Machine learning session 4
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Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
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- How to install Python?
3. Python libraries for data analysis
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The document discusses different types of kernels used in support vector machines (SVM) for classification, including linear, polynomial, and radial basis function (RBF) kernels. It provides the mathematical formulas for each kernel type and explains that kernels transform input data into a higher dimensional space to make non-separable problems separable. The document also notes that while SVM classifiers offer high accuracy, their long training times make them unsuitable for large datasets.
Machine learning session 8
This document provides an overview of logistic regression and support vector machines (SVM). It discusses the key concepts, assumptions, and types of logistic regression, including binary, multinomial, and ordinal logistic regression. It also explains the working, concepts, and goal of SVM, which is to find a maximum marginal hyperplane to divide datasets into classes. The next session will cover implementing SVM in Python.
Machine learning session 7
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Machine learning session 3
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Machine learning session 1
Python is a high-level, general-purpose programming language that is widely used for web development and data science applications. It is an interpreted language that is interactive, object-oriented, and easy to learn. Python has simple syntax, a large standard library, and is highly extensible through modules. It is commonly used for tasks like data analysis, machine learning, and building web applications.
Augmented reality session 5
Augmented reality (AR) is an interactive experience of a real-world environment where the objects that reside in the real world are enhanced by computer-generated perceptual information, sometimes across multiple sensory modalities, including visual, auditory, haptic, somatosensory and olfactory.
Augmented reality session 4
This document discusses augmented reality and feature extraction using OpenCV. It explains that good features for recognition should be locally unique, like corners or edges, and invariant to transformations like scale, rotation or brightness changes. It describes using the ORB detector to extract features and binary string descriptors. Feature matching finds closest descriptor matches between object and scene. Homography estimation finds the transformation mapping points between images as the object is detected in subsequent frames. The document outlines using a matrix approach and pinhole camera model to map 3D world points to 2D image points. It introduces using the RANSAC algorithm to estimate the homography matrix in the presence of outliers.
Augmented reality session 3
This document provides an overview of an augmented reality project that uses Python and OpenCV. The project aims to project a 3D model onto a flat surface in real time as the surface changes position or orientation. It involves pre-processing the image to identify the flat surface and determine the transformation between the reference surface and target image using homography. The process includes recognizing the reference surface through feature extraction and matching, estimating the homography, deriving the transformation to project the 3D model onto the target image pixel space, and drawing it. The next session will cover homography estimation, the RANSAC algorithm, feature extraction, sampling, and results.
Augmented reality session 2
This document provides steps to build an augmented reality book using an online platform. It outlines registering for an account, selecting images and audio files to augment, uploading the media through the platform's book creator tool, exporting the augmented book file and downloading it to view on a mobile device using an AR browser. Focusing the camera on the images will trigger the associated audio files to play, allowing the user to experience an augmented reality interaction.
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- 1. PROGRAMMING CRASH COURSE KEDS BIODESIGNS WEEK : II CLASS : 4 SESSION : MACHINE LEARNING DATA
- 2. INTRODUCTION While working with machine learning projects, usually we ignore two most important parts called mathematics and data. It is because, we know that ML is a data driven approach and our ML model will produce only as good or as bad results as the data we provided to it. In the previous chapter, we discussed how we can upload CSV data into our ML project, but it would be good to understand the data before uploading it. We can understand the data by two ways, with statistics and with visualization. In this chapter, with the help of following Python recipes, we are going to understand ML data with statistics.
- 3. LOOKING AT RAW DATA The very first recipe is for looking at your raw data. It is important to look at raw data because the insight we will get after looking at raw data will boost our chances to better pre-processing as well as handling of data for ML projects. Following is a Python script implemented by using head() function of Pandas DataFrame on Pima Indians diabetes dataset to look at the first 50 rows to get better understanding of it
- 5. UNIVARIATE PLOTS: UNDERSTANDING ATTRIBUTES INDEPENDENTLY The simplest type of visualization is single-variable or “univariate” visualization. With the help of univariate visualization, we can understand each attribute of our dataset independently. The following are some techniques in Python to implement univariate visualization.
- 6. MULTIVARIATE PLOTS: INTERACTION AMONG MULTIPLE VARIABLES Another type of visualization is multi-variable or “multivariate” visualization. With the help of multivariate visualization, we can understand interaction between multiple attributes of our dataset. The following are some techniques in Python to implement multivariate visualization
- 7. SCRIPT 2
- 8. RESULT