A-Jacobson / interview_prep

Collection of notebooks reviewing concepts in CS, ML, and Stats

Interview Preparation

Collection of notebooks reviewing concepts in CS, ML, and Stats for data science interviews

Data Structures & Algorithms

Resources:

• Stanford Algorithms Course - https://www.coursera.org/specializations/algorithms
• Problem Solving with Algorithms and Data Structures - https://interactivepython.org/runestone/static/pythonds/index.html
• Grokking Algorithms - https://drive.google.com/drive/u/0/folders/0B73KGlyDvGNtU1hwTnhZNnRjMjQ
• CLRS - https://drive.google.com/drive/u/0/folders/0B73KGlyDvGNtU1hwTnhZNnRjMjQ
• Cracking the Coding interview - https://drive.google.com/drive/u/0/folders/0B73KGlyDvGNtU1hwTnhZNnRjMjQ

Topics:

• Big O - https://github.com/A-Jacobson/interview_prep/blob/master/algorithms/BigO.ipynb
• Recursion -
• Linked Lists -
• Arrays -
• Hash Tables -
• Stacks -
• Queues -
• Trees -
• Graphs -
• Sorting:
• insertion sort - https://github.com/A-Jacobson/interview_prep/blob/master/algorithms/Insertion%20Sort.ipynb
• merge sort - https://github.com/A-Jacobson/interview_prep/blob/master/algorithms/Merge%20Sort.ipynb
• Breath First Search -
• Depth First Search -

Statistics

Resources

• Harvard Data Science - https://github.com/cs109
• Think Stats - https://drive.google.com/drive/u/0/folders/0B73KGlyDvGNtMHpTSS1pZkR6ZnM
• Think Bayes - https://drive.google.com/drive/u/0/folders/0B73KGlyDvGNtMHpTSS1pZkR6ZnM

Topics

• Bayes Rule -
• A/B testing -
• Summarization -

Machine Learning

Resources:

• Andrew Ng Coursera Course - https://www.coursera.org/learn/machine-learning
• Elements of Statistical Learning - http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf
• Mining Massive Datasets - https://drive.google.com/drive/u/0/folders/0B73KGlyDvGNtbDhRZWowOWRCSDA
• Python Machine Learning - https://drive.google.com/drive/u/0/folders/0B73KGlyDvGNtbDhRZWowOWRCSDA
• Pedro Domingos ML tips - https://drive.google.com/drive/u/0/folders/0B73KGlyDvGNtbDhRZWowOWRCSDA
• Andrew NG nuts and bolts of deep learning - https://www.youtube.com/watch?v=F1ka6a13S9I

Supervised Learning:

• KNN -
• Linear Regression -
• Logistic Regression -
• Trees -
• Naive Bayes -
• SVM -

Unsupervised Learning:

• PCA -
• TSNE -
• K-means -

Validation

• Precision vs Recall
• K-fold CV
• Learning Curves
• ROC curves

Representations

• Image feature representations
• Text feature representations
• Class Imbalances

Optimization

• Normal Equation
• SGD

Deep Learning

Resources:

• CS231n: Convolutional Neural Networks for Visual Recognition - http://cs231n.stanford.edu/
• CS244d: CS224d: Deep Learning for Natural Language Processing - http://cs224d.stanford.edu/
• Deep Learning Book - http://www.deeplearningbook.org/
• Keras Blog - https://blog.keras.io/
• WildML - http://www.wildml.com/

Topics:

• CNN -
• RNN -
• Word2Vec -
• Batch Norm -
• Dropout -

Other Topics:

• Databases
• AWS
• Spark
• SQL
• web scraping