A code-first approach to understanding how to use large language models in practice.
Advancements in AI research with a comparison between GPT-4 and ChatGPT
Eight ways ChatGPT can make you more efficient
Key considerations for selecting the best prompts for image generation
Performing EDA on a time series dataset and preparing it for modeling
Performing EDA on a tabular dataset and preparing it for modeling
A blog post on how to deal with categorical variables while analyzing data
Explaining the concepts of vectors and matrices, and how they help in Data Science
Exploring the basics of the Pandas library
Creating a language model that will generate its own movie reviews using PyTorch and Fastai
Classifying movie reviews using sentiment analysis and ULMFit
Predicting the sale price of bulldozers based on the usage, equipment type, and configuration
Figuring out what breed of pet is shown in each image of a dataset using fastai
Building a model to try and identify whether images contain a dog or a cat using TensorFlow
Performing real-time lane detection on a driving video using OpenCV
Identifying whether an image of a hand is showing rock, paper or scissors using TensorFlow
Predicting text in context to a Harry Potter novel using TensorFlow
Creating a color detector that will detect color names and RGB values from any picture using OpenCV
Using SQL to examine data to assess its quality and modifying the data and tables to make analysis easier
A blog post that outlines PostgreSQL basics
Analyzing the quality of red and white wines, and checking which are the attributes that affect wine quality the most
Part 3 of a Python tutorial for beginners.
Part 2 of a Python tutorial for beginners.
Part 1 of a Python tutorial for beginners.
Imagine you are moving to London. We’ll use the London Crime data and the Foursquare API to select which neighborhood best fits our needs.
Exploring content-based and collaborative filtering recommendation systems
Using support vector machines (SVM) to build and train a model using human cell records, and classify cells so as to realize whether the samples are benign or malignant
Exploing neighborhoods in Toronto and grouping them into clusters
We will be looking at agglomerative hierarchical clustering
Creating a model for a telecommunications company using logistic regression to predict when its customers will leave for a competitor
Creating a map looking at the crime data in the city of San Francisco
Exploring neighborhoods in New York City and group them into clusters
Customer segmentation is the practice of partitioning a customer base into groups of individuals that have similar characteristics
An introduction to k-means clustering
Using the decision tree classification algorithm to build a model from the historical data of patients and their response to different medications, and predicting the class of an unknown patient
Building a classifier to predict the class of new or unknown customers for a telecommunications provider
Using scikit-learn to implement different types of linear regression on our dataset
Creating a choropleth map of the world depicting immigration from various countries to Canada
Analyzing China’s GDP growth from the year 1960 to 2019
