Welcome to the 100 Days of Machine Learning series — one of the most watched and most trusted and Evergreen Machine Learning playlists on Indian YouTube, followed by millions of learners who want to build a strong foundation in ML. This playlist is designed as a step-by-step roadmap to master Machine Learning, starting from the absolute basics and gradually moving toward advanced concepts and real-world implementation. Instead of jumping directly into code, the series focuses on building clear intuition, strong fundamentals, and practical understanding of how machine learning actually works. What you will learn in this series: • AI vs Machine Learning vs Deep Learning • Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning) • Data preprocessing and feature engineering • Exploratory Data Analysis (EDA) • Important ML algorithms (Regression, Classification, Clustering, etc.) • Model evaluation and validation techniques • Real-world ML workflow and best practices Each video covers a specific concept in a simple and structured way so you can build knowledge one step at a time, just like a 100-day learning roadmap. If you want to build a strong Machine Learning foundation for Data Science, AI, or ML Engineering, this playlist will guide you from beginner concepts to industry-level understanding. Notes: https://learnwith.campusx.in/s/store/courses/YouTube%20Notes
Curated by: CampusX (134 videos)
A tensor is a generalization of vectors and matrices and is easily understood as a multidimensional array. In the general case, an array of numbers arranged on a regular grid with a variable number of axes is known as a tensor. A vector is a one-dimensional or first-order tensor and a matrix is a two-dimensional or second-order tensor. Tensor notation is much like matrix notation with a capital letter representing a tensor and lowercase letters with subscript integers representing scalar values within the tensor. Many of the operations that can be performed with scalars, vectors, and matrices can be reformulated to be performed with tensors. As a tool, tensors and tensor algebra is widely used in the fields of physics and engineering. It is a term and set of techniques known in machine learning in the training and operation of deep learning models that can be described in terms of tensors. ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in/s/store ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 Instagram: https://www.instagram.com/campusx.official E-mail us at support@campusx.in ✨ Hashtags✨ #100DaysOfMachineLearning #MachineLearningFullCourse #MachineLearningInHindi ⌚Time Stamps⌚ 00:00 - Intro 03:30 - What are Tensors? 03:40 - What are 0D Tensor/Scalar 06:10 - 1D Tensor/Vector 10:30 - 2D Tensor/Matrices 12:15 - ND Tensors 15:21 - Rank, Axes and Shape 18:25 - Example of 1D Tensor 23:28 - Example of 2D Tensor 25:45 - Example of 3D Tensor 32:35 - Example of 4D Tensor 35:14 - Example of 5D Tensor
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