Machine Learning Video Tutorial
Machine Learning Video Tutorial – Solved Numerical Examples and Implementation in Python
Machine Learning
Gradient Descent Algorithm
Gradient Descent Algorithm for Artificial Neural Networks in Machine Learning – 17CS73 Video Tutorial Gradient Descent and Delta Rule in ANN Gradient Descent and the Delta Rule is used separate the Non-Linearly Separable data. Weights are updated using the following rule, Where, Gradient Descent Algorithm Gradient descent is an important general paradigm for learning. It
Gradient Descent and Delta Rule
Gradient Descent and Delta Rule, Derivation of Delta Rule for Artificial Neural Networks in Machine Learning – 17CS73 Video Tutorial Gradient Descent and Delta Rule A set of data points are said to be linearly separable if the data can be divided into two classes using a straight line. If the data is not divided
Appropriate Problems for Artificial Neural Networks
Appropriate Problems for Artificial Neural Networks for Artificial Neural Networks in Machine Learning – 17CS73 Video Tutorial Most appropriate for problems where, Instances have many attribute-value pairs: The target function to be learned is defined over instances that can be described by a vector of predefined features. Target function output may be discrete-valued, real-valued, or
Appropriate Problems for Artificial Neural Networks Read More »
Perceptron Training Rule for Linear Classification
Perceptron Training Rule for Linear Classification for Artificial Neural Networks in Machine Learning – 17CS73 Video Tutorial A perceptron unit is used to build the ANN system. A perceptron takes a vector of real-valued inputs, calculates a linear combination of these inputs, then outputs a 1 if the result is greater than some threshold and
Perceptron Training Rule for Linear Classification Read More »
Candidate Elimination Algorithm in Python
Python Program to Implement Candidate Elimination Algorithm to get Consistent Version Space Exp. No. 2. For a given set of training data examples stored in a .CSV file, implement and demonstrate the Candidate-Elimination algorithm in python to output a description of the set of all hypotheses consistent with the training examples. Candidate Elimination Algorithm Machine
FIND S Algorithm in Python
Python Program to Implement FIND S Algorithm – to get Maximally Specific Hypothesis Exp. No. 1. Implement and demonstrate the FIND-S algorithm in Python for finding the most specific hypothesis based on a given set of training data samples. Read the training data from a .CSV file Find-S Algorithm Machine Learning 1. Initilize h to
Plant leaf infection detection Project
Plant leaf infection detection Project for final year students In this project machine learning and image processing techniques are used to detect and classify the plant leafs as infectious and non-infectious. Video Demonstration Model Building Deployment Streamlit Application The project is tested on different plant leaf datasets such as Bell Pepper, Maize, and tomato leaf
Plant Leaf Disease Detection Machine Learning Project
Plant Leaf Disease Detection Using Machine Learning and Image Processing Project for final year students Description: In this project, we first collect images of different types of infected, good, and seems to be infected plant leaves. Once the leaf dataset is prepared then, we apply an image processing algorithm to the images and predict the
Plant Leaf Disease Detection Machine Learning Project Read More »
Machine Learning Question With Answers Module 5
17CS73 Machine Learning Question With Answers Module 5 MODULE 5 – EVALUATING HYPOTHESIS, INSTANCE BASED LEARNING, REINFORCEMENT LEARNING 1. Explain the two key difficulties that arise while estimating the Accuracy of Hypothesis. 2. Define the following terms a. Sample error b. True error c. Random Variable d. Expected value e. Variance f. standard Deviation 3.