Tag Archives: python
Python Scraper for GoogleNews, Twitter, Reddit & Arxiv
In this post, you will get the Python code for scraping latest and greatest news about any topics from Google News, Twitter, Reddit and Arxiv. This could prove to be very useful for data scientist, machine learning enthusiats to keep track of latest and greatest happening in the field of artificial intelligence. If you are doing some research work, these pieces of code would prove to be very handy to quickly access the information. The code in this post has been worked out in Google Colab notebook. First and foremost, import the necessary Python libraries such as the following for GoogleNews, Twitter and Arxiv. Python Code for mining GoogleNews Here …
Reddit Scraper Code using Python & Reddit API
In this post, you will get Python code sample using which you can search Reddit for specific subreddit posts including hot posts. Reddit API is used in the Python code. This code will be helpful if you quickly want to scrape Reddit for popular posts in the field of machine learning (subreddit – r/machinelearning), data science (subreddit – r/datascience), deep learning (subreddit – r/deeplearning) etc. There will be two steps to be followed to scrape Reddit for popular posts in any specific subreddits. Python code for authentication and authorization Python code for retrieving the popular posts Check the Reddit API documentation page to learn about Reddit APIs. Python code for …
Mining Twitter Data – Python Code Example
In this post, you will learn about how to get started with mining Twitter data. This will be very helpful if you would like to build machine learning models based on NLP techniques. The Python source code used in this post is worked out using Jupyter notebook. The following are key aspects of getting started with Python Twitter APIs. Set up Twitter dev app and Python Twitter package Establish connection with Twitter Twitter API example – location-based trends, user timeline, etc Search twitter by hashtags Setup Twitter Dev App & Python Twitter Package In this section, you will learn about the following two key aspects before you get started with …
Python Scraper Code to Search Arxiv Latest Papers
In this post, you will learn about Python source code related to search Arxiv for relevant and latest machine learning and data science research papers. If you are looking for a faster way to research on Arxiv papers without really going to the Arxiv website, you may want to get this piece of code in your kitty. You can further automate the Arxiv search to get notified based on some logic. Without further ado, let’s get started. Step 1: Install Python Arxiv Library As a first step, install the Python Arxiv library using the code such as below in your Jupyter notebook or Google colab instance: Step 2: Execute the …
Google News Search Python API Example
In this post, you will learn about how to use GoogleNews search Python library to get or retrieve or scrape news from Google News for last N number of days. This would be very helpful for someone wanting to track new work / projects in relation to machine learning, data science, deep learning or any field including sports, politics etc. Without further ado, lets jump in right away. You can log into Google colab and practise the code. Step 1: First and foremost, lets install GoogleNews python library. Step 2: Instantiate GoogleNews object. One can pass the language and period to instantiate the object. The parameter, period, represents the news …
Python – How to Create Dictionary using Pandas Series
In this post, you will learn about one of the important Pandas fundamental data structure namely Series and how it can be used as a dictionary. It will be useful for beginner data scientist to understand the concept of Pandas Series object. A dictionary is a structure that maps arbitrary keys to a set of arbitrary values. Pandas Series is a one-dimensional array of indexed data. It can be created using a list or an array. Pandas Series can be thought of as a special case of Python dictionary. It is a structure which maps typed keys to a set of typed values. Here are the three different ways in …
Python – Replace Missing Values with Mean, Median & Mode
In this post, you will learn about how to impute or replace missing values with mean, median and mode in one or more numeric feature columns of Pandas DataFrame while building machine learning (ML) models with Python programming. You will also learn about how to decide which technique to use for imputing missing values with central tendency measures of feature column such as mean, median or mode. This is important to understand this technique for data scientists as handling missing values one of the key aspects of data preprocessing when training ML models. The dataset used for illustration purpose is related campus recruitment and taken from Kaggle page on Campus Recruitment. As a first step, the …
Free Online Books – Machine Learning with Python
This post lists down free online books for machine learning with Python. These books covers topiccs related to machine learning, deep learning, and NLP. This post will be updated from time to time as I discover more books. Here are the titles of these books: Python data science handbook Building machine learning systems with Python Deep learning with Python Natural language processing with Python Think Bayes Scikit-learn tutorial – statistical learning for scientific data processing Python Data Science Handbook Covers topics such as some of the following: Introduction to Numpy Data manipulation with Pandas Visualization with Matplotlib Machine learning topics (Linear regression, SVM, random forest, principal component analysis, K-means clustering, Gaussian …
Gradient Boosting Regression Python Examples
