Here are the sections:
- Data Science Cheatsheets
- Data Science EBooks
- Data Science Question Bank
- Data Science Case Studies
- Data Science Portfolio
- Data Journalism Portfolio
This section contains cheatsheets of basic concepts in data science that will be asked in interviews:
- SQL
- Statistics
- Mathematics
- Machine Learning Concepts
- Deep Learning Concepts
- Supervised Learning
- Unsupervised Learning
- Computer Vision
- Natural Language Processing
- Software Engineering
- Stanford Materials
This section contains books that I have read about data science and machine learning:
- Intro To Machine Learning with Python
- Machine Learning In Action
- Python Data Science Handbook
- Doing Data Science - Straight Talk From The Front Line
- Machine Learning For Finance
- Practical Statistics for Data Science
This section contains sample questions that were asked in actual data science interviews:
- Data Interview Qs
- Data Science Prep
- Interview Query
- Analytics Vidhya
- Springboard
- Elite Data Science
- Workera
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
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Transfer Rec: My ongoing research work to incorporate transfer learning into the design of recommendation systems.
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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
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Trip Optimizer: Used XGBoost and evolutionary algorithms to optimize the travel time for taxi vehicles in New York City.
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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
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Fashion Recommendation: Built a ResNet-based model that classifies and recommends fashion images in the DeepFashion database based on semantic similarity.
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Fashion Classification: Developed 4 different Convolutional Neural Networks that classify images in the Fashion MNIST dataset.
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Dog Breed Classification: Designed a Convolutional Neural Network that identifies dog breed.
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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
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- Classifying Tweets with Weights & Biases: Developed 3 different neural network models that classify tweets on a crowdsourced dataset in Figure Eight.
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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.
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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
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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
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The 8 Neural Network Architectures ML Researchers Need to Learn
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The 5 Deep Learning Frameworks Every Serious Machine Learner Should Be Familiar With
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The 5 Computer Vision Techniques That Will Change How You See The World
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Convolutional Neural Networks: The Biologically-Inspired Model
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Recurrent Neural Networks: The Powerhouse of Language Modeling
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The 7 NLP Techniques That Will Change How You Communicate in the Future
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The 3 Deep Learning Frameworks For End-to-End Speech Recognition That Power Your Devices
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The 5 Algorithms for Efficient Deep Learning Inference on Small Devices
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The 4 Research Techniques to Train Deep Neural Network Models More Efficiently
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The 2 Hardware Architectures for Efficient Training and Inference of Deep Nets