Category Archives: AI

SVM RBF Kernel Parameters: Python Examples

SVM RBF Kernel Parameters - Gamma and C values

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). …

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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 …

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Generative AI: Scaling Techniques for LLM Models

Scaling techniques for foundational LLMs

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 …

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

Meta AI Announces Advancements in Embodied AI: Artificial Visual Cortex & Adaptive Skill Coordination

embodied ai - artificial visual cortext - adaptive skill coordination

Meta AI has announced two major advancements in general-purpose embodied AI agents, focusing on challenging sensorimotor skills. These advancements include an artificial visual cortex called VC-1 and a new approach called Adaptive Skill Coordination (ASC). Both developments offer valuable benefits to data scientists and researchers in the field of AI. Embodied AI is field of AI focused on agents that can perceive, understand, and interact with their environment through sensorimotor experiences. It aims to create AI systems that can perform tasks in the physical world, bridging the gap between abstract thought and reasoning, and physical actions. VC-1 is a single perception model that supports a diverse range of sensorimotor skills, …

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Support Vector Machine (SVM) Python Example

support vector machine - SVM

Support Vector Machines (SVMs) are a powerful and versatile machine learning algorithm that has gained widespread popularity among data scientists in recent years. SVMs are widely used for classification, regression, and outlier detection (one-class SVM), and have proven to be highly effective in solving complex problems in various fields, including computer vision (image classification, object detection, etc.), natural language processing (sentiment analysis, text classification, etc.), and bioinformatics (gene expression analysis, protein classification, disease diagnosis, etc.). In this post, you will learn about the concepts of Support Vector Machine (SVM)  with the help of  Python code example for building a machine learning classification model. We will work with Python Sklearn package for building the …

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

Machine Learning Bias Explained with Examples

machine learning models bias variance vs complexity

In the artificial intelligence (AI) / machine learning (ML) powered world where predictive models have started getting used more often in decision-making areas, the primary concerns of policy makers, auditors and end users have been to make sure that these systems using the models are not making biased/unfair decisions based on model predictions (intentional or unintentional discrimination). Imagine industries such as banking, insurance, and employment where models are used as solutions to decision-making problems such as shortlisting candidates for interviews, approving loans/credits, deciding insurance premiums etc. How harmful it could be to the end users as these decisions may impact their livelihood based on biased predictions made by the model, thereby, …

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Random Forest Classifier Python Example

random forest classifier machine learning

Random forest classifiers are popular machine learning algorithms that are used for classification. In this post, you will learn about the concepts of random forest classifiers and how to train a Random Forest Classifier using the Python Sklearn library. This code will be helpful if you are a beginner data scientist or just want to quickly get a code sample to get started with training a machine learning model using the Random Forest algorithm. The following topics will be covered: What is a Random Forest Classifier & How do they Work? Random forests are a type of machine learning algorithm that is used for classification and regression tasks. A classifier …

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

True Error vs Sample Error: Difference

Understanding the differences between true error and sample error is an important aspect of data science. In this blog post, we will be exploring the difference between these two common features of statistical inference. We’ll discuss what they are and how they differ from each other, as well as provide some examples of real-world scenarios where an understanding of both is important. By the end, you should have a better grasp of the differences between true error and sample error. In case you are a data scientist, you will want to understand the concept behind the true error and sample error. These concepts are key to understand for evaluating a …

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Types of Probability Distributions: Codes, Examples

uniform probability distribution plot

In this post, you will learn the definition of 25 different types of probability distributions. Probability distributions play an important role in statistics and in many other fields, such as economics, engineering, and finance. They are used to model all sorts of real-world phenomena, from the weather to stock market prices. Before we get into understanding different types of probability distributions, let’s understand some fundamentals. If you are a data scientist, you would like to go through these distributions. This page could also be seen as a cheat sheet for probability distributions. What are Probability Distributions? Probability distributions are a way of describing how likely it is for a random …

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AI Product Manager Interview Questions

interview questions for machine learning

AI has become such an integral part of our lives that it is important to hire professionals who can help create AI / machine learning products that will be used by many people. These AI product manager interview questions will give you insight into your product manager candidate’s experience, skills, and industry knowledge so that you can get prepared in a better manner before appearing for your next interview as an AI product manager. Check out a detailed interview questions and answers with greater focus on machine learning topics. Before getting into the list of interview questions, lets understand what can be the job description of an AI product manager. …

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Posted in AI, Career Planning, Interview questions, Machine Learning, Product Management. Tagged with , , .

