Check out all #keynote

Mathplotlib for beginners

@epythonlab #books

Free Python Course
https://www.udacity.com/course/introduction-to-python--ud1110
#udacity @epythonlab

Do you want to become a software engineer at one of tech company's in USA?

https://www.microverse.org/?grsf=4ydafn
@epythonlab

Count Number of Word Occurrences in List Python

@epythonlab #codetip #list

💻 Linear Algebra for Natural Language Processing

https://www.kdnuggets.com/2021/08/linear-algebra-natural-language-processing.html

Code: https://github.com/Taaniya/linear-algebra-for-ml

@epythonlab #nlp #code #article

Data science from scratch

📖Link: https://github.com/joelgrus/data-science-from-scratch

@epythonlab #books

Auto messaging to groups with multiple accounts
@epythonlab #videotips

How to deal with big data in Python

https://bit.ly/3qQtWRQ
@epythonlab #article

How to be good at algorithms?
https://telegra.ph/How-to-be-good-at-algorithms-01-06

How to Fix: KeyError in Pandas?

The KeyError in Pandas occurs when you try to access the columns in pandas DataFrame, which does not exist, or you misspell them. 

Typically, we import data from the excel name, which imports the column names, and there are high chances that you misspell the column names or include an unwanted space before or after the column name.



https://itsmycode.com/how-to-fix-keyerror-in-pandas/

@epythonlab

Project Idea: Building a spam classifier

Introduction

Spam detection is one of the major applications of Machine Learning in the interwebs today. Pretty much all of the major email service providers have spam detection systems built in and automatically classify such mail as 'Junk Mail'.

In this mission we will be using the Naive Bayes algorithm to create a model that can classify dataset SMS messages as spam or not spam, based on the training we give to the model. It is important to have some level of intuition as to what a spammy text message might look like.
What are spammy messages?

Usually they have words like 'free', 'win', 'winner', 'cash', 'prize', or similar words in them, as these texts are designed to catch your eye and tempt you to open them. Also, spam messages tend to have words written in all capitals and also tend to use a lot of exclamation marks. To the recipient, it is usually pretty straightforward to identify a spam text and our objective here is to train a model to do that for us!

Being able to identify spam messages is a binary classification problem as messages are classified as either 'Spam' or 'Not Spam' and nothing else. Also, this is a supervised learning problem, as we know what are trying to predict. We will be feeding a labelled dataset into the model, that it can learn from, to make future predictions.
@epythonlab #projectIdea #ml

Implementing Bag of Words from scratch

Before we dive into scikit-learn's Bag of Words (BoW) library to do the dirty work for us, let's implement it ourselves first so that we can understand what's happening behind the scenes.

Step 1: Convert all strings to their lower case form.

Let's say we have a document set:
documents = ['Hello, how are you!',
'Win money, win from home.',
'Call me now.',
'Hello, Call hello you tomorrow?']


Instructions:

Convert all the strings in the documents set to their lower case. Save them into a list called 'lower_case_documents'. You can convert strings to their lower case in python by using the lower() method.

@epythonlab #codetip #naive_bayes #ml #AI

Step 2: Removing all punctuation
Instructions: Remove all punctuation from the strings in the document set. Save the strings into a list called 'sans_punctuation_documents'.
@epythonlab #codetip #ml #AI #naive_bayes