Higher-order functions are functions that take a function as a parameter and/or return a function as an output.

A few useful higher-order functions are map(), filter(), and reduce(). map() and filter() are built-in functions, whereas reduce() is contained in functools() module.

Let’s learn about map(), filter(), and reduce() in this article.

map()

map() is used to apply a function to each item in the iterable at the same time.
map() will return a map object that is an iterator:

map(function,iterable…)

Calculating the square of all numbers in the iterable:

• square=map(lambda x:x*2, num): We can pass the lambda function or user_defined function. …

split() vs. partition()

In Python, we can split the string by using the following methods. Let’s look at these methods in detail.

1. split()
2. rsplit()
3. splitlines()
4. partition()
5. rpartition()
6. re.split()
7. Differences between split() and partition()
8. Conclusion
9. Resources

split()

“Return a list of the words in the string, using sep as the delimiter string. If maxsplit is given, at most maxsplit splits are done (thus, the list will have at most maxsplit+1 elements). If maxsplit is not specified or -1, then there is no limit on the number of splits (all possible splits are made).”

— Python docs

str.split(sep=None,maxsplit=-1)…

Linear Regression in Python

Linear Regression is a machine learning algorithm based on supervised learning. Linear Regression is a predictive model that is used for finding the linear relationship between a dependent variable and one or more independent variables. Here,dependent variable/target variable(Y) should be continuous variable.

Let’s learn the math behind simple linear regression and the Python way of implementation using ski-kit learn

Dataset

Let’s looks at our dataset first. I have taken a simple dataset for an easy explanation. Years of Experience vs Salary.

We want to predict the salary of the person based on their years of experience?

Math Behind the Simple Linear Regression

Dataset

A Comprehensive Guide to Pandas for Data Science

Pandas

Pandas is an open-source python package that provides numerous tools for high-performance data analysis and data manipulation.

Let’s learn about the most widely used Pandas Library in this article.

Table of content

1. Pandas Series
2. Pandas DataFrame
3. How to create Pandas DataFrame?
4. Understanding Pandas DataFrames
5. Sorting Pandas DataFrames
6. Indexing and Slicing Pandas Dataframes
7. Subset DataFrames based on certain conditions
8. How to fill/drop the null values?
9. Lambda functions to modify dataframe
10. Merge, Concatenate dataframes
11. Grouping and aggregating

Pandas Datastructures

Pandas supports two datastructures

1. Pandas Series
2. Pandas DataFrame

Pandas Series

Pandas Series is a one-dimensional labeled array capable of holding any data type. …

Linear Algebra for Data Science

A good understanding of linear algebra is much needed for understanding many machine learning algorithms.

In this article, we will learn about vectors, the dot product of vectors, and the geometric representation of the dot product in detail.

Table of Content

1. How point is represented as a vector?
2. Distance between point and origin
3. Distance between two points
4. Row Vector, Column Vector, Matrix
5. Mathematical Operations on Vector
6. Dot Product between two vectors
7. Geometric Interpretation of the Dot Product

How a Point is Represented as a Vector?

Let us consider a point P in 2-D space.

Confusion Matrix

In machine learning, the confusion matrix helps to summarize the performance of classification models. From the confusion matrix, we can calculate many metrics like recall, precision,f1 score which is used to evaluate the performance of classification models.

In this blog, we will learn about the confusion matrix and the metrics calculated from the confusion matrix.

Table of Content

• Classification Accuracy
• What we need Confusion Matrix?
• What is the Confusion Matrix?
• Terminologies used in Confusion Matrix
• Metrics Calculated from Confusion Matrix
  1.Precision
  2. Recall
• Which metric to choose precision or recall?
• F1 Score
• F1 Score vs Accuracy
• Calculating metrics from the confusion matrix
• Confusion matrix using scikit…

Replace Occurrences of Substrings in Strings in Python

1. str.replace()
2. re.sub()
3. re.subn()

By using the above-mentioned methods, let’s see how to replace substrings in strings.

1. Replace all occurrences of substring

‘Using str.replace()’

Syntax: str.replace(old,new,count)

Example 1: Replace substring “two” by “one”

Correlation Coefficient

In machine learning, before we generate any model, we need to understand the relationship between independent variables and the target variable.

The correlation coefficient is used as a measure of association. Let’s learn in detail about correlation and covariance in this article.

Topics covered in this story

• What is the correlation?
• Types of correlation.
• Pearson’s Correlation Coefficient
• What is covariance?
• Why correlation is preferred over covariance?
• What is the range of the correlation coefficient?
• Interpreting Pearson's correlation coefficient.
• How to visualize the correlation?
• Calculating Covariance and Coefficient Correlation.
• Correlation vs Covariance

What is Correlation?

Correlation is a measure of association. Correlation is used for bivariate analysis. …