Topic-5-Basics of Python











Comments

Post a Comment

Popular posts from this blog

Topic-18 | Evaluation Metrics for different model

Image
  Lecture 18 | Evaluation Metrics for different model   Our previous topic was Linear Regression. Linear Regression is used to predict a number from continuous nominal or numeric values. The number we predict can be any number, it can be a positive number or a negative number, it can be a high value or a low value.   A graphical representation of linear regression was also shown.   In graphical representation there is plot, a studio plane, data points and a line that must have most of the data points around it. In order to measure the performance of graph, we also discussed the Mean Squared error and in order to nullify the effect of resultant zero root mean square error. In colab we trained a modal over a data set. An issue raised during its execution that the resultant value of Mean Squared Error was very high. Our today’s topic will start from the finding the reasons of this high value of Mean Squared Error. ...
Read more

Topic 22 | Neural Networks |

Image
  AI Free Basic Course | Lecture 22 | Neural Networks  In today lecture we will continue the Neural Network; in last lecture we gave the basic idea about what is to expect from neural network. Today we will explain the hyper parameters and its components. We will see the affect of the hyper parameter on the neural network. We will use playground and practice on notebook. At the end of lecture, we will implement a basic network using Tensor Flow. Today we will go through the things and would discuss the notebook in detail in tomorrow’s lecture that what does construct means in dense of Tensor flow, what does the layers do and what is loss function. What are possibilities to add in hidden layer and output layer. After practicing in TensorFlow you will get better idea about things. So, we will implement a neural network in TensorFlow and run it to see the outcome. Suppose we have few balls and our target is to through them in a basket placed at a distance of 10 meter. When ...
Read more

Topic 17 | Linear regression using Sklearn

Image
    AI Free Basic Course | Lecture 17 | Linear regression using Sklearn   Regression is the technique of supervised learning. There are two techniques in supervised learning, one is classification and other is regression. In classification we predict category / nominal value. Category or nominal value means something that has label; for example smoker /nonsmoker, cat /dog etc and we have to convert them into numerical value by effort. In prediction, we use the word positive and negative. For example, if a picture is given to the model and how it will predict whether it is picture of cat or not. Positive value in this would be that it is cat as we want to recognize cat. In medical test, the result is either positive or negative. So positive value is the value in which we are more interested to predict. Remember it does not depend upon  whether covid report is positive nor not, it depends what we have set the algorithm about which we are intereste...
Read more