After completing a loads of assignments given by Andrew NG in his course of Machine Learning, we came to an end where he has given the application of machine learning in Photo OCR and its implementation. OCR, or optical character recognition, is one of the earliest addressed computer vision tasks, since it does not require …
Tag Archives: ml
Anomaly Detection System
In machine learning, building a Anomaly Detection System is important to detect the unusual training set from the examples. Anomaly Detection is the identification of rare item which lies out of the group or from the majority of the data. This lead to the suspicious nature of the training example. For example:-Suppose we are testing …
Week 8-Unsupervised Learning
After learning about the Supervised Learning, we will talk about the Unsupervised learning. So, What is unsupervised learning? DEFINITION:- Unsupervised Learning is the learning algorithm used in machine learning which do contains training examples but with no label on it. Here,Clustering is the most important aspect used in unsupervised learning. clustering is the task of dividing …
Support Vector Machine
SVM(Support Vector Machine) is a machine learning algorithm used for both classification and regression problem.It is mainly used for Classification problem in which the set of training examples is divided into further parts. we perform classification by finding the hyper-plane that differentiate the two classes very well (look at the below snapshot). There are ways by which …
Spam Classifier
Spam Classifier is one of the basic project we do implementing Machine Learning. While building the projects there are some key points which we need to keep in mind:- Collect lots of data(Collect those word which occur frequently in spams) Take sophisticated features(which gives header in spam emails) Develop algorithms to process your input in …
Bias vs. Variance
In this section we will learn about bias and variance which is one the factor of contributing error in our prediction.Not only, by using high degree of polynomial, we can reduce the training error but also by understanding about the Bias and variance.So what actually is Bias and variance? High bias (underfitting): both Jtrain(Θ) and JCV(Θ) will be high. …
Evaluation Of Our Hypothesis
Machine learning is not all about creating a data model which can predict the answer based on the given training set but also to improve the efficiency of our Model by checking for errors. There are many ways to check for error, which can be given as:- Getting more training examples Trying smaller sets of …
Neural Network-Autonomous Driving
This blog is not the traditional blog but a video blog in which my mentor explained the use of Neural networks in Autonomous Driving i.e. self driving cars which uses the large application of neural networks and works in the following way which you will be seeing in the below video. The videos has been …
Cost Function and Backpropagation-Neural Networks
After the understanding of Neural networks, you must be how to calculate the h(theta) for the output node of it. There is a way to calculate it which you might be thinking, Yes it the “Cost Function” only. The same cost function which you are using to calculate the logistic regression but with generalization. With …
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Logistic Regression
Logistic Regression is the way to get the solution for Classification problem. Why logistic regression? If we use linear regression for classification problem which contain only discrete values, it’ll give wavy curve which is not suitable.Hence, we use Logistic Regression for classification. Cost function will also be changed in logistic regression, it will look like:- …
The Thesis Whisperer 