Building AI-powered Organization & Cultural Traits

AI powered organization and cultural traits

Artificial Intelligence (AI) has become an integral part of many organizations’ operations. From customer service to supply chain management, AI is increasingly being used to automate and streamline processes. However, AI can do more than just help you run your business more efficiently; it can also be used to build organizational culture and foster data-driven decision making in general while leveraging analytical tools & techniques. Let’s take a look at how AI-powered organizational and cultural traits can help improve the workplace. The following picture is a summary of cultural traits in AI-driven organization Be Curious The adoption of artificial intelligence (AI) within an organization can enhance curiosity in several ways. …

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Challenges for Machine Learning / AI Projects

Challenges related to Machine Learning Projects Implementations

In this post, you will learn about some of the key challenges in relation to achieving successful AI / machine learning (ML) or Data science projects implementation in a consistent and sustained manner. As AI / ML project stakeholders including senior management stakeholders, data science architects, product managers, etc, you must get a good understanding of what would it take to successfully execute AI / ML projects and create value for the customers and the business.  Whether you are building AI / ML products or enabling unique models for your clients in SaaS setup, you will come across most of these challenges.  Understanding the Business Problem Many times, the nature …

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Most Common Machine Learning Tasks

common machine learning tasks

This article represents some of the most common machine learning tasks that one may come across while trying to solve machine learning problems. Also listed is a set of machine learning methods that could be used to resolve these tasks. Please feel free to comment/suggest if I missed mentioning one or more important points. Also, sorry for the typos. You might want to check out the post on what is machine learning?. Different aspects of machine learning concepts have been explained with the help of examples. Here is an excerpt from the page: Machine learning is about approximating mathematical functions (equations) representing real-world scenarios. These mathematical functions are also referred …

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Feature Scaling in Machine Learning: Python Examples

In this post you will learn about a simple technique namely feature scaling with Python code examples using which you could improve machine learning models. The models will be trained using Perceptron (single-layer neural network) classifier. First and foremost, lets quickly understand what is feature scaling and why one needs it? What is Feature Scaling and Why does one need it? Feature scaling is a method used to standardize the range of independent variables or features of data. In data processing, it is also known as data normalization or standardization. Feature scaling is generally performed during the data pre-processing stage, before training models using machine learning algorithms.  The goal is to …

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Supply chain management & Machine Learning

supply chain management and AI and Machine Learning use cases

As supply chains become more complex, businesses are looking for new ways to optimize and automate their supply chain operations. One area that is seeing a lot of growth is the use of artificial intelligence (AI) and machine learning in supply chain management. There are many different applications for these technologies in supply chain management, from forecasting demand to optimizing inventory levels. In this blog post, we will explore some of the most interesting use cases for AI and machine learning in supply chain management. What is supply chain management and what are its key components? Supply chain management is the process of coordinating and controlling the flow of goods, …

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Posted in AI, Machine Learning, Supply chain management. Tagged with , , .

Weak Supervised Learning: Concepts & Examples

weak supervised learning

Supervised learning is a type of machine learning algorithm that uses a labeled dataset to learn and generalize from. The labels act as supervisors, providing the algorithm with feedback so it can learn to map input data to the correct output labels. In this blog post, we’ll be focusing on weak supervised learning, a subset of supervised learning that uses only partially labeled or unlabeled data. We’ll cover some of the most common weak supervision techniques and provide examples of each. What is Weak Supervised Learning? Weak supervised learning is a type of machine learning where the learner is only given a few labels to work with. Weak supervision is …

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