Implementing Logistic Regression in 10 lines in Python

Logistic Regression is one of the most popular Machine Learning algorithm used for the classification problems. It should be noted that though there is a regression word in the name of the algorithm Logistic Regression, it is used for classification problems. A use case of Logistic regression could be, based on the symptoms for a disease that a patient has Logistic … Continue reading Implementing Logistic Regression in 10 lines in Python

Advertisements

Top 5 Advantages and Disadvantages of Support Vector Machine Algorithm

Support vector machines or SVM is a supervised machine learning algorithm that can be used for both classification and regression analysis. Advantages of Support Vector algorithm Support vector machine is very effective even with high dimensional data.When you have a data set where number of features is more than the number of rows of data, … Continue reading Top 5 Advantages and Disadvantages of Support Vector Machine Algorithm

Top 6 Advantages and Disadvantages of Decision Tree Algorithm

Decision Tree is one the most useful machine learning algorithm. Decision tree can be used to solve both classification and regression problem. When we use data points to create a decision tree, every internal node of the tree represents an attribute and every leaf node represents a class label. Like any other machine learning algorithm, … Continue reading Top 6 Advantages and Disadvantages of Decision Tree Algorithm

How to Enable Intellisense or Autocomplete in Jupyter Notebook

No matter how good you are in programming with respect to a language like python you may not be able to remember all the functions names or syntax or function parameters. So you may require to use intellisense or autocomplete feature of Jupyter notebook while programming in pandas, python and similar libraries. Yes it is … Continue reading How to Enable Intellisense or Autocomplete in Jupyter Notebook

AttributeError: module tensorflow has no attribute placeholder TensorFlow 2.0

This error may come if you have installed TensorFlow 2.0 and want to run code that is not compatible with TensorFlow 2.0 For example if you are trying to run below code, you will get the above error. x = tf.placeholder("float", None) The sample error is shown in the Image below Attribute Error TensorFlow 2.0 … Continue reading AttributeError: module tensorflow has no attribute placeholder TensorFlow 2.0