There are two different working modes for the Jupyter Notebook.Therefore , the same keyboard key press has different effect, depending on which mode the notebook is in currently.There are two modes: Edit mode When in edit mode of jupyter notebook, you can type code and text. we can enter this mode, by pressing enter or … Continue reading Jupyter Notebook Edit mode and Command mode

# Data Science

# Difference between Variance and Covariance

Variance is the measure of, the spread between numbers, in a given data set. In other words, it means, how far each number in the data set is, from the mean of this data set. 2. Covariance is the measure of, the directional relationship between, two random variables. In other words, covariance measures, how much, … Continue reading Difference between Variance and Covariance

# Difference Between Batch, Mini-Batch and Stochastic Gradient Descent

Gradient Descent is one the key algorithm used in Machine Learning. While training machine learning model, we require an algorithm to minimize the value of loss function. Gradient Descent is one of the optimization algorithm , that is used to minimize the loss. There are mainly three types of Gradient Descent algorithm1. Batch Gradient DescentBatch … Continue reading Difference Between Batch, Mini-Batch and Stochastic Gradient Descent

# Decision Tree vs Linear Regression

Decision Tree can be used for implementing regression as well as classification models, however , Linear Regression can be used for regression problem only. Decision tree can be used for regression, even when there is non linear relationships between the features and the output variable. However, Linear Regression works really nicely when there is linear relationship … Continue reading Decision Tree vs Linear Regression

# Handling missing data in pandas data frame python

In this post we are going to discuss how to handle missing data from a pandas data frame. Find total number of missing data in the data frame missing_total = df.isnull().sum().sum() Find number of missing data in each column in a data frame missing_per_column = df.isnull().sum() Investigate patterns in the amount of missing data in … Continue reading Handling missing data in pandas data frame python

# Visualize or Print Random Forest Algorithm Model

As a machine learning engineer you may have created the Random Forest Algorithm Model, but have you ever tried to visualize it. If not , this is the post for you. In this post we will learn how to Visualize or Print Random Forest Algorithm Model in Jupyter notebook. Let us import the required library: … Continue reading Visualize or Print Random Forest Algorithm Model

# Random Forest Classification in Python in 10 Lines

Random Forest algorithm is like an ensemble algorithm made of Decision Trees, which comprises more than one decision tree to create a model. It creates more than one tree like conditional control statements to create its model hence it is named as Random Forest. Random Forest machine learning algorithm can be used to solve both … Continue reading Random Forest Classification in Python in 10 Lines

# Random Forest Regression in Python in 10 Lines

Random Forest algorithm is like Decision Tree, which comprises more than one decision tree to create a model. Random Forest algorithm is an ensemble method. It creates more than one tree like conditional control statements to create its model hence it is named as Random Forest. Random Forest machine learning algorithm can be used to … Continue reading Random Forest Regression in Python in 10 Lines

# Visualize or Print Decision Tree Algorithm Model

As a machine learning engineer you may have created the Decision Tree Model, but have you ever tried to visualize it. If not , this is the post for you. In this post we will learn how to Visualize or Print Decision Tree Model in Jupyter notebook. Let us import the required library: from IPython.display … Continue reading Visualize or Print Decision Tree Algorithm Model

# Decision Tree Classification in Python in 10 lines

Decision tree machine learning algorithm can be used to solve not only regression but also classification problems. This algorithm creates a tree like conditional control statements to create its model hence it is named as decision tree. In this post we will be implementing a simple decision tree classification model using python and sklearn. First … Continue reading Decision Tree Classification in Python in 10 lines