Here are the sections:
This section contains cheatsheets of basic concepts in data science that will be asked in interviews:
This section contains books that I have read about data science and machine learning:
This section contains sample questions that were asked in actual data science interviews:
This section contains case study questions that concern designing machine learning systems to solve practical problems.
This section contains portfolio of data science projects completed by me for academic, self learning, and hobby purposes.
For a more visually pleasant experience for browsing the portfolio, check out jameskle.com/data-portfolio
Transfer Rec: My ongoing research work that intersects deep learning and recommendation systems.
Movie Recommendation: Designed 4 different models that recommend items on the MovieLens dataset.
Tools: PyTorch, TensorBoard, Keras, Pandas, NumPy, SciPy, Matplotlib, Seaborn, Scikit-Learn, Surprise, Wordcloud
Trip Optimizer: Used XGBoost and evolutionary algorithms to optimize the travel time for taxi vehicles in New York City.
Instacart Market Basket Analysis: Tackled the Instacart Market Basket Analysis challenge to predict which products will be in a user's next order.
Tools: Pandas, NumPy, Matplotlib, XGBoost, Geopy, Scikit-Learn
Fashion Recommendation: Built a ResNet-based model that classifies and recommends fashion images in the DeepFashion database based on semantic similarity.
Fashion Classification: Developed 4 different Convolutional Neural Networks that classify images in the Fashion MNIST dataset.
Dog Breed Classification: Designed a Convolutional Neural Network that identifies dog breed.
Road Segmentation: Implemented a Fully-Convolutional Network for semantic segmentation task in the Kitty Road Dataset.
Tools: TensorFlow, Keras, Pandas, NumPy, Matplotlib, Scikit-Learn, TensorBoard
World Cup 2018 Team Analysis: Analysis and visualization of the FIFA 18 dataset to predict the best possible international squad lineups for 10 teams at the 2018 World Cup in Russia.
Spotify Artists Analysis: Analysis and visualization of musical styles from 50 different artists with a wide range of genres on Spotify.
Tools: Pandas, NumPy, Matplotlib, Rspotify, httr, dplyr, tidyr, radarchart, ggplot2
This section contains portfolio of data journalism articles completed by me for freelance clients and self-learning purposes.
For a more visually pleasant experience for browsing the portfolio, check out jameskle.com/data-journalism
These PDF cheatsheets come from BecomingHuman.AI.