I write columns on news related to bots, specially in the categories of Artificial Intelligence, bot startup, bot funding.I am also interested in recent developments in the fields of data science, machine learning and natural language processing ...
Full BioI write columns on news related to bots, specially in the categories of Artificial Intelligence, bot startup, bot funding.I am also interested in recent developments in the fields of data science, machine learning and natural language processing
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12 Useful Data Science Walkthroughs
- In this 3-part tutorial, you will learn how to scrape H-1B visa data with R. DataCamp instructor Ted Kwartler walks you through how to parse and store the JSON data, perform Exploratory Data Analysis, adding visuals, and finally create a map of the data thanks to a geocoding API. This walkthrough is valuable as it shows all the steps a data scientist would take to answer a question: Can Data Help Your H-1B Visa Application?
- Part 1: Web Scraping and Parsing Data
- Part 2: Adding visuals to your EDA
- Part 3: Geocode Location & Create a Map of the Data
- Characterizing Twitter followers with tidytext - Explore tidytext in this walkthrough by analyzing your Twitter followers descriptions to learn more about them.
- Python Machine Learning: Scikit-Learn Tutorial - This introductory post covers the basics of scikit-learn using digits data. The techniques covered here are Principal Component Analysis (PCA), Support Vector Machines (SVM), and K-Means algorithms.
- Scikit-Learn Tutorial: Baseball Analytics - This 2-part walkthrough uses baseball datasets to determine Major League Baseball (MLB) Teams wins per season based on team statistics, and which player will be voted into the Hall of Fame based on career statistics and awards. The techniques covered here are Linear Regression, K-Means, Logistic Regression, and Random Forest.
- Part 1: Predicting MLB Teams Wins per Season
- Part 2: Which Player will be Voted into the Hall of Fame
- Machine Learning in R For Beginners - This includes a walkthrough on multi-class classification with the well-known k-nearest neighbor algorithm with the help of the caret library. This short introduction to ML in R is a must for R learners and the data used here is the famous iris dataset.
- What I learned From Implementing A Classifier From Scratch - This is a great walkthrough to understand what is under the hood of Machine Learning. Without using a pre-existing library, build a classifier from scratch to better understand its inner workings.
- Forecasting Website Traffic Using Facebooks Prophet Library - Facebook open-sourced an R and Python library called prophet to automate the forecasting process. This walkthrough introduces this library and uses it to predict traffic volume for a website.
- Keras Tutorial: Deep Learning in Python - Build a Multi-Layer Perceptron (MLP) for classification and regression tasks using a wine data set.
- keras: Deep Learning in R - The Keras package was recently launched in R, be an early adopter! Here you will build a MLP for multi-class classification again using the iris dataset.
- TensorFlow Tutorial For Beginners (Python) - Work on Belgian traffic signs data with Googles very own TensorFlow, one of the more promising deep learning libraries.