In this post, you will learn about the concepts of Gradient Boosting Regression with the help of Python Sklearn code example. Gradient Boosting algorithm is one of the key boosting machine learning algorithms apart from AdaBoost and XGBoost. What is Gradient Boosting Regression? Gradient Boosting algorithm is used to generate an ensemble model by combining the weak learners or weak predictive models. Gradient boosting algorithm can be used to train models for both regression and classification problem. Gradient Boosting Regression algorithm is used to fit the model which predicts the continuous value. Gradient boosting builds an additive mode by using multiple decision trees of fixed size as weak learners or …
Hierarchical Clustering Explained with Python Example
In this post, you will learn about the concepts of Hierarchical clustering with the help of Python code example. As data scientist / machine learning enthusiasts, you would want to learn the concepts of hierarchical clustering in a great manner. The following topics will be covered in this post: What is hierarchical clustering? Hierarchical clustering Python example What is Hierarchical Clustering? Hierarchical clustering is an unsupervised learning algorithm which is based on clustering data based on hierarchical ordering. Recall that clustering is an algorithm which groups data points within multiple clusters such that data within each cluster are similar to each other while clusters are different each other. The hierarchical clustering can be classified …
Keras CNN Image Classification Example
In this post, you will learn about how to train a Keras Convolution Neural Network (CNN) for image classification. Before going ahead and looking at the Python / Keras code examples and related concepts, you may want to check my post on Convolution Neural Network – Simply Explained in order to get a good understanding of CNN concepts. Keras CNN Image Classification Code Example First and foremost, we will need to get the image data for training the model. In this post, Keras CNN used for image classification uses the Kaggle Fashion MNIST dataset. Fashion-MNIST is a dataset of Zalando’s article images—consisting of a training set of 60,000 examples and a …
Keras Neural Network for Regression Problem
In this post, you will learn about how to train neural network for regression machine learning problems using Python Keras. Regression problems are those which are related to predicting numerical continuous value based on input parameters / features. You may want to check out some of the following posts in relation to how to use Keras to train neural network for classification problems: Keras – How to train neural network to solve multi-class classification Keras – How to use learning curve to select most optimal neural network configuration for training classification model In this post, the following topics are covered: Design Keras neural network architecture for regression Keras neural network …
Keras Multi-class Classification using IRIS Dataset
In this post, you will learn about how to train a neural network for multi-class classification using Python Keras libraries and Sklearn IRIS dataset. As a deep learning enthusiasts, it will be good to learn about how to use Keras for training a multi-class classification neural network. The following topics are covered in this post: Keras neural network concepts for training multi-class classification model Python Keras code for fitting neural network using IRIS dataset Keras Neural Network Concepts for training Multi-class Classification Model Training a neural network for multi-class classification using Keras will require the following seven steps to be taken: Loading Sklearn IRIS dataset Prepare the dataset for training and testing …
Python – How to Add Trend Line to Line Chart / Graph
In this plot, you will learn about how to add trend line to the line chart / line graph using Python Matplotlib.As a data scientist, it proves to be helpful to learn the concepts and related Python code which can be used to draw or add the trend line to the line charts as it helps understand the trend and make decisions. In this post, we will consider an example of IPL average batting scores of Virat Kohli, Chris Gayle, MS Dhoni and Rohit Sharma of last 10 years, and, assess the trend related to their overall performance using trend lines. Let’s say that main reason why we want to …
What’s Softmax Function & Why do we need it?
In this post, you will learn about the concepts of Softmax function with Python code example and why do we need Softmax function? As a data scientist / machine learning enthusiasts, it is very important to understand the concepts of Softmax function as it helps in understanding the algorithms such as neural network, multinomial logistic regression in better manner. Note that Softmax function is used in various multiclass classification machine learning algorithms such as multinomial logistic regression (thus, also called as softmax regression), neural networks etc. What’s Softmax Function? Simply speaking, Softmax function converts raw values (as outcome of functions) into probabilities. Here is how the softmax function looks like: …
Python Sklearn – How to Generate Random Datasets
In this post, you will learn about some useful random datasets generators provided by Python Sklearn. There are many methods provided as part of Sklearn.datasets package. In this post, we will take the most common ones such as some of the following which could be used for creating data sets for doing proof-of-concepts solution for regression, classification and clustering machine learning algorithms. As data scientists, you must get familiar with these methods in order to quickly create the datasets for training models using different machine learning algorithms. Methods for generating datasets for Classification Methods for generating datasets for Regression Methods for Generating Datasets for Classification The following is the list of …
