a range of large language models from standard machine learning models to fine-tuned ones for specific tasks. We can build intelligent chatbots, automate code generation, or enhance natural language understanding. We can leverage conversational AI models for interactive virtual assistants that elevate user experiences and streamline operations. Image generation models can be used to produce stunning, realistic visuals. …
Binomial Distribution Explained with Examples
Have you ever wondered how to predict the number of successes in a series of independent trials? Or perhaps you’ve been curious about the probability of achieving a specific outcome in a sequence of yes-or-no questions. If so, we are essentially talking about the binomial distribution. It’s important for data scientists to understand this concept as binomials are used often in business applications. The binomial distribution is a discrete probability distribution that applies to binomial experiments (experiments with binary outcomes). It’s the number of successes in a specific number of trials. Sighting a simple yet real-life example, the binomial distribution may be imagined as the probability distribution of a number …
Model Cards Example Machine Learning
Have you ever wondered how to make your machine learning models more transparent, understandable, and accountable? Are you looking to implement responsible AI practices including ways and means to review and improve your existing model documentation? If so, you will learn about the concept of model cards, a powerful tool for documenting important details about machine learning models. You will learn the concepts with concrete examples and best practices that can serve as a guide for implementing or improving model cards in your organizations. The model card example can be seen as an standard template for model card which gets used in various different companies such as Google. What are …
Hold-out Method for Training Machine Learning Models
The hold-out method for training the machine learning models is a technique that involves splitting the data into different sets: one set for training, and other sets for validation and testing. The hold-out method is used to check how well a machine learning model will perform on the new data. In this post, you will learn about the hold-out method used during the process of training the machine learning model. Do check out my post on what is machine learning? concepts & examples for a detailed understanding of different aspects related to the basics of machine learning. Also, check out a related post on what is data science? When evaluating …
Google Unveils Next-Gen LLM, PaLM-2
Google’s breakthrough research in machine learning and responsible AI has culminated in the development of their next-generation large language model (LLM), PaLM 2. This model represents a significant evolution in natural language processing (NLP) technology, with the capability to perform a broad array of advanced reasoning tasks, including code and math, text classification and question answering, language translation, and natural language generation. The unique combination of compute-optimal scaling, an improved dataset mixture, and model architecture enhancements is what powers PaLM 2’s exceptional capabilities. This combination allows the model to achieve superior performance than its predecessors, including the original PaLM, across all tasks. PaLM 2 was built with Google’s commitment to …
Occam’s Razor in Machine Learning: Examples
“Everything should be made as simple as possible, but not simpler.” – Albert Einstein Consider this: According to a recent study by IDC, data scientists spend approximately 80% of their time cleaning and preparing data for analysis, leaving only 20% of their time for the actual tasks of analysis, modeling, and interpretation. Does this sound familiar to you? Are you frustrated by the amount of time you spend on complex data wrangling and model tuning, only to find that your machine learning model doesn’t generalize well to new data? As data scientists, we often find ourselves in a predicament. We strive for the highest accuracy and predictive power in our …
Generative AI Risks & Concerns: Examples
In the ever-evolving realm of artificial intelligence, generative AI has emerged as a groundbreaking technology, capable of producing incredibly realistic and creative content. From generating art and music to crafting compelling stories and even mimicking human conversations, the possibilities seem endless. Here is a sample representing AI generated talk between Bill Gates & Socrates. You can as well imagine about the endless possibilities. As with any powerful tool, there are risks and concerns related to generative AI that need to be addressed. In this blog, we will delve into the fascinating world of generative AI and explore some of the key concerns it brings forth. We will learn with some …
Outlier Detection Techniques in Python: Examples
In the realm of data science, mastering outlier detection techniques is paramount for ensuring data integrity and robust machine learning model performance. Outliers are the data points which deviate significantly from the norm. The outliers data points can greatly impact the accuracy and reliability of statistical analyses and machine learning models. In this blog, we will explore a variety of outlier detection techniques using Python. The methods covered will include statistical approaches like the z-score method and the interquartile range (IQR) method, as well as visualization techniques like box plots and scatter plots. Whether you are a data science enthusiast or a seasoned professional, it is important to grasp these …
Lime Machine Learning Python Example
Today when core businesses have started relying on machine learning (ML) models predictions, interpreting complex models has become a necessary requirement of AI governance (responsible AI). Data scientists are often asked to explain the inner workings of a machine learning models for understanding how the decisions are made. The Problem? Many of these models stand out as “black boxes“, delivering predictions without any comprehensible reasoning. This lack of transparency (especially in healthcare & finance use cases) can lead to mistrust in model predictions and inhibit the practical application of machine learning in fields that require a high degree of interpretability. It could lead to erroneous decision-making, or worse, legal and …
I found it very helpful. However the differences are not too understandable for me